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Rosanna Fornasiero · Tullio A. M. Tolio Editors

# The Future of Manufacturing: The Italian Roadmap

# **Springer Tracts in Mechanical Engineering**

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Rosanna Fornasiero · Tullio A. M. Tolio Editors

# The Future of Manufacturing: The Italian Roadmap

*Editors* Rosanna Fornasiero CNR-National Council of Reseach Padua, Italy

Tullio A. M. Tolio Department of Mechanical Engineering Politecnico di Milano Milan, Italy

ISSN 2195-9862 ISSN 2195-9870 (electronic) Springer Tracts in Mechanical Engineering ISBN 978-3-031-60559-8 ISBN 978-3-031-60560-4 (eBook) https://doi.org/10.1007/978-3-031-60560-4

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# **Foreword**

Manufacturing is the mainstay of many modern economies, capable of generating social, economic and environmental benefits, as well as helping overcome the great challenges of our times. The last two years have been particularly challenging for economies the world over, and Italy—with its wealth of flexible businesses, a diversified industrial culture and the ability to swiftly reconvert its processes—has managed to respond to the market-induced criticalities.

With its "Manufacturing a Resilient Country" document, the Cluster has already offered a proposal to face situations like the pandemic crisis, pointing out the need for Italy—as a country working together in a networked system—to adopt strategies that can leverage that solid industrial system to strengthen and develop processes, sectors and applications by creating collaborative ecosystems that pool regional specialization area excellences capable of working in national and international supply chains.

This roadmap is the result of intense work put in over two years, which has seen companies, universities, research bodies and associations come together to build a systemic vision for the themes of research and technological innovation with a medium- to long-term outlook. The aim of the document is to recommend paths for improving manufacturing's positioning in the international arena, facing challenges head-on and opening up new strategic opportunities to strengthen Italy's specific industrial leadership across the globe. The roadmap follows seamlessly from Horizon Europe's European research policies, developed in line with these policies and with what has been defined in the regional Smart Specialization strategies.

It is based on a collaborative approach whereby top-down analysis of the global development trends and scenarios generating the challenges for manufacturing are integrated with a bottom-up approach that engages the Cluster's members in bringing forward their research needs for the coming years.

The roadmap is structured along seven strategic action lines for which specific priorities for research and innovation have been identified, designed to seize and develop the opportunities offered by emerging and potential enabling technologies (which have also been identified with the aid of Pathfinders) in relation to the challenges companies are fielding from the market (which have also been identified with the aid of Lighthouse plants).

This work was started under the previous presidency of the Cluster, Luca Manuelli, and has been completed under my mandate and represents a coherent and harmonized vision of the Cluster of Intelligent Factories over the years.

In presenting this document, first of all I would like to thank my closest collaborators of the previous and current management boards for their valuable support on the strategic development of this roadmap: the president of the cluster, Gianluigi Viscardi, Tullio A. M. Tolio, Antonio Braia, Ivan Boesso, Leda Bologni, Paolo Calefati, Mauro Castello, Paolo Dondo, Sauro Longhi, Alberto Longobardi, Luca Manuelli, Alessandro Marini, Maria Rosa Raimondo, Mario Ricco, Daniela Sani, Giuseppe Saragò, Marco Taisch, Flavio Tonelli, Lorenzo Molinari Tosatti, Daniela Vinci and Andrea Volpi.

A particular thanks to the previous and current members of the Scientific Board of the Cluster that gave the scientific direction to the development of the roadmap. Tullio A. M. Tolio (President), Paolo Calefati, Sauro Longhi, Alberto Longobardi, Marco Taisch, Flavio Tonelli and Gianluigi Viscardi.

This work would not have been possible without the continuous and strong commitment of the roadmap editors, Tullio A. M. Tolio and Rosanna Fornasiero, who coordinated the work of all the cluster groups, assured the consistency of the whole document and supported the definition of the coherent and complete vision of the content generated along the process of roadmap development.

My thanks go to Cluster Manager Paolo Vercesi who organized the work around the creation of the roadmap, organizing webinars, managing surveys and supporting actively discussions and various technical meetings.

This roadmap is the result of a collaborative approach involving all the members of the Cluster of Intelligent Factories who have provided their competence, ideas and vision on the future of Italian manufacturing through an interactive process. Companies, Universities, Research Bodies and Associations have lent their expertise in various capacities, called on by the Cluster to take an active part in this process.

In particular, I would like to thank the following groups that have dedicated continuous effort in the development of the roadmap.

The Roadmapping group of the Cluster: Rosanna Fornasiero (Coordinator), Marcello Colledani, Guido Colombo, Melissa Demartini, Paolo Dondo, Luca Giorleo, Cristian Secchi, Flavio Tonelli and Marcello Urgo.

The members of the Steering Committees of the technical and scientific group (GTTS) and their delegates:


#### Foreword vii


The representatives of the Lighthouse plants:


The representatives of the Pathfinder companies:


The representatives of the organizations indicated by Regional Councils:


This work would not have been possible without the economic support of the following organizations:


I think this roadmap is an essential tool for supporting members in determining their paths going forward and, at the same time, to inspire specific policies and actions for research and innovation as well as internationalization at the various stakeholder levels, including at a government level. It can be used to bring institutions—above all, Ministries in particular MUR, MIMIT, MAECI, MASE and MLPS—into the discussion, representing the visions brought into focus by the members of the Italian Cluster of Intelligent Factories. At the European level, it is a qualified point of view for possible interactions with the Manufuture platform, existing partnerships like Made in Europe, Clean Steel and Processes4Planet, the Chips Joint Undertaking and the new partnerships in the area of advanced materials, the KIC Manufacturing and all the other initiatives related to manufacturing. Lastly, this document can further be used for actions to support cross-fertilization between national and regional policies and to support bilateral discussions with other countries.

Bergamo, Italy Gianluigi Viscardi President of the Cluster of Intelligent Factories

# **Preface**

Manufacturing is the mainstay of many modern economies, capable of generating social, economic and environmental benefits, as well as helping overcome the great challenges of our times. From a broader point of view, the availability of advanced expertise, industrial culture, image, brands and reputation, availability of resources for innovation and research, and the right conditions to attract talent are all elements that can seal a country's success.

Over the last three years, it has become even more apparent that—based on the characteristics and availability of resources (such as skills, manufacturing plants and raw materials)—each country needs to develop a strategy to ensure a strong industrial sector, focusing on processes, sectors and applications that embody the uniqueness of the region's characteristics, with a view to achieving excellence in strategic areas of specialization.

Italy, more than most, has a unique heritage in terms of tradition, culture, skills, image, design and technologies, which represent the optimal environment for a manufacturing sector that produces high-added-value products and services exported worldwide.

The only conceivable engine for driving continuous evolution in a country is a research and innovation plan accompanied by a training plan designed to refocus the set of skills within the national industrial fabric in line with European policy objectives. A multi-year research plan must leverage the qualities of Italy's available production resources and must be aligned with research challenges and international trends in the manufacturing field.

The pandemic has put all companies and economies through the wringer, and no analysis of historical data is complete without also looking at current economic data, which nonetheless makes coming up with any forecast for the future much more difficult and calls for great caution in a context that is still very much evolving and marked by a great deal of uncertainty.

On the one hand, it is necessary to avoid the risk of being influenced by the latest trends that the shifting current scenario can easily overturn; on the other, it is necessary to define pathways that take into account challenges and the opportunities they bring for an overhaul of Italian manufacturing.

The Cluster's strategy is based on the fact that the development and application of scientific research outcomes is recognized as one of the most effective levers for improving competitiveness and creating products and processes that are more efficient and sustainable and, more generally speaking, better able to meet people's needs.

In addition, this has a considerable impact on society as it can help improve the quality of life of its citizens and the competitiveness of the system as a whole, tackling social challenges, such as sustainability, product customization and development of human resources.

A process of this kind is complex and involves various components and different roles, taking into consideration different points of view, interests and needs. Over the last 15 years, models capable of supporting an innovation process of this kind have been discussed at length and analysed at a scientific, industrial and political level with the goal of finding more effective ones. Today, one of the most widely adopted models of innovation is the so-called triple helix model.

According to this model, the growth of a country, capable of considering the needs and characteristics of the society and industrial system, can be achieved through proactive collaboration between research, business and government. On one side, the objective of the research activities is the development of innovation that can be applied to different contexts. On the other side, it is the task of businesses to ensure they are profitable, competitive and offer value for money.

Institutions must provide a regulatory framework supporting effective collaboration, assisting them during the initial phase from research to innovation through to actual industrialization, as they often prove unfeasible where they rely on market forces alone. Moreover, a virtuous system should be based on social and economic improvement, which researchers and companies should factor into the technological development models, possibly also backed by government bodies.

In this context, there is no denying the paramount importance of the Cluster's role: it becomes a facilitator of research and innovation networking processes, acting as a soft-governance body to bring together the needs of all these actors through processes designed to help define appropriate policies to support and stimulate research and innovation, and their implementation, with the aid of strategic documents such as the roadmap.

Therefore, with its ultimate goal of defining the new roadmap for research and innovation for the Italian manufacturing industry, this book groups the work of more than 200 people involved with different sessions of brainstorming, focus groups, expert elicitation and content analysis.

The first chapter "Defining a Collaborative Framework for Roadmapping Activities" proposes a collaborative framework and methodology that can be used for supporting roadmapping activities involving large groups of actors representing different interests.

The second chapter "Analysis of the Italian Manufacturing Sector" proposes an insight into the context of the Italian manufacturing sector, comparing it with other countries in Europe and across the globe, also offering a focused look at the sector's response to the pandemic crisis, and with a focus on the machine tools sector and on system competitiveness.

This is followed by the chapter "The Role of Industrial Policies: A Comparative Analysis" with the analysis of the reference documents that are orienting industrial policy at the European, national and regional levels to study how these decisional levels can interact in terms of content and synergies of objectives.

The following chapter ("Building Scenarios for the Future of Manufacturing"), referring to a number of important environmental, social and technological trends, offers a number of reference scenarios that are emerging for having a significant impact on the manufacturing sector in terms of changes in production models along the time horizon from short to long term and that can be used to identify the strategic lines.

Chapters "Strategic Action Line LI1: Personalised Production"–"Strategic Action Line LI7: Digital Platforms, Modelling, AI, Cybersecurity" expand on the content in terms of strategic action lines each covering a specific macro-area and identification of related research and innovation priorities.

Padua, Italy Milan, Italy

Rosanna Fornasiero Tullio A. M. Tolio

# **Contents**



Maria Cristina Vistoli, Angelo Messina, and Francesco Zanichelli

# **Defining a Collaborative Framework for Roadmapping Activities**

**Rosanna Fornasiero and Tullio A. M. Tolio**

**Abstract** Today, more than ever, it urges to increase effort for monitoring and investigating changes in environment, particularly in relation to events in the social, economic, political and ecological landscapes as well as new technologies. Roadmapping activities are based on techniques and practices to analyse the "state" of a system and to identify evolution of emerging drivers. Roadmapping methodologies can support in understanding the impact of drivers on the competitive position of system under consideration and on the advantage of answering to these drivers. This chapter proposes a collaborative framework, designing it with the aim to involve several stakeholders with an iterative approach to consult and validate the results collected from literature and state-of-the-art and to share knowledge in a context where system competitiveness is considered as a precondition for individual benefit. Overall, this work contributes to improve the effectiveness of strategic roadmapping and to increase its value added to the planning process of clusters and in general of large groups of interest, while providing helpful insight to public organizations that promote the competitiveness of related sector under consideration.

**Keywords** Roadmapping · Collaboration · Manufacturing · Systemic approach

# **1 Introduction**

Literature recognises the relevance of technological roadmapping activities as fundamental elements supporting strategic decisions for R&D policy definition both at private and public level. In particular, the aim of roadmapping is to identify development paths to help a system to acquire competitive advantage facing exogenous factors while implementing reactive and pro-active actions. Trends and weak signals

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_1

R. Fornasiero (B) CNR-IEIIT, Padova, Italy

e-mail: rosanna.fornasiero@cnr.it

T. A. M. Tolio Politecnico Di Milano, Milano, Italy

detection become of strategic importance in a context of turbulent environment and increasing complexity. Technological changes like acceleration in digital transformation, production systems innovation, social changes like increase in aging people, evolution of consumer tastes, etc., and political changes like protectionism, trade barriers etc. are only some of the possible factors to be taken into consideration when starting a roadmapping activity.

The roadmapping activities represent a process to be implemented both by single company willing to re-position itself in the market as well as by heterogenous group (i.e. associations, clusters, production networks) willing to understand environment and to position in a macro-scenario where several factors need to be taken into consideration. In this last case (heterogeneous group of interest), it is necessary to have a wide and comprehensive approach that involve many different actors and stakeholders each bringing to the discussion specific interests to balance appropriately these contributions. The perspective is different from the single company and the aim is to define strategies to be taken for the overall group and to bring to the attention of the policy makers a strategic view which is necessary to be shared.

The creation of the roadmap is a structured process based on visions of the future addressing multiple influencing factors and their inter-relationships. From the methodological point of view, there are several papers proposing review of methods and tools to be applied in the fields of future studies, foresight, forecasting, strategy for the future, etc. There is no standard classification of these methods, but when designing a roadmap framework, it is important to consider trade-offs in roadmapping like: top-down versus bottom-up, explorative versus normative, quantitative versus qualitative, and expert-based versus assumption-based.

A multi-stage approach should assure the coverage of different dimensions through quantitative and qualitative methodologies to integrate and cover the following aspects (Popping 2008):


Therefore, in the last years, the Cluster of Intelligent Factories has defined and tested a collaborative framework where methodologies like online consultation, scenario definition, expert panels, literature review and others have been set in an integrated way to support the development of a roadmap applied to an heterogenous group of interest. This pool of methodologies was used as a strategic guideline for the medium-long term roadmapping not only at National but also at regional level.

The path followed by the Cluster to define the roadmap is based on honing a framework that—by integrating different approaches that have also been acknowledged in scientific literature—has allowed to gather and formalize the opinions of the various actors within the actual Cluster or operating within its orbit.

An analysis of the current landscape has been a necessary preparatory phase providing a snapshot of the Italian and global situation and an insight into how manufacturing is reacting to shifting market conditions, in an attempt to extrapolate the industry's strengths and weaknesses. In terms of empirical data, a number of databases, such as the World Bank, OECD, Eurostat, ISTAT, have been used to support the analysis of the general situation, and data from Italian manufacturers associations Federmacchine and UCIMU have been used for a more focused look at the machine tools sector.

In terms of policies, the roadmap's reference points are European documents (such as Next Generation EU, Manufuture's SRIA, and programming documents from the Made in Europe partnership), Italy's national PNR and PNRR documents, and the Regional Specialization Strategies.

This phase also drew on trend analysis documents and grey literature such as consultancy reports (e.g. Roland Berger, McKinsey, Deloitte, PwC and EY), and reports from associations (e.g. Confindustria think tank-CSC, Edison Foundation, trade associations) to understand what the social, economic, environmental and political trajectories look like, and define reference scenarios that include a number of major radical changes in the landscape that are inextricably linked to manufacturing.

The definition of scenarios is a starting point for understanding what the society and global economic structure change factors are and how they affect the industry. They also highlight that the combined effects of certain trends—such as an aging population, the shortage of resources and their overexploitation, climate change, technological acceleration, and opening up of new markets—create unprecedented challenges that call for a transformation of the Italian manufacturing system.

In light of these scenarios, the technological roadmapping activities conducted by the CFI Cluster to identify the priorities and technology trajectories of the area of specialization have been based on findings that have emerged from interaction over the last two years with all the actors within the Cluster's own ranks.

# **2 The Collaborative Framework**

The management structure of the Cluster of intelligent Factories is based on the collaboration of several bodies that have been involved in the various phases of the roadmap's definition (Fig. 1) which are:

**Fig. 1** Organisation structure of the cluster of intelligent factories


More specifically, a significant role in drawing up the roadmap's strategic action lines was played by the 7 Steering Committees of the GTTS, which have guided the specific theme definition process. In particular, each Steering Committee is composed of 7–10 members appointed by their respective institutions to represent them based on a selection process involving stakeholders like industry (through the industry confederations Confindustria and Confartigianato), research bodies (through Deans and directors of Universities and Research organisations) and regional bodies (through regional associations and clusters).

The collaborative framework is based on several iterative steps like (Fig. 2):


The Table 1 below illustrates the most important steps of the collaborative framework taken over this two-year period (2020–2022), which have involved the actors listed.

**Fig. 2** Steps of the collaborative approach for the roadmap definition

# **3 Roadmap Structure for the Cluster of Intelligent Factories**

The roadmap of the Intelligent Factories is set upon 7 strategic action lines that represent the paths along which Italian manufacturing's research and innovation priorities are developed. The objective of the strategic action lines is to respond to specific challenges generated by the market and by the acceleration of technological development.

More specifically, they represent trajectories against which companies can measure their own progress and develop research and innovation pathways, also taking into account the context scenarios, such as new consumption models, circular economy, electric mobility, knowledge management, digital platforms and climate change.

Each strategic action line comprises research and innovation priorities (PRI) within which research and development goals are defined that can help with the planning of short-, medium- and long-term actions both at company and at supply chain level and, above all, at the coordinated country level.

A number of strategic action lines has been identified that have been informed by the market and by the need to research and develop new models, methods and


**Table 1** Steps and actors involved along the collaborative approach

(continued)


**Table 1** (continued)

technologies to meet the society challenges that companies find themselves facing (Fig. 3). The 4 action Lines are as follows:


**Fig. 3** Intersection of market oriented strategic action lines

**Fig. 4** Intersections of the technology push strategic action lines

The other 3 strategic actions lines have been identified as stemming from the need to research and develop new technologies that support the manufacturing sector at different levels (Fig. 4), namely:


Below is a brief summary of the objectives of each strategic action line and the relevant research and innovation priorities.

**LI1-Personalized production**: the objective of this action line is to propose research and innovation priorities aimed at promoting industrial systems and models for the efficient manufacture of customized products that can be reconfigured with fast turnarounds to meet specific requests fielded from individual customers or small groups, and that deliver a high level of integration with the customers in order to ensure they become the main actors of the resulting solution. These design and production systems must be conceived to have the capacity to be reconfigured for the manufacture of products that can be required in certain times of emergency (such as health emergencies) or in response to events that can cause a sudden shift in system priorities and require the industrial system to transfer its focus to different categories of products to those usually made. In this action line, it will be important to research new supply chain management models and local manufacturing models as well as smart materials.

**LI2-Industrial sustainability**: the objective of this action line is to propose research and innovation priorities aimed at transforming the industrial processes involved in the design and manufacture of new products of the future in line with circular economy principles, in order to significantly reduce carbon emissions and improve energy efficiency, reduce and rationalize consumption of resources, facilitate and promote their recovery and recycling. In addition to recovering and recycling materials, it is important to orient future production models towards product repurposing and the recovery and recycling of raw materials. These actions must be aimed at preserving the value of activities involved in transforming raw materials into products. These changes require the introduction of new processes, new machinery and new systems, resulting in a thorough overhaul of the national manufacturing base, opening up new capital goods markets that will see Italy claim a leadership position.

**LI3-Enhancing human resources**: the objective of this action line is to propose research and innovation priorities aimed at designing and developing new solutions to enhance the role of human resources and their skills, and contribute to their satisfaction and wellbeing; research and experimentation of new technologies for reducing physical exertion, cooperation with advanced support systems, with collaborative robots and with AI-powered technologies; mapping of knowledge generated on the job, especially implicit knowledge, in a way that is compatible with privacy requirements, introducing advantages both on the human wellbeing front—whether the individuals are users, operators or managers—and in terms of business strategies and procedures. In this regard, innovative factories will need to be increasingly inclusive, strongly geared towards the engagement and participation of individuals (users, operators and managers). These models must take a human-centric approach to look into/ investigate new technologies and all the dimensions through which the new factory is defined.

**LI4-High efficiency and zero-defect**: the objective of this action line is to propose research and innovation priorities aimed at researching models for efficiency in terms of: zero-defect technologies designed to reduce non-conformances, monitoring of processes during the various phases, quality management, maintenance and internal logistics of a manufacturing system, upgrading and improving the capacity of equipment and industrial goods; robustness/flexibility as the capacity to face disruptions, due to the precarious supply of incoming materials and parts, and to the specific properties of the material (anisotropy, low rigidity, etc.); smart systems for optimized use of available resources (equipment, human operator, knowledge) and for the control and management of production systems through models (CPS, empirical models, etc.).

**LI5: Innovative production processes**: the objective of this action line is to propose research and innovation priorities across various aspects of production processes, such as: digitization of conventional production processes in order to improve their interactions and handle different types of processing, even by means of hybrid processes; the growing role of additive manufacturing and its ensuing challenges in terms of both design and production; processing of standard and innovative materials, or materials with meso/macro geometries, including also nano- and micro-manufacturing. In addition, process innovation also needs to take the shape of innovation in support of re- and de-manufacturing processes, to start with, through to the development of bio-inspired transformation models.

**LI6-Evolving and resilient production**: the objective of this action line is to propose research and innovation priorities aimed at researching and developing evolving and resilient production systems by exploiting a high degree of machine automation and self-learning, with levels of autonomy and adaptive intelligence designed to facilitate the operators' job. The priority research topics concern: modelling and simulation for the design and management of production systems, and hardware and software technologies for production system reconfigurability. The technology enablers are linked to the availability of smart modular devices that can be integrated wireless in a transparent, autonomous way, capable of monitoring and controlling manufacturing assets and products, and supporting decisionmaking, ensuring ready access to all necessary operational, configuration, fault and maintenance data.

**LI7-Digital platforms, modelling, AI, security**: the objective of this action line is to propose research and innovation priorities aimed at researching and developing innovative digital architectures for the monitoring, control and management of manufacturing activities and related assets, modelling new products/services and production processes, use of Al, Big Data and adequate Cybersecurity systems. More specifically, the LI7 line research and innovation priorities assume that criteria need to be defined for the management and transformation of raw production data into strategic information for decision makers, identifying the information to be collected from each digital access point by means of suitable enabling technologies and then delivered as appropriate. Digital platforms and cybersecurity also play a significant role in the definition of dynamic supply chain models.

It is expected that the impact derived from the implementation of the roadmap's contents embraces a number of different aspects, such as:


and innovation paths will also be promoted: starting with fundamental research, they will progress through research and innovation to lead to the development of knowledge in industrial fields.

• The development of specific local skills through the integration of actions at a national and regional level.

# **References**


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Analysis of the Italian Manufacturing Sector**

**Rosanna Fornasiero and Tullio A. M. Tolio**

**Abstract** When creating a roadmap, it is important to contextualise the sector under consideration. The work in this chapter is based on identification of relevant indicators which are analysed with a comparative approach both along the time horizon and with other countries and sectors. For this reason, this chapter is based on the extraction of data from International, European, national and regional dataset and describes the Italian manufacturing industry, exploring which are the most relevant sectors, which is the position comparing with European and international countries, and a focus is made on the machine tools sector. The system competitiveness is also analysed in terms of capability to bring innovation to Italy and to the sustainable development goals. The chapter closes with an analysis of the reaction of manufacturing to disruptions like the pandemic crisis and a proposal for a systemic recovery.

**Keywords** Manufacturing · Machinery · Statistical data · Innovation strategies

# **1 Industry Data**

In Italy, the manufacturing sector achieved a turnover just short of e1,000 billion in 2019, employing 3.8 million people, with a value added of over e250 billion. The manufacturing sector took out the top spot at the European level, too, as it is clear from the NACE EU-27 non-financial business economy rankings in terms of value added and number of people employed, while it came second for turnover.

In 2019, the sector employed 23% of all Europe's workforce, and generated 29% of its value added. Overall, more than 30 million people worked in 2 million manufacturing companies, generating a turnover in the region of e7,800 billion and a value added of almost e2 billion (Table 1 and Fig. 1).

e-mail: rosanna.fornasiero@cnr.it

T. A. M. Tolio Politecnico Di Milano, Milano, Italy

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_2

R. Fornasiero (B) CNR-IEIIT, Padova, Italy


**Table 1** Non-financial business economy in Europe, 2019 (*Source* Data from Eurostat)

**Fig. 1** Value added in EU non-financial sectors, 2019 (*Source* Data from Eurostat)

The manufacturing value added of the top 4 European countries accounts for 60% of the total value of European manufacturing, while the value added of the top 10 accounts for 87%. Over the last 10 years, the manufacturing value added of the top 10 European countries has increased to the extent that it now tops 1,800 billion in total, while Italy continues to play a foremost role on the European stage, placing second after Germany for value added (Figs. 2 and 3).

In Italy, within the manufacturing industry, there are a number of sectors that have demonstrated particularly impressive performance in terms of value added and turnover (Fig. 4). Most notably, the sectors concerned with manufacturing machine

**Fig. 2** Manufacturing added value (eM) of top EU countries, 2009.2019 (*Source* Data from Eurostat)

**Fig. 3** Manufacturing size of the top 10 EU countries (dimension of the bubble is value added), 2019 (*Source* Data from Eurostat)

**Fig. 4** Italian manufacturing sectors, 2019 (*Source* Data from Eurostat)

tools, fabricated metal products, food products and the fashion industry account for 46% of value added, 42% of turnover, and 41% of exports. These sectors, together with the furniture and timber industry, traditionally represent the "made in Italy" brand, consistently earning our country a place amongst the top European countries in terms of turnover and value added. Over the years, mature economies have lost their leadership position in terms of global share of manufacturing, but now it is possible to see the emergence of a new map of global manufacturing hubs after two years of changes brought about by the pandemic and the fallout from the Ukraine crisis.

The growth of manufacturing is no longer driven by foreign demand alone, it is also driven by the increase in domestic consumption stimulated by the potential of digitization and by the need to develop manufacturing and distribution models where geographical distance might still play a significant role.

High demand for new products has led to the evolution and redefinition of manufacturing models, with shorter planning horizons and life cycles, reduced lot sizes, hence entailing optimized use of resources in terms of streamlining, flexibility, agility and reconfigurability of the production process. The intensive use of the planet's resources, new production sites in emerging countries, and shortening of product life cycles have made environmental issues increasingly pressing, and the need to implement the circular economy is bringing forth important business opportunities at the global level.

Lastly, new standards impact heavily on production processes, while the lack of homogeneity between different countries results in unbalanced advantages, further compounded by the difficulty in protecting intellectual property rights in the global context.

The shocks to the system caused by COVID-19 and the nearby war in Ukraine, and the push towards product and process sustainability, call for the country to adopt a new approach to resilience in order to mount an adequate response to the critical and unexpected events occurring all along the supply chain, which has proven remarkably fragile.

# **2 Global Positioning**

# *2.1 Ranking of Global Manufacturers*

The advent of the pandemic crisis resulted in a drop in manufacturing worldwide in the first six months of 2020, which was followed by a recovery over the rest of the year. Essentially, the positions of the various countries being compared in terms of their % share of global manufacturing value added (calculated at current prices) reveal a fundamentally stable situation, with the Chinese sector accounting for 30% of global value added, and Italy still falling within the top 7 countries for manufacturing with 2.2% (Table 2).


**Table 2** Global ranking per manufacturing sector dimension (Value added in current\$) (*Source* Data from CSC)


However, there has been the odd exception, for example China's market share was up almost two percentage points, going from 28.6% of total value added in 2019 to 30.1% in 2020, further widening the gap with the United States (16.6%). South Korea and Taiwan—whose manufacturing systems have developed a strong specialization in electronics-related manufacturing—managed to climb up the rankings last year (by one and two places respectively). Italy claims its place as the world's seventh-ranked manufacturer, with a 2.2% share—consistent with its 2019 performance—followed by France (1.9%) and Great Britain (1.7%).

Analysing countries from the standpoint of the evolution of global manufacturing export and import shares in 2019, the world's top 20 exporting countries have continued to account for 80% of global exports for 20 years, and almost half of them (47%) are from Asia. Italy sits in ninth place with a 3.4% share of world trade and with a consistently positive trade balance (Table 3).

Notably, in terms of exports by the Italian manufacturing sector (Fig. 5), the machinery makes up the biggest exported product category out of the various manufacturing subcategories, totalling 82 billion euros for 2019, and still managed to retain the same 14.5% share of Italy's total exported value in 2021 despite the serious falling-off in the wake of the pandemic. When it comes to sectors driving development, a comparison of contributions by the various industry segments to the change in manufacturing value added of the different countries reveals the clear dominance of two sectors that have acted as an engine driving global industrial development.

Both constitute an important component of the current paradigm shift to the new digital economy: on the one hand, the manufacture of machinery and equipment, which incorporate enabling technologies for industry 4.0; and on the other, the manufacture of electronic components and hi-tech goods, thanks to the widespread application of technologies such as advanced sensor equipment, Internet of Things (loT), AI and big data. The contributions of each sector to the percentage growth of the manufacturing value added of China, the United States, Japan, Germany and

**Table 3** Manufacturing value added of top 10 countries (current \$) (*Source* Data from UNDATA)

**Fig. 5** Manufacturing exports by product type, 2019 (e) (Source data from Eurostat)

South Korea highlight that both sectors appear in the top four on the list of sectors driving industrial development.

As pointed out at length in the Confindustria study on Italian industry (CSC, 2019), the strong push towards digitization of industrial processes has had significant impact on national manufacturing of capital goods (the sector making the biggest contribution to growth over the last two years) and on related industrial machinery installation and repair activities. However, at the same time, it does not appear to have stimulated the electronics segment, which continues to account for only a marginal portion of Italy's total manufacturing value added, with a virtually unchanged share over the past 20 years or so of roughly 3.5% (in nominal terms), with a high trade deficit.

# **3 Focus on Machine Tools and Capital Goods**

Following robust growth in recent years—during which the machine tool sector strengthened its position in the global market—in 2019, Italian manufacturing of machine tools, robots and automation came in at 6,510 million euros, down 3.9% on 2018.

Consumption has dropped, by 6%, to 4,855 million as a result of the fall-off in deliveries in the domestic market (−6.5%, 2,911 million). In terms of global manufacturing, the manufacture of machine tools fell to below 59 billion euros in 2020, with Asia claiming the top spot accounting for 55% of global manufacturing, followed by Europe on 35%. In the manufacturer ranking, China is followed by Germany and Japan, with Italy in fourth place (Fig. 6).

Exports have also plummeted, falling victim to restrictions on the movement of goods and people, with Germany claiming the title of top exporting country, and Italy again sitting in fourth place with 2,625 million euros. According to the UCIMU report compiled from Italian statistics (Istat), in the June–July 2020 period, Italy's main export destinations were: the United States (152 million euros −18.2%), Germany (113 million euros −39%), China (105 million euros −36.4%), France (73 million euros −39%), Spain (48.6 million euros, −28.4%).

In terms of capital machinery and equipment in general, represented by 12 sectors making up Federmacchine—Italy's national federation of associations of manufacturers of capital goods—the total turnover of the 5 thousand companies just topped 41 billion euros in 2020, corresponding to 2.5% of GDP (Fig. 7). The most significant contribution to the Italian economy by the sector comes from foreign sales: with 27.8 billion euros, machinery sales abroad account for 5.7% of all Italian exports, a figure that climbs to 6.4% including goods export. Employment in the capital goods sector, in 2020, accounted for 4.3% of employment in the Italian manufacturing industry.

A distinctive trait of the Italian capital goods industry is a strong focus on exports that, in 2020, accounted for 67.1% of turnover, with a consistently positive trade balance with European Union counting for 29% of the total. Other primary outlets for Italian machinery are Asia (10.3%), North America (10.3%) and Eastern Europe (9%). South America, Africa and the Middle East account for smaller shares.

The Italian capital goods sector consistently rates highly in world rankings, placing amongst the country's top industrial sectors for turnover, exports and value added. This attests to Italy's specialization and strength in the capital goods sector, in a European context marked by German dominance and the marginalization of other countries (Table 4).

# **4 System Competitiveness**

Research and development (R&D) is a strategic variable in economic competitiveness of a country as it allows high levels of knowledge content to be incorporated into the production of goods and services, with positive effects on overall economic results.

There are many indicators that can be used to assess a country's capacity for innovation. Information on intra-muros R&D is the main component of statistical indicators on R&D used in the European arena to assess policies in support of research and improvement of a country's capacity for innovation and competitiveness.

Even the UN's Sustainable Development indicators include a number of R&Drelated indicators. On the global stage, only the United States, Japan and Korea outperform Europe in terms of improvement in their innovation indicators between 2014 and 2021.

**Fig. 6** Manufacturing and export shares in 2020 (mln euro) (*Source* Data from UCIMU)

**Fig. 7** Capital goods in Italy, 2020 (*Source* Data from UCIMU)


**Table 4** Machinery and equipment sector in Europe, 2019 (*Source* Data from CSC and Eurostat)

The 2021 European Innovation Scoreboard has updated the indicators, which now include a number relating to digitization and environmental sustainability to bring it into line with the EU's political priorities. Italy has a performance score that is higher than the European average for innovation-related indicators, as well as certain environmental sustainability indicators. Italy is one of the five countries that have seen a 25%-plus improvement in performance since 2014, while still falling into the "moderate innovators" category, albeit closing the gap with the European average.

The ISTAT census data offer an up-to-date picture of the level of evolution in innovative strategies pursued by Italian manufacturing companies, which allows us to assess whether, and to what degree, the intangible asset investment lever (in its various components) is actually being used within the national manufacturing system and, above all, how it is paired with tangible investments in a logic of complementarity. More specifically, in terms of research and innovation activities carried out over the 2016–2018 three-year period by Italian manufacturing companies with at least 10 employees, the following levers were considered: (i) R&D carried out in-house or outsourced; (ii) procurement of licences, software and databases; (iii) personnel training; (iv) procurement of machinery, equipment and hardware.

The first three items capture the importance of investments in intangible assets, the fourth in tangible assets. The information collected by companies concerns whether or not each of these activities is present in innovation projects, without considering the amount of financial resources channelled into each (Table 5).

The first finding to emerge from the analysis of the data is that two thirds of the 69 thousand companies included in the census state they invested in at least one of the four aforementioned activities, with 36% of innovators in Italian manufacturing actually pulling just one of the four innovative investment levers in question, and an additional 33% pulling just two of them. Hence, the most complex forms of innovative strategy are a prerogative of just a minority of companies (9%).

As Table 6 reveals, investments in tangible goods account for 71% of the innovation levers most widely used by Italian manufacturing companies, although the incidence of innovating companies engaged in R&D activities (59%) and in the purchase of digital goods (46%) is also high. On the other hand, on average, just 29% of companies have personnel training in place for innovative projects. Increasing levels of complexity in innovative strategies are associated with a greater ability to bring about the dual digital and green transition.

On this note, out of all the innovating companies, about one third has invested in industry 4.0 digital technologies (loT, advanced robotics, big data analysis, additive manufacturing, virtual and augmented reality), and within this percentage there is a considerable jump from the 20% of innovators who have pulled just one investment lever to the 58.3% of those that have pulled all four of the levers analysed.


**Table 5** Manufacturing companies per investment levers, 2021 (*Source* Data from ISTAT and CSC)


**Table 6** Complexity of innovation strategies, 2021 (*Source* Data from ISTAT and CSC)

Moreover, 82% of the innovating companies were engaged in reducing the impact of their activities on the environment, with peaks of almost 90% within the group of innovators pursuing the most complex strategies.

Companies that manage to simultaneously implement a number of actions in innovative strategies register an improved turnover growth performance, too, and the percentage of those that have experienced an uptrend in revenue for the 2020 June– September quarter was higher than that of non-innovating companies; while it was once again the highest within the group of innovating companies who have invested with more complex strategies (namely, pulling all four of the levers in question at the same time).

Hence, the analysis would suggest that the greatest return on innovative investments is achieved by pairing tangible assets—on which Italian manufacturing companies have focused most efforts to date—with intangible ones. There are several reasons why these strategies are not yet particularly widely practiced by companies, and concern both the different reference landscape in which they operate (quality of the ecosystem for innovation, financial constraints on investments, market structure) and their different ability to profitably handle the complexity associated with innovation, which in turn depends on the quality of technical and business knowledge amassed within the organization.

Generally speaking, the Italian R&D system is characterized by a series of limits that affect the management of relevant policies like fragmentation of actions, with numerous initiatives at both the national and regional level; delays in implementing measures and high variability in terms of availability and size of budget.

One of the goals to be achieved with the upcoming programming is to reduce regional disparities and speed up growth in Southern Italy, which is still suffering from marked inequality in terms of development, for example, of technological activities, income and infrastructures.

# **5 The Role of Manufacturing in Implementing Sustainable Development Goals**

In 2015, the United Nations adopted the development agenda titled: "Transforming our world: the 2030 Agenda for Sustainable Development" (UN, 2015). The 17 Sustainable Development Goals (SDGs) making up the agenda refer to different areas of social, economic and environmental development, and industrial processes also play a part in promoting this development in a sustainable way.

The list of SDGs features numerous references to the wellbeing of people and equitable sharing of benefits arising from development, clear references to the use of resources and to the environmental impact of activities. For each SDG, specific goals have been defined that are to be reached over the course of the years and are monitored by means of almost 250 system indicators.

More specifically, when it comes to goal 9 "Industry, innovation and infrastructure", an analysis of the indicators in this area reveals that, in 2020, the pandemic containment measures resulted in a reduction in the manufacturing industry's percapita value added, while industry's contribution to the economy as a whole in terms of value added and employment remained unchanged.

As Fig. 8 below shows, the contribution of companies to indicators related to R&D spending is better than 10 years ago on all fronts, while it is up from the year before in terms of research intensity, number of companies with innovative product and process activities, and investments in research and development out of total investments.

When it comes to goal 12 "Sustainable consumption and production", progress in curbing material consumption—which has characterized Italy since 2010, allowing our economy to gain in efficiency in production processes—has levelled out over the last five-year period, but Italy is one of the EU countries with the lowest domestic material consumption (DMC) both per capita and per unit of GDP, claiming first place in the per-capita rankings and fourth place per GDP. In 2019, the DMC per unit of GDP was stable compared to the 2017–2018 two-year period (0.28 tonnes per 1,000 euros). On the other hand, the circular material use rate—namely the portion of all


**Fig. 8** Reference indicators for SDG9, value for Italy (*Source* Data from ISTAT)

**Fig. 9** Rate of circular material usage, 2019 (% value) (source: data from ISTAT and EUROSTAT

material recycled and fed back into the economy—has seen an improvement between 2010 and 2019, in Italy, whose rate thus comes to 19.3% compared to the EU 27 average of 11.9%; the data also show a greater improvement in Italian performance than the EU 27 average, both over the last decade and over the last year, putting our country in fourth place in the European rankings, after the Netherlands (28.5%), Belgium (24.0%) and France (20.1%) (Fig. 9).

When it comes to goal 13 "Action to combat climate change", a steady reduction in emissions was recorded in the period between 2009 and 2019, both within institutional sectors (families and companies) and within the various activities, albeit with differing intensity. For companies in general, in 2019, the level of the emissions rating was 81.8 (2009 = 100), while for the manufacturing industry, the rating fell to even lower levels than 2009 (75.4). Therefore, manufacturing is a key factor in achieving these sustainable development goals and it is necessary to consider how they can be broken down into actionable research and innovation priorities when defining medium to long term research and innovation strategies in the sector.

# **6 Manufacturing and the Pandemic Crisis**

# *6.1 Global Situation*

Over the last few years, the international landscape has been dominated by the financial crisis triggered by the effects of the COVID-19 pandemic, while the war currently playing out in Ukraine could further disrupt reference scenarios, already beset by significant dynamics of change.

In terms of the pandemic crisis, the measures required to contain the spread of the virus—the adoption of which, from the very start, followed different timelines from one country to the next—have had a profound effect on the social and economic fabric, resulting in a real shock that has simultaneously affected both supply (businesses forced to close and temporary interruption of value chains) and demand (plummeting consumption, increased unemployment, reduced income).

To counter the effects of the lockdown on the economy, all the main central banks promptly and repeatedly intervened with emergency measures to bolster demand, pumping cash into the economy. At the same time, many governments have introduced expansive fiscal measures aimed at shoring up the incomes of their citizens and manufacturers hit hard by the lockdown measures. Despite these best efforts, last year, with the exception of China, all major economies recorded a marked drop in GDP (CSC, 2021).

Structural delays (application of stricter health protocols for unloading cargo, personnel shortages in the transport and logistics sectors), along with "anomalous" growth in the demand for goods—resulting from manufacturing companies and businesses needing to replenish stocks, which had been depleted during 2020—were compounded by chance factors (temporary closure of a number of ports in China or blockage of the Suez Canal).

All the above led to a considerable increase in transport costs and additional bottlenecks in international supply chains, which have had a negative effect on global industrial production growth. Manufacturing's recovery after the most acute phase of the pandemic is following very different trajectories according to sector.

The explosion of the health emergency sent demand skyrocketing for the pharmaceutical industry, at the front line of Covid-19 medicine and vaccine development, and the electronic device industry (due to the accelerated digitalization dictated by isolation, at first, and then by social distancing measures), recording a boom in production volumes worldwide between the fourth quarter of 2019 and the months of June-July 2021 of 15.4% for pharmaceuticals and 12.2% for electronics respectively.

The recovery of capital machinery and equipment has been driven by growing sectors calling for new or reconfigured machinery to help them manufacture products required by the pandemic event (such as face masks, drugs, dedicated packaging).

# *6.2 The Reaction of the Italian System*

In Italy, following the drastic drop in production of over 40% recorded two months out from the introduction of the March 2020 restrictive measures, business volumes began to increase as early as the second quarter of 2021, with production sitting comfortably at its late 2019 levels, experiencing a return to pre-crisis levels that has yet to be seen in the other major European industrial economies.

With the figures characterizing the current phase, Italy no longer plays the role of "follower" behind the other major eurozone economies in terms of manufacturing growth, and—unlike the situation that unfolded in the years following the previous global financial crisis (the 2008–2009 period)—the country's behaviour changes in terms of its ability to respond to shock: this time around, finding itself in the position of driving the area's recovery in production volumes.

The reason for this performance lies, above all, in the dynamic of the domestic component of the demand for goods that—thanks to the government's initial income support measures, followed by spending stimulus measures—has made a crucial contribution to manufacturing's recovery, marking a far cry from the events following the outbreak of the 2008 financial crisis, when national industry's growth was structurally curbed when part of domestic demand was wiped off the board.

While export turnover—due to ongoing challenges in the international context in August this year was up barely 2.8% in value on the pre-crisis peak of February 2020, domestic turnover recorded a 7.0% increase over the same period.

Since late 2020, the global landscape has been characterized by significant increases in commodity prices: price hikes are widespread and concern not just metals (copper up 51% on the end of 2020, and iron up 73%), but food, cotton, timber and oil, too (World Bank data). These price rises greatly affect both Italian companies and consumers, because Italy is a manufacturing country that is heavy on processing and has high volumes of imported commodities.

Between January and March 2021, the increase in turnover was seen across many of the manufacturing sectors, with rates varying to great degrees: while performance was good in the furniture, metallurgy and electrical equipment segments—up almost 30%—and automotive and machinery sectors—up 25% on the first quarter of 2020 there was a noticeably more subdued recovery, or plateau, for sales in some traditional manufacturing segments (textile, clothing) that, in the first quarter of 2020, had experienced some of the most severe drops in turnover of the entire sector (Istat, 2021).

A key factor in the recovery has been the low level of exposure of Italian manufacturing companies to the bottlenecks that are plaguing global value chains at this juncture. According to an analysis by Confindustria, with reference to the beginning of the third and fourth quarter of 2021, "just" 15.4% complained about restrictions on manufacturing supply due to a lack of materials or shortage of equipment, compared with an EU average of 44.3%, and with an even more weighty 78.1% of respondents in Germany. Italy's overseas trade in goods, following the freefall recorded in the second quarter of 2020, has picked up quickly and strongly, climbing convincingly back above pre-crisis levels. In the months of June–August 2021, exports at constant prices topped late 2019 levels by 2.6% (exports up 7.3% in value). Exports of intermediate inputs and investment goods, above all, enjoyed positive performance, while consumer goods, as yet, have seen only a partial recovery. Within the asset category, growth has been driven chiefly by electrical equipment, while capital machinery and equipment have not yet recovered fully.

For what concerns employment, the increase in industrial production starting in summer 2020 was reflected in a significant recovery in the number of job hours, even though, at the end of Q2 2021, it was still below pre-pandemic levels (−4.2%). Manufacturing companies' expectations on the labour demand appear to be improving constantly and significantly, which comes with an increase in the number of companies who are reporting increasing difficulties in procuring the labour required for the production cycle, in a context with a progressive increase in the plant utilization rate.

The business climate is continuously evolving and, following the pandemic emergency, it has become clear just how hard it still is to predict the fallout from the Russia–Ukraine conflict: in addition to generating a crisis in a number of supply chains (prime examples include rolled metal, metal castings and grain), the conflict has also generated a series of unprecedented economic measures against Russia and a parallel increase in the cost of procuring gas, the impact of which on society and on the manufacturing sector is still hard to predict.

# *6.3 Proposal of a New Collaborative Model: Manufacturing a Resilient Country*

Italy clearly emerges as a country characterized by a strong manufacturing sector that, despite the market's economic criticalities in terms of both supply and demand, has managed—even over the course of the last two years—to evolve and reinvent itself, seizing market opportunities to tackle criticalities and the most challenging of economic times.

Having different production sites offers countless economic and social advantages for a country working in a networked system, and the "intelligent factory" (meaning a factory that adopts emerging enabling technologies designed to assist human capital in an efficient way) is becoming a well-established reality thanks also to the efforts of different actors across the country, including the Cluster itself, promoting actions to improve awareness of the role played by manufacturing at the national and European level.

More specifically, during the early stage of the pandemic, the Cluster created a task force to define strategic actions to support manufacturing, their work culminating in the compilation and proposal of the document entitled "Manufacturing a Resilient Country".

The document suggests three types of action intended to cast manufacturing in a leading role for the recovery of the country during emergency periods.

These actions are classified into:


More specifically, drawing on the strong manufacturing base already present in the country and pooling the best existing skills, the Cluster's proposal is for a model to exploit a certain amount of the existing manufacturing capacity and put it in a position to be able to rapidly act on goals determined in times of emergency, without prior warning.

The term manufacturing capacity is used here to mean all activities required to make and deliver the product to its point of use within the right timeframe and in the right conditions. Hence, an implementation model is proposed that must be based on the creation of new collaboration opportunities that can be used for the activity in question, but which can also be harnessed for other initiatives.

The plan is to create a public–private collaboration model that, if activated in times of crisis, would enable companies to swiftly produce the necessary volumes of all products required to handle the situation, and to help geographical areas that do not have such an emergency response system in place by supplying necessary products in situations of global significance, increasing their resilience.

When this system is activated under normal circumstances, it allows the country to progress thanks to the new products stemming from the creative interaction between various stakeholders, the creation of products and machinery for pioneering sectors, the creation of new products in the health industry and civil defence sector and, more generally speaking, products and solutions for societal challenges in the event market rules are insufficient to automatically create trigger conditions.

# **7 Evolution of Italian Manufacturing and the Roadmap Objectives**

There are various explanations for Italy's apparent paradox of a high capacity for innovation coupled with low R&D spending. Firstly, the small average scale of manufacturing companies in the country, which results in most innovation activities not being formalized. Then there is the fact that the Italian industrial system has a strong presence in sectors in which innovation mostly takes the shape of incremental development of production processes and products (learning by doing, learning by using and learning by interacting), incorporating new technologies into machinery or into patents and licences, while less prevalent is innovation based on the introduction of radically new tech, necessarily entailing underlying scientific research activities (ranging from fundamental to applied research).

Supply chains are currently undergoing a shake-up that—paired with the technological discontinuities stemming from the dual digital and green transition represents a structural change factor in the competitive landscape, which leads to a profound transformation in value-creation mechanisms.

Emergent collaborative models are requiring companies to demonstrate a new capacity for innovation since it is proving increasingly necessary to:


(iii). keep up with the constant evolution of market needs.

Investment along these lines is not limited to aspects directly related to production process efficiency, and instead increasingly encompasses the various company functions both upstream and downstream, from design to configuration all the way through to distribution and after-sales, embracing a logic of growing the intangible component of the product's value.

This gearing towards new strategies today calls for increasingly structured and "coded" forms of innovation, with formal product research, development and design activities being promoted alongside the existing unofficial exchange of information. At the same time, companies should be systematically making use of the data available to them with the aid of digital tech for activities such as process and product monitoring—which also ties in with sustainability—analysing market changes, and engaging employees in the formalization and use of knowledge, and in the development of knowledge and skills through training.

This is also required to cater to the growing demand from end consumers, financial markets and legislators for information on the sustainability of production processes and supply chains. Italy has a unique heritage in terms of tradition, culture, skills, image, design and technologies, which represent the optimal environment for a manufacturing sector that produces high-added-value products and services. More specifically, the distinctive resources on which Italy is privileged to draw are:


The engine behind this transformation should be a research and innovation process based on collaborative approach along supply chain, accompanied by a training plan designed to refocus the set of skills within the national industrial context. A multi-year research plan must leverage the qualities of Italy's available production resources and must be aligned with research challenges and international trends in the manufacturing field.

# **Suggested References**


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# **The Role of Industrial Policies: A Comparative Analysis**

**Rosanna Fornasiero and Tullio A. M. Tolio**

**Abstract** The aim of this chapter is to analyse and compare European, Italian and regional industrial policies aimed at promoting the research and innovation activities, with focus on manufacturing sector. The analysis is based on secondary data collected from websites, documents issued by related governmental bodies and grey literature which are compared along scientific topics of interest. Moreover, the chapter discusses how these policies are expected to have an impact on industrial competitiveness and how these policies are interconnected each other. A comparative analysis of the regional and national priorities is also proposed as the result of an iterative collaboration with regional actors. The chapter closes with the analysis of the role of the cluster in supporting industrial policies.

**Keywords** Industrial policy · Research policy · Public–private partnerships · Europe · Italy · Regions

# **1 The European Research Strategy and Actions**

The ongoing plan Horizon Europe set out in 2021 is an ambitious research and innovation programme to allow the EU both to strengthen the outcomes achieved with H2020 and fortify Europe's frontline position in the research and innovation sector at the global level. Horizon Europe's purpose is to boost the scientific, social and economic impact of European research funds. Several programmes have a strong focus on close-to market activities including innovative financial instruments, and aspires to meet research needs by placing emphasis on widespread generation of knowledge generated through activities supported from basic research to the market.

T. A. M. Tolio Politecnico Di Milano, Milano, Italy

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_3

R. Fornasiero (B) CNR-IEIIT, Padova, Italy e-mail: rosanna.fornasiero@cnr.it

The strength of Europe starts on the back foot, grounded on delivering 17% of worldwide research and 25% of high-quality scientific publications with an average investment in research and development amounting to 1.5% of GDP, which is still very low compared to other high-tech countries, such as the US (2.1%), Japan (2.6%) and Korea (3.6%) (EU, 2020).

With a budget of 95.5 billion euros, the programme Horizon Europe is implemented over the course of 2021–2027 through 3 pillars (Excellent Science, Global Challenges and European Industrial Competitiveness, Innovative Europe). Horizon Europe stands out from previous programmes for its more integrated approach of pooling various financing instruments and, above all, is much more focused on achieving tangible application outcomes.

Of the three pillars, two are explicitly geared towards applied research in areas defined as priority areas for European industrial competitiveness in response to major global challenges (Pillar II) and towards radical product and process innovations by European companies (Pillar III) respectively, with a view to helping them achieve commercial success in global markets.

Within pillar II, the most important area for the manufacturing sector is cluster 4 "Digital, Industry and Space", whose goal is to support investments in competitive and trusted technologies for a European industry with global leadership in key areas, with production and consumption models that respect the planet, maximizing benefits for society in the different social, economic and regional contexts in Europe.

The overarching aim is to create a competitive, digital, circular, low-carbon industry, with the goal of ensuring the sustainable supply of raw materials, developing advanced materials, and introducing innovation to meet global challenges in society. More specifically, within the cluster 4 programme, medium-term goals (referred to as destinations) have been defined that, in turn, are broken down into calls for collaborative projects (like RIA, IA and CSA); for the first two years of Horizon Europe, these goals are:


Going in particular to analyse the manufacturing sector, Made in Europe is the new partnership established as part of the Horizon Europe Framework Programme (2021– 2027) specifically focused on this sector. The creation of the Made in Europe partnership has been discussed between the relevant bodies involved, European Commission, member states and EFFRA association since 2019. The partnership's reach and ambitions were defined in the Made in Europe orientation paper and, in 2021, the Strategic agenda for research and innovation was published.

The European partnership involves the whole manufacturing value chain in Europe to support the promotion of manufacturing excellence among companies, especially SMEs, to ensure competitiveness and sustainability across the European manufacturing industry, defending Europe's technological leadership around the world, as well as the prosperity and wellbeing of employees, consumers and society.

Made in Europe will make a substantial contribution for facilitating the networking between the main actors who steer and/or implement manufacturing innovation initiatives at the national and regional level, and its goal is to engage in dialogue with new actors, such as local authorities tasked with drawing industry to their cities and municipalities. In particular, the goal of the Made in Europe partnership is to propel European manufacturing ecosystems towards global leadership in manufacturing technologies in line with sustainability principles. The partnership is working to create a competitive, green, digital, resilient and human-centred manufacturing industry in Europe.

More specifically, manufacturing will be central to the twin transition (green and digital) in line with the European Commission's Green Deal, given that the sector is both an engine driving such changes in other sectors and it is itself on the receiving end of these innovations.

There are several European initiatives supporting industry and one of the most recent with the actions that may affect manufacturing is the Chips Act. The European Commission has proposed a comprehensive series of measures aimed at ensuring supply security, resilience and technological leadership for the EU in semiconductor technologies and applications. The European Chips Act has the goal of strengthening Europe's competitiveness and resilience, and it can help bring about both the digital and the green transition.

The European Commission has set out to mobilize over 43 billion euros in public and private investments through measures designed to prevent, prepare for, anticipate and respond rapidly to any future supply chain interruptions, together with member states and our international partners, with the aim of achieving its ambition of doubling its current market share in this sector to 20% by 2030.

More specifically, the objectives of the European Chips Act are:


The Chips for Europe initiative will pool the resources of the Union, member states and third-party countries involved with the Union's existing programmes, as well as those of the private sector, by means of the "Chips Joint Undertaking" resulting from the reorientation of the existing Key Digital Technologies Joint Undertaking.

A substantial 11 billion-plus euros will be made available to support research, development and innovation in the sector and to ensure the use of advanced semiconductors, in innovative applications. Actions will also be identified aimed at training and reskilling in the industry, and at the development of the research and innovation ecosystem and relevant value chain.

There are plans for a new framework to ensure supply security by attracting greater investments and manufacturing capacity, required to promote innovation in advanced nodes, and innovative, energy-efficient chips.

Moreover, a Chips Fund will facilitate access to funding for start-ups in order to assist them in bringing their innovations to fruition and attracting investors. It will also include a share investment mechanism focused on semiconductors as part of the InvestEU programme to support scale-ups and SMEs to facilitate expansion of their market.

# **2 The Matching Between Cluster Objectives with European Goals and SDGs**

In defining the roadmap objectives, the Cluster of Intelligent Factories refers to a number of strategic documents at the international and European level. More specifically, the Cluster aims to contribute to the definition of research and innovation themes aligning with a number of important European policies and, beside the aforementioned, other policy documents are: A new industrial strategy for Europe (EU 2021), The European Green Deal (EU 2019), A Europe fit for the digital age (EU 2021), An economy that works for people/Building a strong social Europe (EU 2021).

The Cluster works closely with the Made in Europe Co-programmed partnership and with the Manufuture European platform—since a number of actors within the Cluster also operate at the European level—and sets out to define research and innovation priorities that help manufacturing achieve the following European goals:


At international level, the Sustainable Development Goals (SDG) of the United Nations (UN 2019) are considered as a parameter to evaluate the impact of manufacturing in terms of performance. Moreover, in defining research and innovation priorities, the Cluster endeavours to analyse how manufacturing can contribute directly to the pursuit of the following UN goals:


During the activities set up with the Scientific Board of the Cluster aimed to identify the overall goals, it was possible to identify the links between the Cluster's goals, the EU's strategic documents and UN goals (see Table 1).


**Table 1** Breakdown of EU and SDG goals versus the Cluster's goals

# **3 The National Research Strategy**

In the last years, the Italian Government has set some strategies to support industrial policy with research and innovation activities that can be mainly recognized in the multi-year National Research Programme (PNR) and the National Plan for Recovery and Resilience (PNRR).

The National Research Programme (PNR) is a periodic document that orients research policy in Italy, identifying priorities, goals and actions aimed at supporting coherence, efficiency and effectiveness across the national research system, defining guidelines at the national level (PNR 2020). The ongoing PNR 2021–2027 has been structured to track alongside the Horizon Europe programme in terms of timelines and themes, breaking the six clusters included in pillar II into six broad research and innovation areas developed through intervention areas such as:


Thus, the national strategic vision is aligned with European programmes, including elements of complementarity aimed at promoting interventions to help Italian research system to increase its competitiveness and to become an increasingly key player on the European stage.

The potential ensuing advantages are not limited to the possibility of achieving shared access to R&I funding, instead there are inherent benefits to collaborative research, most notably those resulting from sharing outcomes and collaboration with other countries' national R&I systems.

These R&I areas covered in the PNR also allow to tie in with the goals of the European Green Deal, hence making the PNR a tool designed to provide a significant contribution to the green transition, in which the conservation of natural capital, biodiversity and the processes that depend on it—and on which the very life of the planet depends—becomes a necessary common condition for the pursuit of the goals of prosperity and wellbeing identified by the European Green Deal.

Another of the priorities identified in the plan is promoting the flow of knowledge and skills between research organisations and the manufacturing system, and exploiting research outcomes through a virtuous cross-fertilisation process to ensure the country's competitiveness, even more in the current twin transition, both green and digital.

The document states that public intervention is required to help kick-start this cross-fertilisation, and indicates the challenges to be tackled, the goals to be achieved and setting out consistent action lines. The PNR stresses that in policy planning, it is necessary to take into account the starting conditions and, above all, the gap by which Italy lags behind other European countries in terms of the propensity of businesses to cooperate on innovation.

This gap is also underlined in the European Innovation Scoreboard (EIS 2020), which, for years, has been putting our country in the "moderate innovators" category, with performance trailing behind the European average in terms of collaboration between enterprises, and between enterprises and the public research system. Hence, promoting innovation necessarily goes hand in hand with strengthening relationships between research and the manufacturing system, fostered by mobility programmes between the research institutions and industry, and targeted technologytransfer strategies that facilitate the transition from fundamental and applied research to ideas delivered to the market more successfully.

As part of the National strategy for research and innovation, it is useful to analyse also the National Plan for Recovery and Resilience (PNRR) conceived as a planning document for specific investments and reforms for Italy after the response of the European Union to the pandemic crisis with Next Generation EU (NGEU). The document is set for planning investments and reforms to speed up the green and digital transition, improve worker training and achieve greater gender, regional and generational equity.

For Italy, NGEU represents an opportunity for development and investment that it cannot afford to squander. Italy must modernize its government bodies, strengthen its manufacturing system, and step up its efforts to fight poverty, social exclusion and inequality. NGEU might be just the opportunity to resume a sustainable and lasting economic growth path, removing the obstacles that have stood in the way of Italian growth in recent decades.

The PNRR has been formulated following an intense preliminary analysis and research phase, and comprises sixteen components, grouped into six Missions:


With this document, the government plans to update national strategies around development and sustainable mobility; environment and climate; hydrogen; automotive; and the healthcare supply chain. In addition, the PNRR contains a very important chapter on training for companies to ensure the growth of human capital required to successfully bring about the twin digital-sustainable transition.

In terms of the competitiveness and resilience of the manufacturing system, the PNRR plans to pull different levers to strengthen and modernize the operational capability of companies in our country; more specifically, part of the plan is to promote digital transformation processes in Italian companies, and boost tools for the digital transition of the manufacturing system, and complete the digital infrastructure rollout process through the twin digital-sustainable transition.

# **4 Regional Specialization Strategies for the Manufacturing Sector**

A look at the way the manufacturing sector is distributed across the Italian regions reveals heterogeneous situation in terms of concentration, value added and other important indicators. In particular, there is a strong concentration of manufacturing companies in Lombardy, which accounts for 27% of Italian value in terms of turnover and value added, with values across all 4 dimensions (companies, turnover, value added and number of employees) representing at least double the value individually reported by the next 3 top-ranked regions (Veneto, Emilia-Romagna and Piedmont).

In the international rankings, four of the European Union's top ten manufacturing regions are Italian: Veneto, Emilia-Romagna, Lombardy and Piedmont. According to the NUTS2 rankings, Lombardy is the EU region with the second highest number of employees in manufacturing companies, behind the French Paris region, Île de France. Out of the top ten European manufacturing regions, Veneto sits in second place for the ratio of manufacturing employment to population, with 10.6 employees in manufacturing companies per 100 inhabitants, behind Stuttgart (14.4 manufacturing company employees per 100 inhabitants). Emilia-Romagna, Lombardy and Piedmont placed 4th, 6th and 8th respectively, with 9.9, 8.9 and 8.1 employees per 100 inhabitants. Moreover, Emilia-Romagna has a per-capita manufacturing value added of 7899 euros against the Italian average per-capita value of 4,278 euros, followed by Veneto on 7,335 euros per capita and Lombardy on 7,030 euros per capita.

Looking at research spending in 2018 (Fig. 1), over a third of R&D is conducted in the Northeast, while the combined contribution of the Southern regions and islands comes to almost 15%. Notably, 68.1% of total spending, amounting to around 17.2 billion euros, is concentrated in five regions, namely Lombardy on 20.6%, Lazio on 13.7%, Emilia-Romagna on 13.0%, Piedmont on 11.8% and Veneto on 9.0%.

Overall, companies in the five major manufacturing regions account for 75% of national research in the sector. More specifically, out of the most virtuous regions, Lombardy makes the biggest contribution to total spending with 25%. In 2018, Lombardy, Lazio and Emilia-Romagna were again the regions where universities had invested the most in R&D and, together with Campania and Tuscany, they accounted for 55.7% of total R&D spending in this sector. Looking at the incidence of R&D spending as a percentage of GDP (Fig. 2), the regions' rankings range from Piedmont's top rate (2.17%) to Aosta Valley's lowest rate (0.48%). In addition to Piedmont, the regions with the highest R&D spending as a percentage of GDP are Emilia-Romagna (2.03%), Lazio (1.75%), Friuli-Venezia Giulia (1.67%), Province of Trento (1.56%) and Tuscany (1.55%).

In terms of research policies, national and regional research and innovation strategies for smart specialization (RIS3) are integrated, place-based economic transformation agendas designed towards five important actions:

(1) Focus policy support and investments on key national and regional priorities, challenges and needs for knowledge-based development;


**Fig. 1** Italian manufacturing distribution per Region

**Fig. 2** Intra-muros R&D spending by Region, 2018 (%GDP)


All the regions participating in the Cluster CFI activities, as part of their respective specialization strategies, have defined priorities and goals that concern the promotion of models, methods and technologies to support the implementation of intelligent factories, as a path to the economic and industrial growth of the region, as well as the improvement of social integration with a new role for industry.

These policies have been developed based on analysis of the industrial and research system's local conditions, compared with state of the art of technologies and global trends, backed by the application of foresight and roadmapping methodologies.

The regional strategies analysed as part of the Cluster's activities share a common far-reaching vision of innovation, with approaches based on—but certainly not limited by—technology and the main, cross-cutting drivers associated with it. They have all paired a monitoring system with shared output, specialization, transition and outcome indicators coordinated nationally for monitoring of the national S3.

All regional authorities have worked over this period on defining the proposal on how to update their individual S3 for the 2021–2027 period, formulated in light of: the changes observed in the regional manufacturing system and relevant innovation challenges identified; the strategic-planning reference framework at the European, national and regional level; the outcomes from various exchange and feedback sessions with regional stakeholders; and also building on the experience gained over the 2014–2020 agenda period.

During the course of 2020, under the aegis of the Cohesion Agency, the Cluster has turned the spotlight on the interregional table, which involves both the regional authorities already formally partnering with the Cluster, as well as candidate authorities (Friuli-Venezia Giulia, Province of Trento, Umbria and Basilicata), for the sharing of new specialization strategies.

For the sake of this work, the roadmapping group of the Cluster, with the support of the bodies representing the regional authorities in the Cluster Management Board, carried out a deep analysis of the strategies for research and innovation developed by each region in the RIS3 to align the Cluster proposals with these requirements, endeavouring to map the research priorities and strategic lines of the two analysis levels (regional and national). As illustrated by the diagram below (Figs. 3 and 4), the Cluster research and innovation priorities (which will be described in the coming chapters) can be seen as a macro container collecting the interests of the various regional stakeholders. Each region has its own specificities, and some regional priorities have been mapped along different strategic action lines.

**Fig. 4** Cluster research and innovation priorities per Region

# **5 The Role of Clusters in Definition of Industrial Policies**

From this analysis, the definition of research and innovation roadmaps (at any level) leverages on the interconnection between the manufacturing system and public research and policy definition and this is where public–private collaboration models also come in, and initiatives like national and regional Clusters serve as a valuable tool in this direction. In countries where manufacturing accounting for such a significant portion of GDP and employment, it is clearly important to successfully overcome the strategic limit of the actual strategies, giving enough space to sectors that have demonstrated—backed up by figures—their worth as producers of wealth. Moreover, a new governance model should be developed to assign a leading role to partnerships that have been operating across the country for years, such as national and regional clusters, and design mechanisms to highlight specific priorities for capital goods. The role of networking stakeholders, mapping research needs, helping institutional communication, helping knowledge and technology transfer is enabled by these actors of soft governance.

Hence, the Cluster sets out pathways for the evolution of Italian manufacturing, bringing stakeholders like industries and researchers together into a national collective to support an ongoing dialogue with Ministries, Regional authorities and institutions, even at the European and international level, with a national vision for the manufacturing system defined through this roadmapping initiative.

To cement a stable position in the global competitive arena in the field of manufacturing, Italy must innovate its manufacturing sector to leverage the resources available to it and generate added value in both traditional and new markets.

This innovation must include, on the one hand, achieving greater productivity and, on the other, the development of new strategic industries, leveraging the existing successful production structure. In view of this, the roadmap defines visions for future manufacturing that can:


This role of the Cluster promotes coherence between the various practitioners, to overcome fragmentation issues, and improve the ability to implement actions that are coordinated and, where possible, concerted so as to ensure the sector's harmonious growth. This organic framework is set to benefit businesses as well as universities and research bodies, i.e. the practitioners of research and innovation.

**Fig. 5** Framework of integration between regional, national and European policy level

Backed by such a framework, each actor will actually be in a position to take part in regional, national and European initiatives through projects that—starting with the local and progressing to national and, ultimately, international projects build on the expertise gained along this growth path. On the other hand, low TRL outcomes on the European or national level can be allowed to evolve further within the regions, possibly through demonstrators and pilot plants (Fig. 5). Lastly, multiregional initiatives taken in different countries can lead to European-wide initiatives (e.g. Vanguard).

**Acknowledgements** This chapter was developed thanks to the support of the roadmapping group of the Cluster of Intelligent factories. In particular, we wish to thank the Regional bodies participating to the activities promoted for the chapter on "Regional specialisation strategies" namely: AFIL for Lombardy Regional Council, AR.TER for Emilia-Romagna Regional Council, Cluster Lucano Automotive for Basilicata Regional Council, Cluster Marche Manufacturing for the Marches Regional Council, COMET for Friuli-Venezia Giulia Regional Council, HIT for Trento Autonomous Provincial Council, MEDISDIH for Apulia Regional Council, MESAP for Piedmont Regional Council, SIIT for Liguria Regional Council, Sviluppoumbria for Umbria Regional Council, VenetoInnovazione for Veneto Regional Council.

# **References**

European Commission. (2019). *The European Green Deal*.


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Building Scenarios for the Future of Manufacturing**

**Rosanna Fornasiero, Tullio A. M. Tolio, Melissa Demartini, Walter Terkaj, and Flavio Tonelli**

**Abstract** Forecasting future scenarios is an approach that enables companies, governments, and countries to interpolate the most important trends unfolding in a certain context to help tackle the uncertainties of a world that is changing ever more rapidly. The future outlook can be influenced by many different variables and it is reasonable to consider different possible scenarios. This chapter briefly describes the scenarios discussed and validated by the National Cluster of Intelligent Factories as some of the most promising to influence and change production systems. The work is based on the analysis of various trends from economic, social, technological, and environmental dimensions that have been clustered. The expected influence on the manufacturing sector has been discussed and validated with the support of a group of experts.

**Keywords** Future scenarios · Consumption trends · Electric mobility · Circular economy · Digital economy

R. Fornasiero (B) CNR-IEIIT, Padova, Italy e-mail: rosanna.fornasiero@cnr.it

T. A. M. Tolio Politecnico Di Milano, Milano, Italy e-mail: tullio.tolio@polimi.it

M. Demartini EADA Business School, Barcelona, Spain e-mail: m.demartini@eada.edu

W. Terkaj CNR-STIIMA, Milano, Italy e-mail: walter.terkaj@cnr.it

F. Tonelli Università Di Genova, Genova, Italy e-mail: flavio.tonelli@unige.it

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_4

# **1 Introduction**

Future scenarios can be forecasted to enable companies, governments and countries to interpolate the most important trends unfolding the external environment to tackle the uncertainties of a world that is changing ever more rapidly (Amer et al., 2013). Scenario planning is used to support public policy decisions and industrial policy in various contexts using qualitative methods to gather the approval of industry experts and quantitative methods to forecast phenomena based on the interpretation of historical data (Georghiou, 2008).

It is important to create different scenarios, taking into account a number of dimensions, namely economic, social, technological, environmental trends, as sources of possible changes to have a vision of the future based on causal relationships between exogenous and endogenous variables with the aid of experts from various fields or with the use of forecasting methods.

The pandemic has further accelerated sudden changes, while the crisis linked to the Russia-Ukraine war likewise has a profound impact on energy scenarios, the supply of materials and production supply chains, as well as an impact in terms of outlets for specific sectors. In this context, it becomes even more important, beyond the individual trends, to understand the possible interactions between current and forecast changes, and develop potential future scenarios that can serve as a reference for manufacturing's development.

The creation of future scenarios facilitates the ability to set up strategies to deal with ongoing changes and can give a country or its manufacturing system a competitive edge. In addition, if informed by appropriate industrial policy actions that intervene at the national and regional level, this ability is a starting point for the future.

Herein, a mixed approach has been applied while starting from statistical data on exogenous trends. A group of experts from the Cluster for Intelligent Factories has been involved through focus groups with an iterative method to support the following phases:


The roadmapping group of the Cluster has managed to involvement of the experts along the six scenario definition taking into consideration their expertise. Both experts from academia and industry have been involved with brainstorming sessions and interviews.

Six scenarios are presented in this chapter, focusing on relevant topics such as electric mobility, new consumption models, circular economy, knowledge management, digital economy, and climate change. Each scenario is introduced with its current context and possible future evolutions. Then the impacts on manufacturing and action lines are outlined to support the preparation and adjustment of manufacturing strategies.

# **2 Scenario 1: Electric Mobility: A Supply Chain Challenge**

In 2019 more than 2.1 million electric cars have been sold worldwide, outperforming 2018—which was already a record year—for a grand total of 7.2 million electric cars (which also includes plug-in hybrid electric vehicles).

While accounting for only 2.6% of global car sales and approx. 1% of the global car installed base in 2019, electric cars are recording a 40% year-on-year increase (IEA, 2020). With the advancement of new technologies in vehicle, bus and truck electrification and their growing market, electric vehicles are expanding significantly. In recent years, ambitious policy interventions have been key to stimulating the launch of electric vehicles in the main markets.

Moreover, the 2019 indications of a shift from direct subsidies to more systemic approach, based on regulatory measures on zero-emission vehicles and new fuel economy standards, provided clear signals to the automotive industry and consumers that an economically sustainable transition can be achieved in the medium to long term, also with the help of governments.

Italian car manufacturers are also working on electric vehicles and related technologies to meet an exponentially growing demand. In Italy, there is a significant ecosystem of both electrical component and power electronics manufacturers, relying on the expertise of many product design and engineering hubs. The role of machinery manufacturing companies in the industrial production of components already providing manufacturing solutions to the electric mobility industry—is also significant.

Major structural interventions are required on several fronts to facilitate the transition to electric mobility:


In particular, investments are required in treatment plants for the re-purposing, reuse for static uses and material recycling of batteries from various applications.


To understand the potential effects on the automotive value chain, it is worth noting that 50% of the industrial cost of an electric car is not present in a traditional internal combustion engine car. And the effects are not limited to the powertrain, but also extend to the body, chassis and interior.

An average of 6.2 h of labour are required to assemble the engine and transmission of internal combustion engine cars. In the case of plug-in hybrid cars, the number of hours increases to 9.2, since the electric motor and batteries must also be assembled. On the other hand, for a full electric car, the number of hours decreases to 3.7, i.e. 40% less, because the engine and transmission are simpler.

Therefore it is necessary, at the Italian level, to organize and orient academic and industrial expertise, coordinating stakeholders like researchers, design and product engineering companies, industries (automotive, electrical and electronic component, chemicals and SW development excellences), utilities and transport companies, in order to "work as a system" and facilitate the creation of supply chains aimed firstly at the development and production of electric vehicles fully made in Italy. The contribution to the country's GDP and industrial resilience that this sector represents is given by the 100 billion turnover of the automotive supply chain (including lower tiers suppliers), 258,000 workers, with a worker multiplication factor equal to 3, and 3.9 billion investments in the industrial sphere that should be oriented by national industrial policy precisely towards the production of electric vehicle components.

In terms of current supply chains, electric is a fast-growing sector even though, as mentioned, figures compared to traditional cars are still very much on the low side for now.

Therefore, the traditional car business cannot be excluded from the equation, as this will still be the main market for at least another decade. Italy is well positioned as a country working together in a networked system, especially regarding automotive components.

Moreover, it is necessary to monitor the development of electric mobility since this may not be the end point but simply a stage in the transition to more environmentally friendly mobility, where the combination of hydrogen with electric, for example, could be the solution for industrial vehicles.

The expected impact of the scenario on R&D activities is associated with the observation that investments in electric mobility are yielding results in the short term. Still, structural interventions are essential, potentially leading to significant transformations in the medium to long term. These interventions can help minimize the inherent national dependence on those controlling the necessary raw materials during this transition.

This scenario should be studied from the point of view of the following action lines:


# **3 Scenario 2: New Consumption Models**

Before the Covid-19 pandemic, 30–40% of luxury goods sales were generated by consumers passing through airports and abroad. However, with the restrictions on travel and movements during lockdown, global tourist spending has halved, while domestic luxury purchases have doubled.

Generally speaking, there are consumption models that are changing radically and prevailing over others. One of these is the luxury market in China, where last year sales increased by 48%, sitting at around 350 billion yuan (\$54 billion), bolstered by domestic consumption due to a reduction in overseas travel (and hence purchases).

Partly thanks to pop-up stores used by brands to sell directly on social media, the Chinese market has increased purchases from overseas, encouraged by a much lower tax rate than on the Chinese portals (namely 9% compared to 40–50%). The winners from this trend were not just the big Italian luxury brands, but niche brands, too, since anyone who managed to communicate effectively through the right channels during lockdown enjoyed a real surge in sales (McKinsey, 2021).

This surge has contributed to the doubling of China's total share of the global luxury market in 2020, going from about 11% in 2019 to 20% in 2020, with an additional increase forecast by 2025. A drop in international tourism contributed to two- or even three-figure increases in the rate of domestic luxury goods spending for some brands. Growth rates varied wildly across the different regions, brands and categories.

When it comes to engines fuelling the birth of new consumption models, generation Z and millennial consumers are expected to continue spending on luxury goods and customized products as almost three quarters of them say that they will increase or maintain current spending levels on these kinds of goods. Consumer online shopping behaviour has changed for good, having overcome the obstacles of recent years (i.e. digitalization).

Moreover, most brands feel that the online penetration of luxury goods (including omnichannel retail) will reach 20–25% within three years. Between 2019 and 2020, e-commerce has grown by at least 50% in Europe and China, as well as in the United States, according to McKinsey.

In the second quarter of 2020, Farfetch sales were up almost 75% on the previous year, hitting a turnover of \$365 million. Brands like Nike and Louis Vuitton have seen an increase in online sales, which they attribute partially to the customization of the shopping experience delivered across all sales channels. To achieve this, fashion brands are defining new customer experience solutions blending of AI and other technologies. This also needs to be backed by adequate design and production systems to provide the luxury or customized goods demanded by the market.

Social shopping can be a channel to support the luxury market and product customization, while the presence of increasingly environmentally conscious and concerned consumers prompts a change in the type of offering, which must be based on traceable and environmentally friendly products.

This scenario can represent a driving force for Made in Italy manufacturing in various sectors and opens up new challenges linked to the need to have production systems in place that allow companies to increase their product customization capacity, and cater to the demands of some geographical regions, both by defining products specifically tailored to their needs, and by offering product configuration systems that are interfaced with the production systems.

As for technological development, the potential of the Internet of Actions (IoA) will enable companies to review their offerings, facilitating the sensory experience linked to the purchase of certain products, delivering a new customer experience in store and online thanks to the development of vision systems and virtual and augmented reality.

While the growth of the social component at the sales and loyalty stage opens up a challenge when it is implemented through multichannels, at the same time it also presents an opportunity since it gives companies access to huge amounts of data about customer preferences, attitudes and needs, allowing them to predict and understand the demand for the design of new product generation and related manufacture increasingly tailored to customer requirements.

The expected impact of the scenario on R&D activities is linked to the ongoing evolution of new consumption models. Urgent interventions are needed in the short term to support design and manufacturing activities with new models and methods.

This scenario should be studied from the point of view of the following action lines:

Ll1: Personalized production,


LI7: Digital platforms, modelling, AI, security.

**Fig. 1** Earth Overshooting day

# **4 Scenario 3: Circular Economy**

Relentless population growth over the last two centuries (from 1 billion in 1800 to 7.9 billion in 2022) has led to a progressive increase in the demand for natural resources, which includes raw materials, water, energy and arable land, as highlighted by "overshoot day" calculations (Fig. 1).

This value measures humanity's environmental footprint and the ability of the Earth—both at the global level and with reference to individual nations—to regenerate the resources consumed over 365 days, including in terms of its ability to absorb emissions released into the atmosphere.

Over the last 50 years, the overshoot day has fallen progressively earlier, meaning that humans are consuming resources quicker than the Earth can regenerate them. The increase in demand for these resources puts a lot of pressure on the environment and, at this rate, material consumption worldwide is expected to increase roughly eightfold by the end of 2050.

Another important trend that adds to the pressure on the environment is the increase in urbanization, which entails an increase in the use of raw materials for construction, such as roads, bridges, dams, sewers, and the need to step up transport systems.

In addition, the rapid shift in the global scenario characterized by crises—such as the COVID-19 pandemic, socioeconomic conflicts, and raw material and energy shortages—has exposed the fragility of our current linear system. There are loud calls to "rebuild better" with a green recovery—thus repairing the impacts of the pandemic and including the climate crisis—as documented by the recent Recovery Plan for Europe budget.

Consequently, it is necessary to improve how resources are being handled currently to identify opportunities for greater wealth for people, while still employing environmentally responsible practices.

This shift is based on the circular economy concept (MacArthur 2015) that considers factors capable of reducing waste and monitoring the consumption of resources more closely. The circular economy reduces the need for new raw materials, instead reusing product functions and existing materials. This practice that can be implemented by rethinking the function of the product in a closed loop.

This extends the life of resources and includes strategies such as reuse, repair, regeneration, recycling and reducing overall negative effects of manufacturing activities on the environment, which call for a thorough overhaul of companies' activities.

The circular economy not only brings challenges for companies, it also allows them to obtain economic, environmental and social benefits, such as giving impetus to innovation and economic growth, increased resilience and competitiveness, reduced pressure on the environment, optimized availability of raw materials, and job creation.

In addition, consumers can buy long lasting products that save money, are innovative, and capable of improving quality of life. Embracing circular economy systems and strategies thus entails macro- and micro-level changes.

At the micro level, companies need to rethink and redesign their production processes to use renewable sources of energy and materials, extend the products' life, create sharing platforms, reuse and regenerate products or components, and rethink products as services.

At the macro level, the European Union sponsored the Circular Economy Action Plan in 2015 to prompt countries to invest in the circular economy area and monitor investments through the Circular Economy Monitoring Framework. This tool is used to analyse and compare the various countries in terms of circular economy.

Figure 2, for example, features a Sankey diagram of material flows in the European Union and the circular material use rate, or circularity rate, namely the amount of material recycled and fed back into the economy.

Both the Sankey diagram and the circularity rate are part of the EU circular economy monitoring framework (Fig. 2). The purpose of a circular economy is to retain the value of products, materials and resources for as long as possible, integrating them back into the production loop once they reach the end of their life cycle, minimizing the waste generation.

Materials like biomass, metals, minerals and fossil fuels are extracted from the environment to make products or produce energy. At the end of their life cycle, products can be recycled, incinerated or discarded as residual waste. These material flows are a core component—effectively the only component—of the circular economy. The fewer products are discarded and the more recycled, the fewer materials are extracted, for the sustainability of society and the environment.

While this scenario may therefore represent an opportunity to increase companies' resilience and competitiveness, at the same time it also poses a challenge for Italian manufacturing—on the one hand, there is the opportunity to reap benefits from a circular system (i.e. reduced production costs, increased resilience, job creation,

**Fig. 2** Material flows in EU, 2020 (bln ton per year)

benefits for both the environment and quality of life); on the other, this change calls for financial and structural efforts with serious impacts on products, processes, governance and the supply chain, such as:


The expected impact of the scenario on R&D activities is significant, given the ongoing implementation of various circular economy initiatives that are making a powerful short-term impact. Planned investments in R&D are mainly focused on the medium term in several directions. The focus is on enhancing processes like recovery, recycling, re-manufacturing, aiming not only for sustainability but also for efficiency with the support of emerging technologies.

This scenario should be studied from the point of view of the following action lines:


# **5 Scenario 4: Knowledge Management and Internet of Actions**

The rapid and pervasive spread of the Internet, digitalization, and cyber-physical systems (CPS) is clearing the path for a novel technological transformation facilitated by the Internet of Things (IoT). In addition, manufacturing is becoming more data and knowledge-intensive (Grigorescu, 2020), thus enabling remote monitoring and interactions for the stakeholders, including managers, operators, and customers.

One development scenario that is progressively solidifying involves the concept of the Internet of Actions (IoA) as a solution. IoA enables entities to share not only data but even sensations and actions thanks to state-of-the-art smart sensors and actuators (Forbes, 2020).

Effective IoA systems will necessitate the precise replication of sensations to generate interactive and adaptive (re)actions by humans and devices (Xu, 2021). Above all, the development and exploitation of sensors and actuators will play a pivotal role in establishing a remote sense of presence and facilitating accurate and safe remote actions (Javaid, 2021). Devices employed in IoA architectures will need to manage interactions with both the environment and human beings.

Factories provide an ideal testing environment for investigating and developing suitable IoA systems since factories already incorporate many of the underlying technologies that drive IoA. Subsequently, IoA systems can be extended to other domains. For instance, IoA enables operators in industrial plants, even when situated at substantial distances from one another, to exchange data, information, and actions, thus ensuring that all operators have the same vision and perception. Complete remote support and maintenance can offer relevant advantages (Silvestri, 2020), particularly where there is a shortage of local specialized workers and when it becomes imperative to operate in intrinsically hazardous or unsafe environments, such as during a lockdown.

This scenario would enable the manufacturing sector to devise strategies for achieving comprehensive remote support and maintenance for manufacturing facilities. Digital technology can effectively support operators to impact the real world with actions generated in a mixed-reality environment (Seiger, 2021). In the long term, once the technology is more mature, applications can be extended to many other unstructured environments, such as the home, entertainment, shopping processes, inspection and exploration.

This scenario entails the development of IoA devices and systems with processors, sensors and actuators, while further developing and integrating various enabling technologies, such as augmented and virtual reality (AR/VR), High-Performance Computing, Cloud and Fog Computing, Cyber-physical production systems, Big Data Analytics, ultrafast communication infrastructure and standards, AI, data archiving, sensors and monitoring, wearable devices and actuator technologies (Tolio, 2019).

More specifically, in the Italian context—characterized by many small- and medium-sized enterprises (SME) facing challenges in adapting to the digital transformation of products, processes and technologies—manufacturing companies will have to address complex production phenomena using solutions based on expertise, wherein the operator may play a central role. Consequently, it is equally crucial to strategically invest in these enabling technologies to support user-centred activities (e.g. operator training and maintenance support through feedback and visual, auditory and tactile interactions) together with the development of digital twin models incorporating appropriate semantics and ontological representations of information and knowledge in support of the reuse of expertise (Terkaj, 2024).

This scenario offers opportunities to SMEs that market products globally, especially durable assets and capital goods, ensuring their capability for remote maintenance and assistance. The IoA approach is also founded on the possibility of operating physically from anywhere worldwide while centralizing product and process knowledge in specific locations, with Italy potentially being one of these.

The expected impact of the scenario on R&D activities is intertwined with the ongoing development of effective knowledge management systems, which, in the short term, require an enhanced ability to interact with operators and consumers. Significant investments need to be strategically scheduled in the medium to long term, particularly in the fields of sensors, actuators and cybersecurity.

This scenario should be analysed considering the following action lines:


# **6 Scenario 5: Digital Economy**

In the contemporary economic landscape, which is increasingly interconnected and complex, businesses operate on a global scale whereby digital platforms enable consumers and enterprises to virtually interact and share information, meet customer needs, and improve their ability to manage company processes. Therefore, stakeholders in the digital platform economy can swiftly create new products and services that cater for consumer preferences and habits. They can also create entirely new offerings by reconfiguring innovative products and services based on information acquired through these platforms (McKinsey, 2020). KPMG estimates that turnover linked to the platform economy will go from 7 thousand billion dollars made in 2018 to over 60 thousand billion by 2025 (KPMG, 2019).

In business models founded on the platform concept, the sale of products and services is integrated—in a transparent way for the user—thanks to the provision of value-added services (shared design, product configuration, maintenance, insurance, after-sales support), offering modular solutions, which leverage the combined offering of different operators who are often in different areas of business, but who are seen as a single entity by the company client/end consumer.

Notably, the progressive implementation of the platform economy through digital technologies (such as sensor-based systems, Internet of Things, Big Data, AI) has been one of the distinctive traits of companies that have thrived during the crisis. On this note, several hi-tech companies have exploited digital platforms as a core part of their business model to go from design to rapid remote prototyping, to handling maintenance and product sales, and have managed to meet market demands even when the presence of operators on site could not be guaranteed.

Similarly, in e-commerce, major retailers have exploited digital platforms—even platforms employing different data management models (some more centralized and rigid, others open and flexible to handle their processes)—to handle the abovementioned processes.

Digital platforms supporting manufacturing processes must be conceived to provide a "digital" extension of functionalities to physical assets (Effra, 2020). In particular, services provided through digital platforms can aim to support:


• Interoperability of decentralized processes and production systems.

These services can be offered by different providers in a multi-actor ecosystem, supporting processes both inside and outside the factory. The preconditions for improving digital platform development in Italian manufacturing contexts include the definition of agreements between providers, as well as between providers and manufacturers, on industrial communication interfaces and protocols, common data models and data interoperability and hence, on a broader scale, intercommunication and interoperability between platforms, ensuring an open approach.

This kind of model still has a margin for expansion in Italy and there are several challenges still to be tackled to study open solutions supporting process management, especially at the manufacturing system level. The opportunity to develop solutions that are federated between different providers is also promoted by the European Commission through the funding of various European projects (Gaia, 2022), and Italy's role is of paramount importance as an advocate for the interests of companies whose task, as either manufacturers or users of machinery, is to bring to light the user requirements of these platforms.

Some of the aspects that need to be worked on over the coming years to improve the digital economy for the manufacturing sector are:


Platform-Based Manufacturing, then, clearly needs to focus not just on handling logistics, but also on the actual transformation processes. This completely changes supply chains and the way businesses deal with customers. Hence, new actors (managing the platforms) are expected to rise, while existing actors change their functions and how they operate. A radical change offering new market opportunities may be key for machinery manufacturers. In addition, these platforms are quick to go global, posing questions around country-level industrial strategy.

The expected impact of the scenario on R&D activities is related to the fact that digital platforms have a powerful short-term impact improving communication and collaboration across different decision levels in manufacturing. Indeed, they urge for investments in R&D in the medium term to support SMEs and technology providers in developing and implementing open solutions that facilitate communication between different actors.

This scenario should be studied from the point of view of the following action lines:

LI7: Digital platforms, modelling, AI, security.

# **7 Scenario 6: Climate Change**

The global warming observed over the past 150 years is probably triggered by human activities, hence resulting in an anthropogenic greenhouse effect that adds to the natural greenhouse effect. With the industrial revolution, humankind has suddenly dumped millions of tonnes of carbon dioxide and other greenhouse gases into the atmosphere, causing a huge upsurge in the amount of CO2 in the atmosphere (Enel, 2021).

The average temperature of the planet has increased by 1 degree on pre-industrial levels and, judging from the trend observed from 2000 to date, if no action is taken, this value is forecast to be as high as +1.5 °C by 2050. "Fire seasons" have become longer and more intense; from 1990 to date, every year there has been an increase in extreme weather events, such as cyclones and flooding, which are even hitting outside the periods of the year that would typically experience this kind of event in the past, and with increasingly devastating consequences (Enel, 2021).

The greatest damage is being caused, above all, by the consumption of coal, oil and gas, which are responsible for most greenhouse gases. According to McKinsey's Global Energy Perspective 2019, fossil energy sources in 2019 accounted for 83% of total CO2 emissions, while coal-fired electricity generation alone accounted for 36%26, although in 2020—as a result of the Covid-19 pandemic—emissions then plummeted (McKinsey, 2019).

It has been estimated that the current rate of CO2 emissions from coal combustion is responsible for about one third of the 1 °C increase in annual average temperatures on pre-industrial levels, making it the number one source of emissions in human history. Oil is the second biggest source of emissions by far, having produced 12.54 billion tonnes of CO2 in 2019 (86% of that produced by coal).

There is also a political issue surrounding fossil fuel at the moment, since the recent tensions over Ukraine could put 155 billion cubic metres a year of natural gas imports into Europe at risk—tensions due to Russia cutting supplies—amounting to 30% of Western Europe's annual gas demand (Rystad, 2020).

European gas markets have been plunged into great instability, gas stocks are at a five-year low, and international prices are highly volatile. These are just some data showing how important it is to define development scenarios based not just on renewable energy—wind, solar (thermal and photovoltaic), hydro, geothermal and biomass power—as a fundamental alternative to fossil fuels.

Their use enables a reduction not just in greenhouse gas emissions resulting from the generation and consumption of power, but also in our dependence on fossil fuel imports (especially gas and oil), hence improving Italy's position from the standpoint of energy resource procurement, a crucial point for the economy in general and an essential factor in manufacturing when it comes to competitiveness.

To achieve the ambitious EU-defined goal of renewable energy accounting for a 20% share of its energy mix, efforts thus also need to be stepped up in energy-related sectors in terms of production systems and innovative products. More specifically, some of the open challenges for manufacturing concern:


Italy should invest in systems of this kind as they boost research excellence spanning from the laboratories of public bodies and universities to the industrial sphere. This will change the economy of whole regions, prompting the rise of new manufacturing hubs linked to new energy sources and more efficient use of energy. Moreover, a larger amount of green energy will be produced and supplied, which will probably see a scaling back of the strategic roles of some players in the supply of energy, such as the Middle East and Russia.

The expected impact of the scenario on R&D activities is predominantly linked to the necessity for investments in both structural and technological aspects, with a medium to long term impact.

This scenario should be studied from the point of view of the following action lines:


# **References**


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Strategic Action Line LI1: Personalised Production**

**Marina Monti, Ferdinando Auricchio, Filippo E. Ciarapica, Antonello Ghignone, and Rosanna Fornasiero**

**Abstract** The objective of this chapter is to describe the action line related to Personalised production (LI1). In particular, this chapter proposes research and innovation priorities aimed at promoting industrial systems and models for the efficient manufacture of customized products that can be reconfigured with fast turnarounds to meet specific requests fielded from individual customers or small groups, and that deliver a high level of integration with the customers in order to ensure they become the main actors of the resulting solution. These design and production systems must be conceived to have the capacity to be reconfigured for the manufacture of products that can be required in certain times of emergency (such as health emergencies) or in response to events that can cause a sudden shift in system priorities and require the industrial system to transfer its focus to different categories of products to those usually made. In this action line, it is important to research new supply chain management models and local manufacturing models as well as smart materials.

**Keywords** Personalised production · Design · Configuration · Supply chain · Smart materials

M. Monti (B) CNR-IMATI, Genova, Italy e-mail: marina.monti@ge.imati.cnr.it

F. Auricchio Università Di Pavia, Pavia, Italy e-mail: auricchio@unipv.it

F. E. Ciarapica Università Politecnica Delle Marche, Ancona, Italy e-mail: f.e.ciarapica@staff.univpm.it

A. Ghignone Vibram, Milano, Italy e-mail: Antonello.Ghignone@vibram.com

R. Fornasiero CNR-IEIIT, Padova, Italy e-mail: rosanna.fornasiero@cnr.it

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_5

# **1 Introduction**

In recent years, the capability to provide consumers and clients with personalised products that meet their specific needs on a large scale, thanks to high levels of flexibility in the production system, has emerged as one of the strategies that can enable to differentiate business offer through high value-added innovative products (Seitz et al., 2020; Aheleroff et al., 2019; Martinelli and Tunisini, 2019). With regard to consumer goods (clothing, footwear, sports items, eyewear, etc.) an approach based on a high level of customisation (i.e. personalisation) helps emphasizing the strength of *Made in Italy* since it provides consumers with a product that combines design and style, with advantages in terms of both functions and comfort (Macchion et al., 2019; He et al., 2022). Other relevant sectors such as the medical one (with personalised prostheses in the orthopaedic, dental and other fields), and production of durable goods in general (in industrial design, automotive and manufacturing sectors overall) can benefit from this approach by seizing the opportunities and challenges arising from a growing demand, both at European level and worldwide, for products that differ in value, functionality and performance (Trenfield et al., 2019; Siiskonen et al. 2020).

The objective of this strategic action line is to study and develop industrial systems and models for an efficient production of personalised products to meet the specific requirements of individual clients and ensure a high degree of integration that can make the clients themselves active players of the developed solutions.

Such design and production systems should be design and developed in a reconfigurable way in order to manufacture specific products needed at times of particular emergency (such as health emergencies) or in the aftermath of disruptive events that can suddenly change the system's priorities and require the industrial system to reposition on product ranges other than the usual (Fig. 1).

To this end, specific models and tools will need to be developed, using technologies that are versatile from both a manufacturing point of view (such as additive manufacturing, micro-processing), and digital point of view (such as artificial intelligence, IoT, Big Data), to exploit the knowledge of the specific context through an integrated approach to the design, production, validation and management of products and services (such as design tools that can promptly generate design alternatives).

Priority research and innovation topics depend on the different development aspects and include digital solutions for the acquisition of customers' requirements, product configurators, advanced measurement systems, consumer-monitoring platforms, and innovative customised-production technologies, such as additive manufacturing, micro-manufacturing, hybrid processes, etc. New flexible and agile supply chain models are also needed, to take into account product modularization, postponement and "multi decoupling-point" strategies with a view to personalisation.

Prerequisites for personalised production systems are cyber security, privacy protection and the availability of platforms for data storage and traceability of information. The digital architectures shall be designed to foster the advancement of

**Fig. 1** Strategic Action Line 1—Personalised production

interoperability standards and to promote the integration of the different IT systems available in the production and distribution chain.

The solutions identified should be geared towards eco-compatibility criteria, with a view to controlling environmental impact, like in mass production (in particular, for consumer goods such as footwear, clothing, etc.), fostering production systems to reduce processing and manufacturing waste generation to a minimum.

*Expected impact*: increasing offer of personalised solutions in various manufacturing sectors; improvement of end product quality; improved matching of consumer needs and proposed solutions; increase in the efficiency and adaptability of customized production systems; optimization of customization processes through an improved control of waste levels and use of resources (such as set up times, production queues, scraps); management of logistics processes with control of procurement and production lead times along the entire supply chain thanks to the interoperability and transparency of processes; models and tools for decentralized production; reduction in the cost of the personalised product/service (compared to the current cost of manufacturing a personalised product).

# **2 PRI1.1: Advanced Tools for Configuration and Design of Personalised Solutions**

Customers can be effectively integrated into the production chain through the identification and correct interpretation of their individual requirements during the design phase. The tools must be targeted at gathering customers' requirements and correctly using them to deliver an optimized concept and design for individual and specific products (Tan et al., 2022; Hara et al., 2019). There is, therefore, a primary need for tools that enable customers to communicate their ideas and/or requirements in a simple way, without demanding specific knowledge and/or posing cultural and linguistic restrictions. The ability to orchestrate properly the innovation phase of a product or service in a multidisciplinary environment involving many players that interact in a virtual way becomes of fundamental importance (Reinhart et al., 2010). Furthermore, the growing demand for possible product variants increases the importance for companies to predict the impact of design choices on all aspects of their production cycle (e.g. product functionality, manufacturability and production costs), distribution, use and disposal/recycling. The ability to exploit the specific knowledge of the context in which a product will be used thanks to artificial intelligence technologies becomes fundamental for the creation of efficient tools, in particular for those types of products created to meet specific requirements that can impact on the functionality of the product itself (e.g. bio-medical products).

The goals of this research and innovation priority are therefore:


expressing or detecting in a natural and easy way their specific requirements. In particular, these solutions should be based on:


## **Interaction with Other Strategic Action Lines**


# **Time Horizon**

Short-term goals (2–3 years):


Medium-term goals (4–6 years):


# **3 PRI1.2: Solutions for the Efficient Production of High Value Personalised Products**

Personalised products require modular, flexible and adaptable production systems, i.e. adapting and reconfiguring themselves according to the features required from time to time by the customer, without losing efficiency and product quality, in accordance with the emerging production paradigms of "Zero-waste and Zero Defect manufacturing", focusing on product quality and efficient use of resources.

Production systems heading in this direction already exist but the amount of alternatives required by personalisation is constantly increasing and must ensure that resources can be used efficiently and adapted to such needs. A high level of customisation (personalisation) also requires production phases to be synchronized with the product design, and with the logistics for handling the parts, so that information can be moved from one phase to another in a flexible and reliable manner (Medini et al., 2019). It is therefore necessary to study new reconfigurable systems and new plugand-produce devices capable of guaranteeing a rapid response to frequent changes in customers' requirements and unit batches as well (Plasch et al., 2012).

These new paradigms carry along a strong integration between production and logistics systems at shop floor level. New digital models, algorithms and self-adaptive autonomous technologies need to be developed to ensure real-time planning and control of reconfigurable production and logistics systems, reducing reconfiguration and downtime (Medini et al., 2019; Keiningham et al., 2020; Zhang et al., 2019).

The goals of this research and innovation priority are:


#### **Interaction with Other Strategic Action Lines**

LI4—High efficiency integrated systems: a reconfigurable internal production and logistics system must guarantee high efficiency even in contexts of high level of product personalisation.

LI5—Innovative production processes: reconfigurability and high flexibility production systems can be achieved through intelligent machines and handling systems.

LI6 and LI7—An efficient production of personalised and high value-added products can be obtained by controlling reconfigurable production systems, Digital Twin solutions, implementation of Human Robot Co-working and applications of AI algorithms.

#### **Time Horizon**

Short-term goals (2–3 years) that are pursued starting from existing technologies:


Medium-term goals (4–6 years) that involve significant development:


Long-term goals (7–10 years) that require the integration of all the technologies developed as a result of a research and innovation priority:

• Digital Twin platforms for customized and resilient productions.

# **4 PRI1.3: Advanced solutions for customer-driven production management**

From a technological point of view, it is essential that demand-driven production is synchronized with the customer order management, with scheduling and production, through a coordinated management of material and information flows. It is also necessary to coordinate production with internal and external logistics through appropriate models for integrating information that comes from different sources.

In order to achieve these objectives, new management systems must be developed, based on technologies such as Big Data Analytics, Artificial Intelligence and decision-supporting models geared to increase the companies' ability to manage and use large amounts of data from different sources (customer, suppliers, social networks) to allow better production management, dynamic supply and distribution networks.

The use of big data technology also allows the activation of blockchain processes that guarantee the integrity of the transferred data. In addition, greater interoperability, transparency and autonomy in the product life cycle through the use of resources and value-added services is desirable.

In this context, goals of this research and innovation priority are:


#### **Interaction with Other Strategic Action Lines**

LI4—Production planning and management require high-efficiency integrated systems, especially where considerable product personalisation is required.

LI5—The management of internal and external logistics to ensure reconfigurability and flexibility needs innovative and "smart" production processes.

LI6 and LI7—Digital Twin solutions and AI algorithm applications can ensure efficient management of the supply chain of high value-added personalised products.

#### **Time Horizon**

Short-term goals (2–3 years) that are pursued starting from existing technologies:


Medium-term goals (4–6 years) that entail significant development:

• Greater extension of End-to-End Engineering through the design of systems for the reconfigurability of a product's dynamic requirements.

Long-term goals (7–10 years) that require the integration of all the technologies developed as a result of the research and innovation priority:


# **5 PRI1.4: Mini-Factories in the Production and Distribution Chain of Personalised Products**

Small-scale distributed production becomes increasingly important in different sectors and in different situations (such as in a health crisis when it is necessary to quickly produce basic necessities with limitations deriving, for example, from the closure of national and regional borders) and is based on the structuring of production facilities with very fast set-up and decommissioning times and easily transferable to different locations (real mini-plants, fab-labs, production in containers).

It is thus possible to ensure that part of the production and in particular the manufacturing of personalised parts/components is postponed to the last mile and carried out near the place of delivery and use of the objects.

It is therefore necessary to define new organizational models based, in accordance with the *urban manufacturing* paradigm, on the creation of laboratories and mini-factories equipped with advanced machinery that support the production of personalized products quickly and at low cost. The new organizational model must include the use of technology highly reconfigurable and adaptable to the specific context and the revision of the collaboration model upstream with suppliers and downstream with users to redefine the flow of operations.

The possibility of operating locally (neighbourhood, municipality, region) with dedicated mini-factories, in addition to reducing logistics costs, can extend the scope of application of "customized" recycling technologies that would otherwise be too expensive and thus meet to the growing demand for customization in the repair/reuse of end-of-life products.

The mini-factory model can constitute the connection between (Do-it-yourself) makers and industrial companies and give rise to new functionalities and innovative production methods (new processes, new machines, low-cost ideas, etc.).

The goals of this research and innovation priority are therefore:


#### **Interaction with Other Strategic Action Lines**

This priority is closely linked to the LI5 line of intervention and in particular to the development of low cost and high productivity multimaterial additive technologies (for local "real time" production of personalised products/components).

#### **Time Horizon**

Short-term goals (2–3 years) that are pursued starting from existing technologies:


Medium-term goals (4–6 years) that entail significant development:


Long-term goals (7–10 years) that require the integration of all the technologies developed as a result of the research and innovation priority:

• Integration in a single supply chain of the four previous objectives.

# **6 PRI1.5: Production Systems of Smart Materials for Product/service Personalisation**

This research and innovation priority focuses on technologies and processes for the production of innovative and intelligent materials (e.g. sensorized fabrics, display materials, micro- and nano-materials, multifunctional fabrics, materials for biomedical use, high-performance renewable materials) that can produce in line with the specific consumer needs or can perform a function based on the adaptive capacity of the material itself (i.e. that can work as sensors by capturing changes in parameters and at the same time as actuators by performing an action). To achieve this goal, the new production systems should produce components made of homogeneous materials (to be easily recyclable and based on the intrinsic active properties of the material, such as shape memory materials, photosensitive, magnetostrictive materials, etc.) or components with engineered morphological structures (lattice structures, multiscale, with gradient, micro-kinematics, etc.) or composite structures or materials.

The ability to integrate a device with sensor capabilities that can equip it with intrinsic intelligence is also of utmost importance. Examples include Lab-on-chip with integrated biosensors for precision medicine and mechanical devices that can monitor external parameters related to the work environment or the device's internal parameters (such as fatigue, critical situations of dysfunctionality, etc.).

The goals of this research and innovation priority are therefore:


#### **Interaction with Other Strategic Action Lines**

Connection with the PRI5.5 of action line LI5, which deals with the production and manufacturing processes of innovative materials.

#### **Time Horizon**

Short-term goals:

• Development of design tools for engineered morphological structures;

Medium-term goals:


Medium-long term goals:

• Development of innovative production technologies for the manufacturing of bioengineered Lab-on-chip micro-devices with integrated biosensors for precision medicine.

# **References**


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Strategic Action Line LI2: Industrial Sustainability**

**Melissa DeMartini, Marcello Colledani, Nicoletta Picone, Grazia Barberio, and Mauro Parrini**

**Abstract** Over the past 20 years, sustainability has become a central issue on the manufacturing and political agenda, and it has recently grown in importance in light of increasingly powerful and devastating climate events. In this chapter, a strategic action line to support companies is proposed to implement industrial sustainability (LI2) by means of strategies, methods and tools to implement sustainable production processes at an environmental, economic and social level, reducing dependence on the external supply of critical production resources or on resources penalized by the laws in force. Priority research actions proposed concern new solutions to reduce noxious or polluting emissions from production processes; methods and techniques for strategic product-process evaluation from a Life-Cycle-Thinking perspective; technologies and processes for the reuse, re-manufacturing and recycling of products, components and materials from used products or maintenance processes; systems and methods for measuring and implementing Sustainable Supply Chains and Closed-Loop Supply Chains.

**Keywords** Industrial sustainability · Design · Raw materials · Circular economy

M. Colledani Politecnico Di Milano, Milan, Italy

N. Picone COBAT SPA, Roma, Italy

G. Barberio ENEA, Bologna, Italy

M. Parrini Simonelli Group, Belforte del Chienti, Italy

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_6

M. DeMartini (B) EADA Business School, Barcelona, Spain e-mail: m.demartini@eada.edu

# **1 Introduction**

Over the past 20 years, sustainability has become a central issue on the manufacturing and political agenda, and has recently grown in importance in light of increasingly powerful and devastating climate events. Against this background, industrial sustainability plays a fundamental role in responding to environmental, social, and economic challenges and transforming the Italian manufacturing sector.

Not only can the industry reduce its environmental impact, but it can also manufacture products that, on the one hand, solve various environmental problems and, on the other, have a limited impact during their life cycle.

This requires raising awareness on the need to transform industrial processes and conceive new products with a view to a circular economy to significantly reduce carbon emissions and improve energy efficiency, reduce, and rationalize water consumption, foster and promote resource recovery. In a circular economy, there are two types of material flows: biological, suitable to re-enter the biosphere, and technical, intended to be reused without entering the biosphere. In line with this vision, all the activities carried out in the industrial system, starting from extraction and production, must be organized in such a way that the waste produced by one sector can, after appropriate transformations, become a resource for others. In addition to material recovery, recovery of a product's functions is also very promising, as it makes it possible not only to recycle raw materials but also to avoid losing the value of the activities carried out to transform the material into a product. These changes require the introduction of new processes, new machines, and new systems, creating an in-depth review of the domestic production base, and laying the foundations for new capital goods markets in which Italy may assume a leadership role.

These systems should be consistent with the evolution of markets and enabling technologies, using technology as a competitive lever with respect to the three dimensions of sustainability (economic, environmental, and social). In this new perspective, the role of the manufacturing industry is fundamental towards the implementation of a circular factory concept. The manufacturer can design products that can be disassembled after use, integrating an increasingly larger fraction of secondary raw materials. In addition, it can manage product information along the value chain with a view to improving the efficiency of component and material recovery after the use phase, thus increasing value for money in reusing them.

In this context, the issue of "de-and re-remanufacturing" is gaining ground, because of the increase in the cost of raw materials and the specific laws introduced by the European Union, which require an improvement in the recovery rate of materials. Furthermore, critical raw materials and primary resources are increasingly scarce (e.g., water) or more expensive (e.g., energy), and their current use levels are no longer sustainable. The demand for critical raw materials in the manufacturing of high-tech products is constantly increasing in Europe, their procurement causing major economic and strategic problems. It is therefore necessary to study how to use electronic waste as a source of rare materials for new technologically advanced products (Fig. 1).

**Fig. 1** Strategic Action Line 2 -Industrial Sustainability

The Importance of a rational use of energy and water resources that are essential for various production processes should also be emphasized, through the promotion of practices aimed at an efficient use of these resources, reduction of consumption, reuse, and optimization of residual flows with a view to closing the cycles and recovering resources, for example from thermal flows, wastewater and sludge generated from their treatment.

In Italy, this line of intervention is aimed at the study and development of strategies, methods, and tools to implement more sustainable production processes at an environmental, economic and social level, reducing dependence on the external supply of critical production resources or on resources penalized by the laws in force. Priority research actions in this sector mainly concern new solutions to reduce noxious or polluting emissions from production processes; methods and techniques for strategic product-process evaluation from a Life-Cycle-Thinking perspective; technologies and processes for the reuse, re-manufacturing and recycling of products, components and materials from used products or maintenance processes; systems and methods for measuring and implementing "Sustainable Supply Chains" or "Closed-Loop Supply Chains".

To encourage the change described above, it is therefore necessary to:


• activate company-level experimentation through new business models and improve the ability of the industrial sector to systematically act towards the implementation of a circular economy.

**Expected impact of the strategic action line:** minimization of the environmental impact of manufacturing with specific focus on increasing the efficiency of natural and energy resources; control and minimization of the environmental footprint at the level of the entire product/process/system life cycle; creation of value in cross-sectoral supply chains; improved ability to recover secondary raw materials and recover the functionality of products; improved recovery and valorisation of waste; improved ability to recover products and transform waste into inputs thanks to industrial symbiosis models.

The research and innovation priorities of the strategic action line on Industrial Sustainability are:

PRI2.1—Design and development from a life-cycle thinking perspective.

PRI2.2—Monitoring of the environmental footprint of products.

PRI2.3—Systems for secondary raw materials.

PRI2.4—Technologies, processes and tools for the reuse, re-manufacturing and recycling of products, components, and materials.

PRI2.5—Modelling and simulation for the Sustainable supply chain.

PRI2.6—Models and tools for "Circular Economy".

# **2 PRI2.1. Design and Development from a Life-Cycle-Thinking Perspective**

Product complexity has increased in several respects, due to the expanding use of innovative materials, materials with key functions and combinations of different types of components. The technological difficulty of separating the various product components limits the development of circular economy strategies, which involve repairing, updating, and remanufacturing to prevent the waste of the precious resources contained in those products.

In this context, the design and development of products from a life-cycle-thinking perspective is one of the key issues of circular economy.

Therefore, it is of strategic importance to consider, from as early as a product's design phase, the use of recycled materials or components that can be reused after their first use phase, in line with the business alternatives offered by circular economy. The goals of this research and innovation priority are related to:

1. Tools for the analysis and design of product functions from an eco-design, designfor-environment (DFE) perspective to develop innovative products with a view to exploiting the multifunctionality of a product and its components. Applying principles of Eco-design, Design for Environment (DFE) will help design sustainable products that use fewer resources, materials, components and that can be reused or re-manufactured after their end of life;


# **Interaction with Other Strategic Action Lines**

PRI2.4—Technologies, processes and tools for the reuse, de- and remanufacturing and recycling of products, components and materials.

#### **Time Horizon**

Short-term goals (2–3 years):

• use of simplified LCA, LCECA, LCC tools to design products according to the principles of eco-design and circular economy.

Medium-term goals (4–6 years):


Long-term goals (7–10 years):

• design solutions for upgradeable products through multiple use cycles to respond to evolving market requirements.

# **3 PRI2.2. Monitoring of the Environmental Footprint of Products and Processes**

The identification and monitoring of the environmental footprint of products and processes are fundamental in providing with a choice-evaluation element the stakeholders involved in a product life cycle (like consumers, entrepreneurs, and policy makers), especially in view of the ambitious strategies and objectives at European level for the next few years (e.g., "Energy Roadmap 2050", "2030 EU Climate and Energy framework"). One of the problems consists in the unavailability of a complete, uniform, updated and available data set for the entire industrial sector and the complexity of modelling particular processes to monitor all its phases. The goals of this research and innovation priority can be grouped in different categories:


#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short-term goals (2–3 years):

• Configuration of sensorized systems to support the monitoring and control of the environmental impact from a greenfield and brownfield perspective.

Medium-term goals (4–6 years):


# **4 PRI2.3 Systems for Secondary Raw Materials**

Today, Secondary Raw Materials (SRMs) are few, scarcely available and tend to be more expensive than "traditional" raw materials. This lack of choice, availability and price competitiveness is the first major obstacle to their diffusion and their use on an industrial scale. Furthermore, the properties and volumes of SRMs are often difficult to predict, poorly repeatable and not suitable for large-scale industrial applications. To date, no competition is possible on large numbers with traditional raw materials, due to their scarce availability, competitiveness, and performance repeatability. Most of the industrial efforts conducted to date have focused on scale economies and the optimization of production processes that assume an enormous availability of basic raw materials of the same quality over time.

The goals of this research and Innovation priority are related to:

• **Production systems for SRMs:** these systems should help increase production in terms of flow stability, quantity, quality, competitiveness (i.e., price/performance ratio) and their use in high value product manufacturing. There is not as much of a need to replace the raw materials' production processes currently in use as to support them with new, more flexible, robust, and controlled processes that guarantee repeatable outputs and quality levels in compliance with the specifications even if they contain SRMs, and as to use tools to promote industrial symbiosis processes for a simple and systematic exchange of resources. This challenge therefore requires thinking in terms of integration about the characteristics of the products, of the production processes that transform the mix of raw and secondary materials into finished products, and of the systems that must implement these processes. The most advanced zero-defect manufacturing and industry 4.0 techniques can be extremely useful in providing current manufacturing systems with the soundness and flexibility required for this purpose.


#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short-term goals (2–3 years):

• Integration of SRMs in high value-added products.

Medium-term goals (4–6 years):


Long-term goals (7–10 years):

• Study of new integrated product-process-system schemes in order to support the implementation of a large-scale manufacturer-centric and repeatable circular economy model.

# **5 PRI2.4 Technologies, Processes and Tools for the Reuse, De- and Re-Manufacturing and Recycling of Products, Components and Materials**

Complex products, consisting of several materials significantly different from each other (for example metals and polymers) are particularly difficult to recycle through mechanical processes, unless one is prepared to forgo the properties of individual materials and significantly downgrade them. The technical difficulty of separating constituent materials or the excessive cost of doing so suggests a different approach for the management of these types of products/materials.

In metal recycling, the greatest criticalities are observed with respect to precious metals and metals defined as critical. The system for closing the processing cycle according to the principles of Circular Economy is still incomplete for these metals, unlike what happens for ferrous and non-ferrous metals, which can count on a consolidated supply chain. In fact, the infrastructures for the recovery and purification of these materials from industrial by-products and waste and from technological waste (e.g. lithium batteries, permanent magnets, WEEE, red sludge) are still very limited in Italy. The state of the art is that hydrometallurgical technologies are the most suitable to pursue these objectives, for which investments are necessary in order to open new branches of research.

The objectives of this research and innovation priority focus on de- and remanufacturing processes for the recovery of functionality and/or intrinsic value of materials from end-of-life products, by-products, and industrial waste. To this effect, the following actions have been identified:


optical multi-sensor systems, the use of robotics solutions, including collaborative ones, the use of multi-physical separation processes, in synergy with modern collection techniques, modelling, data analysis and control deriving from industry 4.0, for example the use of feed-forward control systems and cyber-physical systems, can be extremely efficient in complex context;


#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short-Term Goals (2–3 years):


Medium-Term Goals (4–6 years):


Long-Term Goals (7–10 years):

• Designing production and de-production systems jointly, with a view to a closedloop reuse.

# **6 PRI2.5 Modelling and Simulation for the Sustainable Supply Chain**

Industrial sustainability is a complex concept that requires the combination of different players (companies, institutions, governments, etc.), it impacts different sectors and presents a non-linear and non-rational behaviour. To describe these interactions at best, one needs to devise an approach that combines both the bottomup (industrial and business dimensions) and the top-down (political) perspectives. Traditional approaches (such as the General Equilibrium Model, the Input–Output analysis) cannot fully grasp these dynamics. For this reason, the approaches developed in recent years start with the theory of Complex Adaptive Systems (CAS) and are based on a combination of different approaches, giving rise to hybrid models that allow for a responsible management, from a social, environmental, and economic point of view, of all procurement, production, and distribution processes.

Sustainable supply chains can exploit the benefits generated by hybrid modelling approaches to support decisions, achieve quality, efficiency and productivity goals and solve specific sustainability problems such as those pointed out in the Sustainable Development Goals (United Nations, 2015). When it comes to selecting or designing a sustainable supply chain, hybrid models are considered particularly appropriate for the way they manage flexibility in their analyses.

The goals of this research and Innovation priority are:

	- o Analyse the dynamics and impacts of sustainable business models on supply chains (including the analysis of the industrial symbiosis potential);
	- o Evaluate the impact of government laws and incentives that can influence the behaviour of supply chains from a sustainability point of view;
	- o Provide sustainability metrics to support decision making;

#### **Interaction with Other Strategic Action Lines**

LI5: Innovative production processes. LI6: Evolutionary and resilient production. LI7: AI, Digital Platforms, Cyber-Security.

#### **Time Horizon**

Short-term goals:

• Hybrid approaches for sustainable supply chain management.

Medium-term objectives:


# **7 PRI2.6 Models and Tools for the "Circular Economy"**

Circular economy is emerging as an economic rather than a purely environmental strategy that requires not only a change in production processes, but also in value creation activities, and consumption. This research and innovation priority aims to promote the development and implementation of tools and models for supporting circular economy, by reducing demand for resources and raw materials, increasing the value of scraps and waste, and arranging for their recovery and reuse. The main goals of this research and innovation priority are:


#### **Interaction with Other Strategic Action Lines**

LI15: Processi produttivi innovativi.

#### **Time Horizon**

Short-term goals:


Medium-term objectives:

• Models for the management of discrete products in a circular economy perspective.

Management models for industrial symbiosis.

**Acknowledgements** The authors would like to thank Flavio Scrucca e Roberta De Carolis from ENEA for their support and contribution during the discussion and collection of materials that have brought to the definition of the chapter.

# **References**


REACH, http://reach.sviluppoeconomico.gov.it/


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Strategic Action Line LI3: Factories for Humans**

**Paolo Dondo, Michele Viscardi, Mauro Viscardi, Fabrizio Cardinali, Paolo Chiabert, Tiziana D'Orazio, Claudio Melchiorri, Marcello Pellicciari, Margherita Peruzzini, and Nadia Scandelli**

**Abstract** The objective of this chapter is to describe the strategic action line related to the factories for humans (LI3). In particular, this chapter proposes research and innovation priorities aimed at designing and developing new solutions to enhance the role of human resources and their skills, and contribute to their satisfaction and wellbeing; research and experimentation of new technologies for reducing physical exertion, cooperation with advanced support systems, with collaborative robots and with AI-powered technologies; mapping of knowledge generated on the job, especially implicit knowledge, in a way that is compatible with privacy requirements, introducing advantages both on the human wellbeing front—whether the individuals are users, operators or managers—and in terms of business strategies and procedures. In this regard, innovative factories will need to be increasingly inclusive, strongly geared towards the engagement and participation of individuals (users, operators and managers). These models must take a human-centric approach to look into/

P. Dondo (B) MESAP, Torino, Italy e-mail: p.dondo@advsor.mesap.it

M. Viscardi · M. Viscardi COSBERG, Bergamo, Italy

F. Cardinali MYWAI Srl, Sestri Levante, Italy

P. Chiabert Politecnico Di Torino, Torino, Italy

T. D'Orazio CNR-STIIMA, Milan, Italy

C. Melchiorri Università Di Bologna, Bologna, Italy

M. Pellicciari · M. Peruzzini Università Di Modena E Reggio Emilia, Modena, Italy

N. Scandelli Cefriel, Milan, Italy

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_7

101

investigate new technologies and all the dimensions through which the new factory is defined.

**Keywords** Workers · Human–machine interaction · Skills and competences · Training · Safety and ergonomics

# **1 Introduction**

In the coming years, exponential growth in connected IoT devices is expected. It will include consumer, industrial and healthcare applications, with a market forecast of between \$4 and \$11 trillion by 2025, around 1600 billion in Industrial IoT (*Source* McKinsey). Machines will be increasingly pervasive, intelligent, connected, and equipped with forms of distributed intelligence that will interface with users.

Ongoing technological changes call for the definition of socially sustainable digital innovation pathways make technology work at the service of humans (workers, citizens, students, or else) and for the development of a human-centred society. According to a 2018 study by the McKinsey Global Institute, in urban contexts, digitization has contributed to raising various quality-of-life indicators from 10 to 30%, in areas ranging from transport and healthcare to the reduction of the pollution generated by manufacturing.

In particular, according to a recent Microsoft/IDC study, about 85% of jobs are expected to undergo a transformation in the next 3 years, 33% of workers will have to engage in re-skilling, 26% will take on new roles, 27% of works will be outsourced or automated.<sup>1</sup> Skills will undergo essential changes accordingly. A ranking of the skills necessary to use artificial intelligence shows that technical, cognitive, process and social skills will need improvement. New future professions include, among others, data scientists, data engineers, data analysts and the like.2

The new document of the European Commission on Industry 5.0 focuses on the crucial role of people, in industrial contexts, on data, information and knowledge management. Industry 5.0 presents a business model based on cooperation between machines and humans, which implies rethinking and innovating models and tools for managing information, as the current ones are often inadequate to deal with the complexity of present socio-technological environments. Particularly so in fact, as the evolution of information technology has made new developments possible.

In the new scenarios, the debate on the role of people in factories is spurred by a wealth of new ideas. This makes it increasingly urgent to study new models that can enable people to improve their work and have a leading role in the evolution of production processes and in the introduction of systems for the exploitation

<sup>1</sup> Microsoft (2018). Brave New World - How AI Will Impact Work and Jobs. Retrieved from https:// news.microsoft.com/en-hk/2018/05/02/brave-new-world-how-ai-will-impact-work-and-jobs/

<sup>2</sup> Deloitte (2018). Global Human Capital Trends. Retrieved from: https://www2.deloitte.com/us/en/ insights/focus/human-capital-trends/2018.html.

of artificial intelligence. All these studies have to consider the diversities of the workers' cognitive and physical skills. Artificial intelligence and future technologies will increase the workers' skills and knowledge, providing physical help in dealing with the heaviest and riskiest jobs and decision-making support.

However, knowledge generated during the job (particularly tacit knowledge) must be mapped before it can be re-used in the company in a way that meets the privacy requirements of the factory and the individuals. All this requires an intensive effort from both a technological and an organizational point of view.

The continuous introduction of new technologies and management practices calls for new skills and knowledge that should be developed through training for people wishing to enter the job market and through continuous education programs for those already in employment.

This action line aims to design and develop new solutions to enhance individuals and their skills, thus contributing to their satisfaction and well-being. Innovative factories should be increasingly inclusive and firmly focused on the involvement and participation of people (users, operators, managers). These models should be humancentric. Humans run and control technologies and, in general, all the dimensions through which the new factory is defined.

The most relevant challenges certainly include the creation of safe and comfortable workplaces capable of generating positive emotions in the people involved in the production, and workstations that reduce the workers' physical and cognitive effort and help them move on to activities with greater value added. In the new factories, the design of spaces, workstations, new facilities architectures, and new models for the safe interaction between humans and machines will become central aspects. Particular attention will be paid to improving human interaction with an environment populated by various technologies/systems such as robots, machinery, interfaces, and automation systems.

The study and experimentation of new technologies for reducing physical effort can be sparked by exploiting the potential of wearable devices and exoskeletons. The cognitive effort could be eased by intelligible and straightforward information, profiled according to the worker's needs.

Similarly, the role of workers is shifting from traditional repetitive to higher-level tasks thanks to the cooperation with advanced support systems, collaborative robots, and AI-based technologies. On the one hand, the objective will be to provide all the material and cognitive support tools that can improve workers' skills and knowledge and, on the other, generate new training procedures and tools to help workers be up-to-date and keep step with the changes in manufacturing processes.

Finally, the knowledge generated on the job, particularly tacit knowledge, will be mapped and reused within the company to be compatible with individual privacy. This will bring benefits both in terms of the well-being of people, whether users, operator or managers, and in terms of company policies and procedures.

*Expected impact*: increasing workplace safety and well-being; reducing psychophysical stress; increasing qualification opportunities and promoting the personal enhancement of workers, preserving them from exclusion and downgrading; improving the transparency of the algorithms that regulate the operation of digital

**Fig. 1** Strategic Action Line 3—Factories for humans

platforms; improving the capacity of companies and workers to exploit the advantages of the data economy to ensure, in compliance with ethical-legal limitations and individual freedom, the achievement of collective goals in terms of the workers' health and well-being in the factory.

The research and innovation priorities of the strategic action line on Factories for humans are (Fig. 1):


# **2 PRI3.1 New Technologies, Methods and Tools for Optimizing the Work Environment, Human–machine Interactions and Cognitive Load**

People play a central role in designing intelligent factories and, above all, in generating their flexibility and "resilience". Factories can thus adapt to new and not entirely predictable contexts and reconfigure promptly and efficient according to new work patterns and production models.

This research and innovation priority proposes to design new technologies and methods for managing human resources as a critical element in the new concept of "resilient factory". In particular, this priority proposes objectives connected with ergonomics, human–machine interaction, and the person's cognitive load, promoting new ways of working and communicating (remote work, collaboration with automation systems, and management of production re-purposing).

The proposal is to develop methods, models, and systems to support operators in their work, increase their physical and cognitive abilities, improve work quality and productivity, and reduce margins of error while ensuring the workers' general well-being. As a result, safety and well-being in the workplace will be increased by acting on numerous factors: an adequate redesign of the type and sequence of activities during work shifts, better organization of the premises, information display mode optimized according to needs and activities, natural and intuitive interactions between humans and machines). This will reduce psycho-physical stress, mental and psychological fatigue, anxiety, and overload.

One of the fundamental themes for the future will be to guarantee a continuous and rapid evolution of production systems to seize new opportunities and to face ongoing changes. Humans are at the centre of a constant redevelopment that requires analytical skills, goal identification, and creation of solutions and planning of pathways to achieve the desired results. This shift has to take place at all levels, from the local improvement of individual workstations, geared to increase efficiency and respond to new needs in terms of products and materials, seizing new opportunities in terms of tools, sensors and plants, to factory networks, supply chains and interaction with the markets. To be effective, this change must be pervasive and see the joint effort of all the people who take part in this evolution at different levels. The ability to manage this evolution, rather than the optimization of individual situations, will in all likelihood be the real competitive lever in a rapidly and radically changing context such as the current one. Humans are the critical resource, as they can use their intelligence and dedication to ensure high performance and competitiveness for their company. In this perspective, it is important to underline that people must be put in a position of dominating changes. The rapid and continuous evolution of technological products and production systems should parallel the ongoing and targeted development of the knowledge and skills of the people who cooperate to manage change at different levels.

In particular, the goals of this research and innovation priority concern:


cognitive skills of individual workers, considering their role, duties, abilities and needs; developments of intelligent solutions designed to recognize operators and to reconfigure the working environment based on working conditions.


#### **Interaction with Other Action Lines**

This research and innovation priority is transversal and will have strong interaction with action lines LI5 and LI6.


#### **Time Horizon**

Short-Term Goals (2–3 years):


Medium-Term Goals (4–6 years):


Long-term goals (7–10 years):

• Tools to bolster the adaptation to continuous changes in production processes (intelligent systems capable of predicting the operator's actions and offering online support, and advanced Virtual Training systems to train operators to deal with changes)

# **3 PRI3.2 New Approaches to the Management of Corporate Knowledge, Privacy and Human Capital**

Knowledge, creativity and the human capability to cope with unforeseen events play a fundamental role in generating innovation and managing production. Competitiveness involves acquiring awareness of these aspects and enhancing the human component and its ongoing development. As machines become increasingly intelligent and connected (Smart Machines), new business models will lead the way from a product economy to a product-service economy (Servitization) also in manufacturing. Prognostics and remote configuration of the new "connected products" will be the basis for this change. Knowledge is increasingly important and remotely available thanks to new digital tools.

Furthermore, emerging production models provide for the pervasive introduction of AI-based tools, which involves the creation of knowledge management and decision-making support tools, as well as smart machines that work together with humans. In this scenario, it is necessary to keep the focus on the central role and skills of people means promoting the trustworthiness and explainability of AI methodologies, with a view to ensuring that AI-based systems comply with the ethical principles of respect for people, prevention of damage, fairness and explicability.

The research and innovation priority must therefore address the following objectives:

• **Methods for managing information, privacy of people and factories:** Define rules and methodologies to collect, manage, harmonize and share information related to workers and factories. They should ensure an appropriate acquisition and use of data with a particular focus on sensitive data Privacy and Security. This area includes: CyberSecurity (i.e. IT equipment safety and security), anonymization of data both at data generation and data processing level, certification of data from its generation all along the management and analysis process chains. The issue of the workers' self-sovereignty with regard to their digital identities (Self Sovreign Identity) is also very important.


providing them with the necessary decision-making elements. It is expected that in these systems, the "human factor" will be at the centre of the decision-making process.

• **Systems for the management of Knowledge IP:** it is essential to define methods, infrastructures, guidelines and application tools to ensure that the ownership of knowledge is available and valued, as both a corporate and a personal asset, in a perspective of trust and enhancement of the ideas of those who generated them and of those who provided the tools to generate them.

# **Interaction with Other Strategic Action Lines**


# **Time Horizon**

Short-term goals (2–3 years):


Medium-term goals (4–6 years):

• **Advanced Interactions:** The definition and development of IoT, AR and smart wearable technologies, for monitoring and supporting workers, can be addressed through actions in the short-medium term.

Long-Term Goals (7–10 years):

• **Advanced Interactions:** Natural multi-modal interfaces, capable of allowing intelligent and self-adaptive interaction with the industrial environment and robots, and convergence of AI, CBI and Exoskeleton systems should instead be addressed in the medium-long term, given the complexity of the context.

# **4 PRI3.3 New Technologies and Methods to train and certify the skills and competence of individuals and of a company's human capital in the era of Life Long Learning (LLL)**

The constant introduction of new technologies and new management practices in companies is creating a lack of coordination between demand for new professional profiles, the qualification of operators active on the labour market. In Italy, the difference between supply and demand amounts to more than 150,000 jobs available within companies that are not matched on the labour market (2019 data).

In addition, the knowledge acquired by the current workforce during their educational career becomes obsolete quickly and continuous training is required to generate added value for the company. Accordingly, there is a demand for new skills and knowledge in the offer of training programs for people entering the job market, and of updating programs for those already in employment.

From a company's point of view, it becomes essential to map the workforce's competence and skills to understand proactively the impact of a new technology, the timeframe to its full exploitation as added value and the most effective training methods.

From an individual's point of view, the need is for virtual and tangible tools and environments to be constantly available, to ensure a continuous education and the certification of the skills and knowledge acquired, in a Life Long Learning (LLL) perspective. New teaching frames must be used to increase the power of attraction of the manufacturing world and the efficacy of training.

New tools should be developed also to provide user support functions, to manage knowledge and training for classes of both younger and more experienced users, to develop/improve high-profile skills in manufacturing.

Finally, it is essential to develop tools to assess the work carried out and test the skills and abilities acquired, so that the new strategies and technologies introduced can be properly validated from a corporate point of view, the effects of the training received can be assessed, and objective tools can be provided for the certification of the skills and abilities acquired on the job.

The research and innovation priority must therefore address the following objectives:

1. **New methods and tools for active training**: the availability of new hardware devices such as smartphones, tablets, smart glasses, combined with the prompt availability of protocols with enormous bitrates (5G) and the use of software such as social media and APPs pave the way to the interaction with digital twins of machinery, lines and production environments. The focus is on strengthening MOOCs (Massive Open Online Courses) and experiential tools such as Mixed, Augmented and Virtual Reality, alongside innovative methodologies and new educational models such as "serious games", to further enable training both in presence and at a distance.


# **Interaction with Other Strategic Action Lines**

• With reference to LI6, digitization/digital twin's issues will be useful in defining tools for sharing and managing knowledge within the digital factory.

# **Time Horizon**

Short-term goal (2–3 years):


Medium-term goal (4–6 years):


# **References**


Manufuture—STRATEGIC RESEARCH AND INNOVATION AGENDA 2030.


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Strategic Action Line LI4: High Efficiency and Zero Defect**

**Marcello Urgo, Vittorio Rampa, Renato Cotti Piccinelli, Marco Sortino, and Pierluigi Petrali**

**Abstract** The objective of this chapter is to describe the strategic action line related to high efficiency and zero defect production (LI4). In particular, this chapter proposes research and innovation priorities aimed at studying models for efficiency in terms of: zero-defect technologies designed to reduce non-conformances, monitoring processes during the various phases like quality management, maintenance and internal logistics of a manufacturing system, upgrading and improving the capacity of equipment and industrial goods; robustness/flexibility as the capacity to face disruptions, due to the precarious supply of incoming materials and parts, and to the specific properties of the material (anisotropy, low rigidity, etc.); smart systems for optimized use of available resources (equipment, human operator, knowledge) and for the control and management of production systems through models (CPS, empirical models, etc.).

**Keywords** High efficiency · Zero defect · Advanced control · Maintenance · Artificial intelligence

M. Urgo (B) Politecnico Di Milano, Milan, Italy e-mail: marcello.urgo@polimi.it

V. Rampa CNR-IEIIT, Milan, Italy

R. Cotti Piccinelli Streparava, Adro, (BS), Italy

M. Sortino Università Di Udine, Udine, Italy

P. Petrali DIH-Lombardia, Milan, Italy

# **1 Introduction**

Efficiency is the ability to reduce the effort associated with the achievement of a goal, optimizing the use of resources, materials and time. Quality plays a significant role in terms of efficiency in achieving the expected results, as it permits to avoid rework and waste of products and materials. Efficiency is an enabler of company competitiveness, since the ability to work efficiently in complex and variable conditions determines the possibility to operate in more demanding and competitive areas, such as product customization, adoption of new technologies, enabling of remanufacturing activities. Finally, the growing complexity of production systems also requires an efficient use of resources in a more general way. In this perspective, the efficient use of available machinery and equipment, the ability to exploit available knowledge and take advantage of advanced digital tools and artificial intelligence towards the implementation of next generation production systems (Fig. 1).

The goals of this strategic action line fall into three groups:


**Fig. 1** Strategic Action Line—High-efficiency and zero defect

Furthermore, the expansion of the equipment's scope of application is explored also in terms of the possibility to operate multiple technologies at the same time and the use of robots for a wider range of applications.

• **Intelligent systems.**Efficiency is considered in terms of optimized use of available resources (equipment, human operator, knowledge). It is necessary to consider approaches and methodologies for the control and management of production systems through models (CPS, empirical models, etc.) and approaches that exploit artificial intelligence, to establish an efficient collaboration between human operators and automatic tools, as well as approaches and methodologies for the consolidation of knowledge.

The research and innovation priorities of the strategic action line on High Efficiency and zero defect are:

PRI4.1: Advanced monitoring and control of production processes (zero defects) PRI4.2: Approaches for an integrated quality/maintenance/logistics management (zero defects) PRI4.3: Updating, retrofitting and valorisation of capital goods (zero defects) PRI4.4: High efficiency for repair remanufacturing (robustness/flexibility) PIR4.5: Advanced industrial robot modelling and planning (robustness/flexibility) PRI4.6: Cyber-physical systems (CPS) for smart factories (intelligent systems) PRI4.7: Human-artificial intelligence for knowledge consolidation and humanmachine cooperation in high-efficiency production systems (intelligent systems) PRI4.8: Advanced production, planning and scheduling (intelligent systems)

# **2 PRI4.1 Monitoring and Advanced Control of Production Processes (Zero Defect)**

Monitoring and control contribute to process efficiency, as all the elements of Overall Equipment Efficiency (OEE)—i.e. Availability, Performance and Quality—require accurate and precise measurement in order to:


Furthermore, the exploitation of sensor systems in combination with standard methods for collecting, filtering and archiving data will provide an unprecedented source of data, useful in understanding complex production phenomena and scenarios.

The goals of this research and innovation priority are related to:

• Improving data management through the adoption of standard ontologies, communication protocols and open software that can be easily reused.


The expected impacts from the introduction of advanced monitoring and control solutions will allow manufacturing companies to improve process efficiency from all three perspectives:


The integration of process and product parameters will encourage a holistic approach to the optimization of production processes, and promote waste reduction, a more energy efficient production and lower CO2 emissions.

#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short-term goals (2–3 years):

• Development of standard ontologies and communication protocols.


Medium-term goals (4–6 years):


Long-term goals (7–10 years):


# **3 PRI4.2 Approaches for Integrated Quality/Maintenance/ Logistics Management (Zero Defect)**

Digital tools, together with formalized knowledge and data, offer the possibility to implement complex approaches to quality management, taking into account the wide range of factors that influence product quality, and improve efficiency of the production system as a whole.

These factors include: the control of production processes, the management and supply of materials and components, the maintenance of production assets and their updated performance, the logistics of the whole system and the interconnection of the different aspects and actors to determine their performance.

Building on knowledge and data, and exploiting integrated models based on quality, logistics and maintenance factors, the focus of this research and innovation priority is on methodologies and approaches aimed at improving the overall efficiency of production systems, in terms of productivity, qualitative characteristics of the products, use of resources, maintenance policies, etc.

The selected approaches should cover a wide range of products, processes and resources. For example, large products for which transportation, inspection and processing are specifically difficult. The considered approaches should be robust, including in the modelling the intrinsic uncertainty of real production systems, and any changes in decisions and planning when unexpected events occur, to mitigate the impact of such events on the systems' overall performance.

The main goals of this research and innovation priority cover the following areas:


#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short to medium term goals (2–6 years):

• Models and approaches for an integrated management of decisions related to quality, maintenance and logistics, considering that a substantial amount of partial research results and embryonic products are already available.

Long-term goals (7–10 years):

• Methodologies and tools for identifying quality problems in complex products, since the main gap with respect to the state of the art is the absence of standard reference models (ontologies, definition of semantic data), while a secondary gap is the development of unattended machine learning approaches applied to complex systems (Cyber-Physical Systems of Systems-CPSoS).

# **4 PRI4.3 Updating, Retrofitting and Enhancement of Durable Equipment (Zero Defects)**

A substantial portion of the durable equipment of manufacturing companies (for example, machine tools, assembly systems, etc.) has been designed to have a significantly long life, in many cases approximately 20–30 years. However, the recent rapid and radical evolution of production systems has accelerated the obsolescence of a large part of it, mainly because of the impossibility to integrate it with digital management and control infrastructures, rather than on its actual process capacity.

Therefore, retrofit and updating geared to the integration of modern digital functionalities into operating but dated durable equipment are extremely significant, and represent a sustainable, valid and effective approach for the management and updating of industrial equipment in terms of I4.0 technologies.

The goals of this research and innovation priority cover the following areas:


standards to regulate and allow the modelling of the characteristics, functionalities and capabilities of industrial equipment in a general and unique way. Furthermore, the assessment of the operational and technological capacity of industrial equipment is influenced by multiple intersecting factors. Therefore, to ensure a coherent and uniform set of management and control tools, it is important to design and develop specific approaches as a basis to more general approaches for the evaluation and certification of performance in the different situations (architectures, machine characteristics, control approaches, etc.)

#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short to medium term goals (2–6 yeas):


Medium term goals (4–6 years):

• Toolbox for updating and retrofitting industrial assets.

# **5 PRI4.4. High Efficiency for Repair and Remanufacturing (Robustness/Flexibility)**

The transition to circular production models requires, from a technological point of view, processes, technologies, skills and capital goods for the maintenance, repair, updating and reworking of products and their components. Therefore, not only the production, but also the repair and regeneration of products require plants in which to operate. These plants must be able to work in collaboration with the supply chains of the original production plants, to manage the entire life cycle of the products.

This research and innovation priority arises from the need for durable equipment and production facilities to regenerate, repair and recycle products and components. The focus is on highly efficient technologies and approaches that can partially carry out processes and/or repeat a limited and/or alternative set of operations to obtain compliant/degraded and reclassified products. Furthermore, these processes will be expected to deal effectively and efficiently with the variability of incoming products, which is typical of reworked products that come from different kinds of uses.

The goals of this research and innovation priority concern in particular:


#### **Interaction with Other Strategic Action Lines**

In relation to the strategic action lines of the CFI roadmap:


# **Time Horizon**

Short-term goals (2–3 years):


Medium-term goals (4–6 years):


Long-term goals (7–10 years):


# **6 PRI4.5. Advanced Industrial Robot Modelling and Planning (Robustness/Flexibility)**

The planning and control of industrial robots is essential in ensuring a safe, effective and reliable use of robots in applications other than those in which they are commonly used.

The objectives of this research and innovation priority mainly concern:


structure. It is necessary to develop modelling techniques that can estimate the forces generated between the end-effector (tool) and the parts being machined, implementing appropriate control techniques to compensate actively for deviations from the ideal trajectory, rather than relying on the inherent rigidity that lightweight structures such as robots cannot provide.

• **Specific programming approaches capable of bypassing the need for a definition of specific trajectories,** focusing decisions on the characteristics of the process to be implemented and avoiding the difficulties associated with the adoption of robots in manufacturing processes. It is necessary to study advanced software that can support the programmer in defining the operations to be performed by the robot. These approaches will be based on features such as learning by examples, assisted and simplified programming, and the ability to modify and reconfigure the operations assigned to a robot in a simple and reliable way.

Safe and effective collaboration between robots and operators has strong impacts in terms of ergonomics and workers' well-being, as it contributes to relieve people of heavy and/or tiring tasks. At the same time, it can boost systems' performance in terms of flexibility, by calibrating workloads (for both operators and robots) depending on the volume and characteristics of the products.

Extending the use of robots to the execution of technological operations can significantly increase the flexibility of production systems. The processes involved include finishing operations (such as polishing, grinding, deburring, etc.), in addition to those concerning the removal of materials.

Greater ease in the implementation and reconfiguration of processes assisted or performed by robots would have a significant impact in terms of the diffusion of robots in manufacturing industries and their competitiveness in terms of cost, quality and flexibility.

#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short-medium-term goals (3–6 years):


Medium-long term goals (4–10 years):

• Use of robots instead of machine tools.

# **7 PRI4.6 Cyber-Physical Systems (CPS) for Smart Factories (Intelligent Systems)**

The flexibility and reconfigurability of production systems require modular and intelligent architectures, as well as monitoring and controlling of logistics and system quality, compliance with process constraints and safety of man–machine interactions.

Traditional hierarchical control techniques rely on predefined configurations and statically-designed decision platforms, which do not provide the required degree of flexibility, adaptability and efficiency.

Furthermore, the behaviour of a production system is usually modelled as a chain of actions within a purely temporal domain defined in terms of events. The interaction with the underlying processes requires approaches based on continuous-time control or, alternatively, discrete-time control but with better temporal resolution. These two levels are not considered jointly, thus constituting a barrier to the overall optimization of the system's behaviour.

It is therefore essential to develop an integrated and distributed platform for monitoring, control and supervision. It should consist of intelligent and interacting units, based on Cyber-Physical Systems (CPS) and a hybrid paradigm, to consider simultaneously different temporal domains (discrete and continuous events) related to the modelling of the behaviour of a production system, at different levels.

This class of approaches is applied for example to modular robotic cells in scalable production systems, where adaptation to external conditions and to the processes to be carried out is fundamental. In those cases, coordinating the intelligent components that operate in a production system, and integrating them into management and control platforms are key factors in governing the network of complex interactions between physical, software, robotic and human components, human–robot interaction and human–robot collaboration.

Using CPS approaches for machines and machine-systems reveals new possibilities that go beyond current control approaches, evolving towards the possibility of automatic adaptive performance improvement, in terms of efficiency and safety (also in human–machine interaction) of the whole system at different levels.

The availability of a Digital Twin for a physical system determines the possibility of developing predictive control algorithms based on the updated status of the plant, as well as the possibility of using AI methodologies based on the available data.

CPS architectures of individual production units, based on the 5C paradigm (Connection, Conversion, Cyber, Cognition, Configuration), should therefore evolve towards a new 6C paradigm, where the additional level would be the Cooperation between factory objects. It is therefore necessary to develop an integrated level of control where intelligent agents can integrate and cooperate towards the collection of information and data, and the management and optimization of performance.

This level's key factors are techniques of Massive Data Acquisition, Data Analytics and Machine Learning, geared to create and integrate the cognitive, selfconfiguration and cooperation levels of intelligent CPS units, to drive flexibility, high performance, and efficiency.

#### **Interaction with Other Strategic Action Lines**


# **Time Horizon**

Short-term goals (2–3 years):


Medium term goals (4–6 years):


Long-term goals (7–10 years):


# **8 PRI4.7 Human-Artificial Intelligence for Knowledge Consolidation and Human–Machine Cooperation in High-Efficiency Production Systems (Intelligent Systems)**

The challenges faced by production systems and technologies require the ability to combine the adaptability and flexibility of human intelligence, which can efficiently handle unexpected and evolving production scenarios, with the capabilities of artificial intelligence, which can process large amounts of data in real time and manage complex situations.

In this context, some emerging issues can be identified:


This research and innovation priority applies to the following areas:

• Structured and formalized approaches (for example, ontologies and semantic-web technologies) for the representation of knowledge related to production processes and systems. These approaches must be suitable to support interfaces for access and use by both human and automatic actors. This will require the ability to operate on the partial/incremental structuring of knowledge due to missing information and data, to sequential or incomplete formalization and structuring processes, to human operator errors.


New developments in this scientific and technological field are important for the competitiveness and efficiency of manufacturing companies in relation to future competitive scenarios. The formalization and consolidation of corporate knowledge bring out and capitalize on the experience and skills of operators. In combination with the support provided by artificial intelligence approaches, they are important elements in preserving and profiting from the know-how of companies. These factors are strategic in future manufacturing scenarios, characterized by the pervasive adoption of digital technologies, the need to interact and collaborate globally while preserving the intellectual assets of companies, and the drive towards platform-based collaboration paradigms.

# **Interaction with Other Strategic Action Lines**

• LI3: Involvement of human operators in the process of structuring and consolidating their experience and knowledge.

# **Time Horizon**


# **9 PRI4.8 Advanced Production Planning and Scheduling (Intelligent Systems)**

The growing complexity of modern production systems requires advanced planning approaches to exploit the most of their features and functionality. The objectives of this research and innovation priority cover the following areas:

• Production planning and scheduling based on artificial intelligence. Approaches related to the latest developments in artificial intelligence (e.g. deep learning), as well as more traditional technologies (e.g. expert systems) can play a supporting role in the planning and programming of production systems. These approaches can eventually lead to an identification of the system's current state by expressly monitoring parts, components, state of production resources, through the analysis of tracing data and/or images relating to the production system. They can also help identify the state of the system based on pattern recognition approaches; select planning rules/policies based on historical data, identify system behaviour deviations from the plans and determine new planning.


#### **Interaction with Other Strategic Action Lines**

• LI1: Planning and scheduling approaches can support the implementation of personalized production approaches within the limits of the available capacity of production resources.


#### **Time Horizon**

Short-medium term goals (2–6 years).


**Acknowledgements** The authors would like to thank Gianantonio Susto from the University of Padova and Roberto Zuffada from Siemens for their support and contribution during the discussion and collection of materials that have brought to the definition of the chapter.

# **References**

AFIL Roadmap per la Ricerca e l'Innovazione sull'Economia Circolare, 2020.


Yin, Y. H., Nee, A. Y., Ong, S. K., Zhu, J. Y., Gu, P. H., & Chen, L. J. (2015). Automating design with intelligent human–machine integration. *CIRP Annals, 64*(2), 655–677.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Strategic Action Line LI5: Innovative Production Processes**

**Luca Giorleo, Carmen Galassi, Francesco Ziprani, Paolo Calefati, Gianni Campatelli, Roberto Munaro, and Gianluca Trotta**

**Abstract** The objective of this chapter is to describe the strategic action line related to innovative production processes (LI5). In particular, this chapter proposes research and innovation priorities across various aspects both related to conventional and nonconventional processes, such as: digitization of conventional production processes in order to improve their interactions and handle different types of processing, even by means of hybrid processes; the growing role of additive manufacturing and its ensuing challenges in terms of both design and production; processing of standard and innovative materials, or materials with meso/macro geometries, including also nano- and micro-manufacturing. In addition, process innovation also needs to take the shape of innovation in support of re- and de-manufacturing processes, to start with, through to the development of bio-inspired transformation models.

**Keywords** Smart materials · Bio-intelligent manufacturing · Consolidated manufacturing · Micro-nano processes · Additive manufacturing

L. Giorleo (B)

Università Di Brescia, Brescia, Italy e-mail: luca.giorleo@unibs.it

C. Galassi CNR-ISTEC, Faenza, Italy

F. Ziprani Marposs Spa, Bentivoglio, BO, Italy

P. Calefati Prima Power, Torino, Italy

G. Campatelli Università Di Firenze, Firenze, Italy

R. Munaro Cembre, Brescia, Italy

G. Trotta CNR-STIIMA, Bari, Italy

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_9

# **1 Introduction**

Manufacturing processes must be the focus of innovation in manufacturing, since they influence the competitiveness of the country-system and provide capital assets for the domestic market or for exportation. This improves global performance in terms of efficiency, sustainability, reconfiguration, flexibility and resilience.

In particular, Italy should become a leader in building manufacturing facilities to be able to address various types of needs, including: management of a large variety of products at different stages of the life cycle; regeneration, reuse, repair of products, components, materials; increase in process efficiency to manufacture high-value and highly complex products.

Research and innovation to promote the digitization of consolidated manufacturing processes have come to the forefront in recent years, but there is still scope for further developments, as consolidated processes continue to represent a large portion of manufacturing processes as a whole.

At the same time, it is essential to study the development of innovative processes with a view adapting them to industrial contexts and improving the interactions that make different types of processes (hybrid processes) manageable. These improvements should focus on a manufacturing system that processes both standard and innovative materials, as well as meso/macro geometries even on a nano or micro scale. Process innovation has always focused on transformation processes for production, and should now support first of all re- and de-manufacturing processes, and the development of transformation models inspired by biological systems.

Against this background, this strategic action line aims to define research and innovation priorities for the development of innovative manufacturing processes that can help the manufacturing system implement the necessary transformation to meet the social and technological challenges illustrated below (Fig. 1):

**Process innovation**: in order to remain competitive, conventional processes will need to implement digital transformation in combination with technological improvement. This involves adopting systems that can develop solutions to improve productivity, flexibility and sustainability, including through real time process control solutions and process management by way of adaptive control systems. In the short term, these technologies will be just marginally replaced by alternative technologies, as they achieve high surface finishes and geometric accuracy. However, the growing demand for increasingly complex products with lower production volumes is leading to the development of unconventional technologies such as Additive Manufacturing, laser, micromachining, electro -physical and chemical processes and innovative assembly processes, as well as hybrid processes consisting in a combination with traditional technologies, which needs to overcome lead times, volume and cost limits in order to compete in terms of efficiency with traditional processes.

**Materials innovation**: innovation in materials should be geared towards greater durability, environmental sustainability, possible reuse/recycling, zero/low CO2 emissions. Manufacturing processes should produce and use these innovative materials in typically industrial volumes and time. The concept of generative design is

**Fig. 1** Strategic Action Line 5—Innovative production processses

also important. Generative design involves the integration of the properties of these materials in the manufacturing cycle of a component right from the design stage, by developing databases and software tools that include material, process, product performance and life cycle modifications to enhance the advanced modeling and characterization tools of the manufacturing process.

**New production paradigms such as biological transformation**: a new concept of manufacturing inspired by biological systems found in nature is currently spreading at European level (Byrne et al., 2018). Biological and bio-inspired principles, materials, functions, structures and resources are expected to be increasingly used and integrated in manufacturing, in order to obtain intelligent and sustainable technologies and manufacturing systems (Cainelli et al., 2020).

Four research priorities have been dedicated to the first paradigm. They focus on the innovation of consolidated processes, processes for additive manufacturing, hybrid processes and micro processes. Distinct research priorities have been dedicated to the second and third paradigms, which are in fact cross-topical.

*Expected impact*: innovation of conventional and non-conventional processes; reduction and reuse of waste materials through innovative processes; increased flexibility and resilience of manufacturing processes; increased performance in terms of volume of work and productivity of innovative processes; reduction of energy consumption; redesign of the manufacturing cycle of a component based on the choice of new materials, new geometries and new manufacturing processes; integration between consolidated and innovative manufacturing processes, improvement of the tools necessary for the simulation of manufacturing processes; reduction of set up and cycle times with shorter time-to-market; improvement of manufacturing processes' sustainability.

The research and innovation priorities of the strategic action line on Innnovation Production Processes are:


# **2 PRI5.1 Technologies, Processes and Materials for Additive Manufacturing**

On a domestic front, Additive Manufacturing (AM) has been recognized as one of the enabling technologies of the Industry 4.0 plan defined by the Minister of Economic Development, because of the benefits it offers in terms of strategic aspects such as design digitalization, supply chain transformation and high flexibility and freedom in the manufacturing of high-value innovative products. In addition, there is a growing interest on the part of domestic, European and international industry for the development of AM technologies to manufacture metal components and components reinforced with polymeric matrix fibers. In these areas there has been a rapid and important technological development from systems limited to rapid prototyping, to systems that can support small series manufacturing of functional components and final parts.

Despite its robust growth (SmarTech Analysis estimated in 2019 a global additive manufacturing market grow over 10.4 billion dollars) AM is still not fully mature to be implemented in a manufacturing system due to its yet limited manufacturing times and volumes. In particular, the most consolidated processes for manufacturing metal parts (PBF—Powder Bed Fusion and DED—Direct Energy Deposition) are still showing considerable limits in terms of time, printable materials, high defect rate and eecomplex post process operations for the removal of supports, as mentioned by Gartner Hype Cycle 2019, which places these processes in the "Sliding into the Trough" phase. As it is mainly achieved through Material Extrusion processes, also the manufacturing of reinforced components experiences high lead times and the deposition of customizable fibers only on the plane orthogonal to the printing direction. Finally, it should be stressed that, out of habit, the geometry of a component is still currently designed in accordance with traditional processes' rules, without a full exploitation of AM's intrinsic ability to manage complex geometric shapes.

For AM to become an industrially viable technology, the research and innovation priority should set the following goals:

• **New design rules**: make the most of the AM design potential by combining the concepts of generative design, topological optimization, conformal geometries, meta and multi materials, hierarchical and functional complexity using numerical modeling software to test their performance.


The expected benefits will be considerable: reducing waste materials and energy consumption compared to traditional technologies will improve the environmental impact of manufacturing; furthermore, new, more sustainable products can be introduced on the market if existing components and assemblies are redesigned with a view to reducing their weight and keeping the same performance. The reduction in set-up and cycle times will lead to a reduction in the time-to-market of small series, allowing Italian companies to be competitive on the global market.

# **Interaction with Other Action Lines**


# **Time Horizon**

Short-term goals (2–3 years):


Medium-term goals (4–6 years):


Long-term goals (7–10 years):

• **Solutions for Multi-material AM**: at present, this topic is strongly growing in terms of scientific research but it is not yet ready for an effective fallout in the industrial world as the main AM process (DED) is being developed as reported by Gartner's Hype Cycle. In the coming years it will be necessary to analyze and optimize the aspects related to dimensional accuracy, thermal stress, the chemical and physical affinity obtainable by mixing different materials.

# **3 PRI5.2 Bio-Inspired Technologies and Manufacturing Processes**

Biological transformation, i.e. the systematic application of biological knowledge in improving manufacturing, is expected to be one of the next technological leaps in manufacturing processes. From a manufacturing point of view, an increase is expected in the use and integration of biological and bio-inspired principles, materials, functions, structures and resources to obtain intelligent and sustainable technologies and manufacturing systems. Biological transformation processes can develop in three separate steps: inspiration, integration, interaction.

In the first step, inspiration, biological phenomena will be translated in the design of products (for instance lighter structures), in their functionality (for instance biomechanics), in the organizational solutions (for instance swarm intelligence, neural networks). In the second step, knowledge of biology will be applied to obtain a real integration of biological systems into manufacturing systems (e.g. replacement of chemical processes with biological processes such as the use of microorganisms for the extraction of rare-earth elements from magnets). The third step will finally see a global interaction between manufacturing, information and biological systems, leading to the creation of completely new and self-sufficient technologies and production structures or the so-called bio intelligent production systems.

The impact of biological transformation in manufacturing will lead in the long term to a continuous improvement in innovation and sustainability for manufacturing processes. A systematic two-way approach would lead to new manufacturing developments, innovations and new products. It would be driven (top down) by technology and industry or (bottom up) by biology. This systematic approach should be based on the various manufacturing processes and on the different biological elements. Full potential can only be reached by combining the various strategies with data collection, digitization and the development of new processes such as additive manufacturing technologies.

However, these paradigms are still at basic research levels to date. Therefore, with a view to their integration into the manufacturing world, this research and innovation priority has the following goals:


#### **Interaction with Other Action Lines**


• The concept of biological inspiration will be applied at various levels in current business situations, from process innovation to system innovation (LI 4, LI 6) and it will finally change the rules of next generation manufacturing management systems (LI 7).

# **Time Horizon**

Medium-term goals (4–6 years):


Long-term goals (7–10 years):

• **Biological interaction and integration in manufacturing processes**: the transition to the design of bio-inspired processes should take place incrementally by adopting various biological solutions in the medium-long term.

# **4 PRI5.3 Innovation of Consolidated Manufacturing Processes**

The increasing demand for customized products, with lower environmental impact and prompt and quick response to customer needs, is radically changing the organization of manufacturing systems (A graphical method for performance mapping of machines & milling tools). Despite the current evolution of innovative technologies such as additive manufacturing, chip removal will for years to come retain its role as primary technology (Agubra et al., 2016) in the higher value sectors of the Italian supply chains, together with foundry, plastic deformation and sheet metal processing (cutting, welding and bending) because they can obtain excellent surface finishes and high geometric accuracy. For example, it is expected that some sectors, such as e-mobility, will push towards an ever greater use of machine tools, given that powertrain components require high levels of precision (AMFG—The Additive Manufacturing Landscape, 2020).

In order to remain competitive, consolidated processes will have to undergo a digital transformation associated with technological growth through the adoption of systems capable of developing solutions that can improve productivity, flexibility and sustainability, also by adopting solutions for real-time process control and its management by adaptive control systems (Arias-Rosales; Armendia et al., 2019; Ii & La Bioeconomia in Italia).

The goals associated with this research and innovation priority are listed below:


need for process planning models designed to meet several goals, such as the costeffectiveness and low environmental impact of machining processes, and that can produce alternative machining sequences one can choose from during the manufacturing of products, depending on the conditions of each specific manufacturing plant.


#### **Interaction with Other Action Lines**


# **Time Horizon**

Short-term goals (2–3 years):

• **Autonomous solutions for real-time optimization of productivity and process quality**—These solutions should not be considered only at individual machining process level. Instead, it is necessary to develop rules and logics at machine and manufacturing cell level.

Medium-term goals (4–6 years):


Medium-long term objectives (5–10 years).


# **5 PRI5.4 Manufacturing Processes Using Hybrid Technologies**

Developing production systems to support customization is a goal of the transition to Industry 4.0 at national and international level (A graphical method for performance mapping of machines & milling tools; Agubra et al., 2016). This goal can be achieved by developing flexible, productive solutions that can process the new materials available on the market. Thanks to research and technological development, many conventional and unconventional manufacturing processes are now available for a highly efficient machining of traditional and innovative materials. The combination and integration of these processes to create a hybrid production system is a fundamental step to drive further improvements in process efficiency and greater flexibility. Hybrid manufacturing processes are based on a controlled interaction of several processes during the same machining procedure. These processes have different energy sources, tools and process parameters (AMFG—The Additive Manufacturing Landscape, 2020). This integration can help obtain improved performance, i.e. better machining of materials and less friction, and achieve high levels of flexibility by promptly alternating multiple technologies within the same manufacturing process. This can already be seen in the additive-subtractive hybrid machines recently launched on the market (Arias-Rosales).

The integration of different technologies poses challenges at design and process management level. It will be necessary not only to identify the optimal solutions for hybridization, pinpointing groups of technologies that can lead to concrete benefits thanks to a thorough integration, but also to develop solutions and approaches for an efficient use of hybrid technologies, whether in terms of guidelines for redesigning components and work cycles or in terms of software for constant exchange of information between integrated processes (Manufuture 2030; Armendia et al., 2019).

These systems should allow companies to reduce manufacturing cycle times and manufacturing cycle setup times, significantly increasing production flexibility. The introduction of new multi-materials makes it possible to obtain products with better physical–mechanical characteristics than current solutions. Furthermore, an increase in productivity is expected thanks to the support of multiple technologies/energy sources. Processes will be faster and cheaper and material waste will be contained by adopting technologies that can reduce the geometric constraints of current solutions (e.g.: additive–subtractive hybrid solutions).

The goal of this research and innovation priority is to study and develop advanced hybrid solutions and in particular:


• **Combination of traditional and micro-scale machining** in an integrated machine that guarantees an improvement in cycle times (i.e. time required to move the component from one station to another) and component quality (no repositioning).

# **Interaction with Other Action Lines**


# **Time Horizon**

Short-term goals (2–3 years):


Medium-term goals (4–6 years):


# **6 PRI5.5. Manufacturing and Machining Processes for Innovative Materials**

The European strategy towards circular economy (waste elimination) implies that materials for 500 Mt/year could be re-injected into the economic system. Therefore, materials innovation will be oriented towards greater sustainability in terms of durability, environmental sustainability, the reusing/recycling possibility, zero/low CO2 emissions. The replacement of metals with polymers is already a reality thanks to injection processes for the manufacturing of gears, levers, pulleys, for example in food packaging. Innovative design is already proposing materials obtained from completely natural raw materials (bio-based and biodegradable packaging), such as "plastics" from algae. Organic alternatives such as bamboo, mushrooms and wheat straw are already being used in place of traditional oil and plastic-based packaging.

Innovation in materials requires guaranteed performance, to be implemented in terms of multifunctionality and diversification of application areas, because of the improved performance (for example of composites) with respect to the new geometries/architectures resulting from the application of lightening, miniaturization and hybridization.

The use of materials can be improved from as early as the combined product/ material design phase, by developing databases and software tools that include material, process, product performance and life cycle changes, by enhancing modeling tools and advanced characterization (generative design). (Arias-Rosales; Armendia et al., 2019) The development of new materials (light, high-performance, secondary materials) requires the study and development of many technologies, processes and capital assets.

The objectives of this priority for the coming years refer to:


# **Interaction with Other Intervention Lines**


# **Time Horizon**

Short-term goals (2–3 years):

• Manufacturing of structural and functional materials: this goal, which is currently consolidated at laboratory level, will be effectively introduced into the manufacturing system in the coming years.

Medium-term goals (4–6 years):

• Technologies for the use of nano-materials: the technologies that will allow industrial production in the next five years will have to be developed in the short and medium term.

Long-term goals (7–10 years).


# **7 PRI5.6. Micro-Scale Processes, Products and Functionalities**

In recent decades, a strong trend has emerged towards the miniaturization of devices, to extend their use to contexts with space and weight limitations. The electronic field, with MEMS, was the pioneer of this breakthrough towards miniaturization, first with laptops and then with smartphones. The coming of microengineering, in addition to microelectronics, has generated new product and process strategies that are becoming significantly widespread in the aerospace, automotive and, above all, biomedical industries. The latter, in particular, represents the real next frontier of micro devices development. In fact, applications such as lab-on-chips and microfluidic devices in general, for home care and self-diagnosis, have become attractive development areas for the scientific community and the industrial world.

In the field of micromachining, two different approaches can be identified, namely one that specifically focuses on the creation of micro components and one that deals with developing micro functionalities on macro products. These two approaches often involve, in different ways, main micro-processing techniques such as micro EDM, laser micro-processing, micro-milling, injection micro-molding, surface micro structuring. In other cases, however, they all contribute, thanks to an interdisciplinary approach, to the development of very complex devices with a massive impact in terms of diffusion.

The "micro factory" has become the new manufacturing standard, and it focuses on the realization of micro components and micro devices. The production of micro parts often requires special manufacturing, handling and assembly environments, such as clean rooms or vacuum chambers. A prerequisite for both the micro-factory and the (macro) factory, is the achievement of complete process integration. The manufacturing of micro parts can, in fact, be facilitated if different processes (or process phases) are performed with only one positioning, in order to contain machining tolerances, with micrometric or even sub-micrometric precision. A new concept of factory is being defined, as a result of the high cost of the devices and equipment used in microfabrication, and the high technological competence required, namely the concept of a widespread (micro) factory, in which the various parts of the equipment are available in different places, towns or even regions. This approach distributes the costs of purchasing and operating equipment, but it also requires planning to optimize the transfer of components or semi-finished products (sometimes in considerable volumes) from one place to another. Thus, "design for micro-manufacturing" and "design for micro-assembly" are no longer alternative, but strongly interconnected approaches. An aspect that is positively affected by this miniaturization process is certainly logistics. Indeed, moving these micro parts is very simple and very economical as their small size makes them considerably easier to be packed and transported safely, while also ensuring the management of large volumes.

Therefore, the essential goals for the next few years, in order to bring the micro manufacturing sector to an important level of feasibility, are:


because of its small size and the risk of damaging micro components. It is therefore necessary to study and develop new inspection technologies, new approaches and methodologies to support the geometric and surface characterization of micro products.


#### **Interaction with Other Action Lines**


#### **Time Horizon**

Medium-term goals (4–6 years):


level takes on considerable importance in the micro field. Let us think, for example, of the Van derWalls forces, the surface tension rather than the adhesion coefficient.

Long-term goals (7–10 years).


# **Suggested References**

A graphical method for performance mapping of machines and milling tools.


Global Machine Tool Outlook October 2020 by Oxford Economics.

https://www.ptc.com/it/products/cad/generative-design

Environmental Macro Indicators of Innovation (http://www.emininn.eu/). Italian Government, Piano Industria 4.0.


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Strategic Action Line LI6: Evolving and Resilient Production**

**Cristian Secchi, Fabio Golinelli, Luigi Semeraro, Paolo Benatti, Edmondo Gilioli, Marco Macchi, Gildo Bosi, Sandro Salmoiraghi, and Luca Tomesani**

**Abstract** The objective of this chapter is to describe the strategic action line related to evolving and resilient production (LI6). In particular, this chapter proposes research and innovation priorities aimed at exploiting a high degree of machine automation and self-learning, with levels of autonomy and adaptive intelligence designed to facilitate the operators' job. From the work with cluster members, it emerged in particular that the following topics need to be studied and developed in the coming years: modelling and simulation for the design and management of production systems as well as hardware and software technologies for production system reconfigurability. The technology enablers are linked to the availability of smart modular devices that can be integrated wireless in a transparent, autonomous way, capable of monitoring and controlling manufacturing assets and products, and supporting decisionmaking, ensuring ready access to all necessary operational, configuration, fault and maintenance data.

C. Secchi (B)

Università Di Modena E Reggio Emilia, Modena, Italy e-mail: cristian.secchi@unimore.it

F. Golinelli · L. Semeraro ABB, Dalmine, Italy

P. Benatti BLM GROUP, Levico Terme (TN), Italy

E. Gilioli CNR-IMEM, Parma, Italy

M. Macchi Politecnico Di Milano, Milan, Italy

G. Bosi Sacmi Imola, Imola, Italy

S. Salmoiraghi SALMOIRAGHI, Monza, (MB), Italy

L. Tomesani Università Di Bologna, Bologna, Italy

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_10

**Keywords** Reconfigurable processes · Sensors · Digital twins · Smart inspection · Human–robot interaction

# **1 Introduction**

The use of sensors, automated controllers and embedded systems has become an essential part of production systems. However, many industrial companies have rather chosen to develop a proprietary Intranet of Objects, focused on local, restricted and closed-loop scenarios. Yet, the changes that are required of companies depend on a greater and deeper interaction between the various parts of the factory, based on collaborative machine-machine and human–machine behaviours. The analysis of the information collected and exchanged will allow to evaluate the occurrence of modifications and adapt the behaviour of machines, ensuring optimization of efficiency even in variable contexts. This will in turn encourage the implementation of manufacturing intelligence, i.e. a self-aware production system, capable of evolving and resilient enough to defy the uncertainties of the context.

Collaboration and connectivity will result in large amounts of data that will need to be analysed in real-time or near-real time and to be converted for the mobile devices of the decision makers at both central management and plant levels. Manufacturing firms will have a competitive advantage over their competitors if they can perform real-time analytics on a large volume of data from business processes, products and management systems.

The objective of this strategic action line is studying a new generation of production systems that can evolve over time to adapt dynamically to the changing conditions of the context, which are determined by the turbulence of demand, the speed of technological cycles, the dynamics of the competitive situation, and also by the dynamics resulting from sudden changes, such as catastrophic events like pandemics. The new production systems should, therefore, be conceived like evolving and resilient ones thanks to a high degree of machine automation and self-learning, with levels of autonomy and adaptive intelligence to facilitate operators to a large extent. Priority research topics regard: modelling and simulation for the design and management of reconfigurable production systems with related hardware and software technologies. Technological enablers will be dependent on the availability of modular and intelligent devices integrated wireless in a transparent and independent way, capable of monitoring and controlling production assets and products, and of supporting decisions based on data related to the entire operational, configuration, failure and maintenance processes (Fig. 1).

This objective will be implemented through research activities on the following areas:

• **Reconfigurability:** Design and control of reconfigurable and modular production systems;

**Fig. 1** Strategic Action Line 6—Evolving and resilient production


Artificial intelligence and related techniques will also have a significant influence in the development of each research and innovation priority.

*Expected impact*: improvement of the manufacturing sector's capacity to adapt to the continuous evolution of technological, economic and market scenarios; improvement of the sector's ability to respond to endogenous and exogenous shocks (from natural disasters to the temporary unavailability of critical infrastructures, from epidemics and health emergencies to contingent situations with a high impact on performance and continuity of operations); decrease in time to market; improvement of production factors' efficiency; improvement of production control in real time/ near-real time; improvement of system performance prediction capacity and greater human–robot collaboration.

The research and innovation priorities of the strategic action line on Industrial Sustainability are:


• **PRI6.5**—Human—Robot Co-working

# **2 PRI6.1 Design and Control of Reconfigurable Production Systems**

The evolution of market and the speed of change in consumer needs are shifting mass production in favour of new products that can quickly meet immediate needs even when caused by disruptive events and trends related to product customization.

The new paradigm of competitive manufacturing will be redesigning assets adapting as promptly as possible to market changes, relying on an adaptive and resilient production system. The goal of this research and innovation priority is the study and development of technologies and algorithms for the design and control of highly reconfigurable manufacturing systems (i.e. easily integrated, adaptable and scalable).

Compared to the state of the art, the problem of reconfigurability must be considered at both machine and production line level, which involves the necessary development of advanced design, control and simulation systems and techniques that can adapt and optimize production models according to the product or the product mix.

The objectives of this research and innovation priority concern in particular:


• **Configuration of monitoring and control.** The reconfigurability of a production system requires adequate methodologies for configuration of a monitoring and control system that should adapt to the variability of applications and operating conditions. In particular, It is necessary to study and develop new methodologies for safe and remote commissioning of production lines when technicians can't be on site with the support of systems for the modelling, simulation, control and monitoring of the systems.

#### **Interaction with Other Lines of Action**

LI1—Personalised production: reconfigurability affords the necessary adaptability for personalised production systems.

LI4—High efficiency production & zero-defect production: a reconfigurable production system can adapt to varying operating conditions to achieve maximum efficiency at all times.

LI5—Innovative production processes: reconfigurability is frequently the key element for the development of new highly flexible production processes.

# **Time Horizon**

Short-term goals of 2–3 years (starting from existing technologies):

• Solutions to simplify the definition of the digital factory model and corresponding simulation environments, through procedures based on techniques of identification and adaptive recognition of the resources used in the production system—machinery, people, materials, also aimed at remote set-up and control;

Mid-term goals of 4–6 years (significant development required):


Long-term goals of 7–10 years (requiring the integration of all the technologies developed by the research and innovation priority):

• Configuration of advanced monitoring systems for production plants, generation of dynamic digital twins with extensive use of machine learning and deep learning techniques.

# **3 PRI6.2 Components, Sensors and Intelligent Machines for Adaptive and Resilient Production**

An adaptive and resilient production system requires new components that can detect and exchange data in an effective and intelligent way as well as new methodologies for managing collected and exchanged data which need to be integrated into the machines operating in the production plant.

Adaptive and resilient production systems will be characterized by multisensory networks for the supervision of processes and environments and for data collection. Increasingly sensitive and cost-effective sensors will facilitate the measurement of various process-influencing parameters, including on site measurements for process monitoring.

Sensor networks collect data that can be stored and processed near processes (edge computing) or uploaded to a private or public cloud network. Data must be available anytime and anywhere within the production system so that they can be processed through artificial intelligence systems, improving knowledge of a process at a systemic and detailed level.

The objectives of this research and innovation priority concern in particular:


and in terms of the provided data formats. It will also be necessary to study new decoupling systems between the data measurement and collection part and the machines and plants control system.


#### **Interaction with Other Strategic Action Lines**

LI5—Innovative production processes: the new components and materials and the most effective sensors will forge intelligent machines to be deployed towards the design of innovative production processes.

LI1—Personalized production & LI4—High efficiency production: the new components will improve production systems, increasing both their adherence to individual needs (LI1) and their production efficiency, by reducing waste and defects (LI4).

#### **Time Horizon**

Short term of 2–3 years (starting from existing technologies).


Medium-term of 4–6 years (significant development required).


Long term of 7–10 years (requires the integration of all the technologies developed by the research and innovation priority).

• Integration, at machine and process level, of "in-process" monitoring techniques for a closed-loop control of semi-finished/finished products' quality and for the implementation of self-diagnostic and predictive maintenance logics.

# **4 PRI6.3 Digital Twins for Performance Prediction and Operational Management in Highly Flexible Production and Logistics Systems**

The introduction of Cyber Physical System (CPS) allows the development of advanced and highly flexible production and logistics systems. With CPSs in the factory, shop floor control architecture for supervision in real/near-real time becomes feasible. This development is required by the current high product-variety trends, which result in a resource management complexity that has to be synchronized to the production-logistics system.

The "traditional" ability to manage and control the system in real time is supported by a new ability to predict performance in short-term decision-making (i.e., a few hours, several work shifts, a few weeks of planning) and supervision of decisions regarding operations. The use of Digital Twins (DTs) is a help in this context, for the processing of real-time/near-real time information from the field to support production control, in coordination with related activities such as maintenance, factory logistics, quality and others.

The potential of DTs in this area promises benefits in terms of enhancing system performance prediction capacities starting from constant monitoring of activities, in terms of ensuring the robustness of production programs in the face of process variability and frequent changes in workload conditions, and in terms of enabling a high response capacity to market needs while respecting production efficiency and costs. These features support the decision makers in the prompt evaluation of the various alternatives involved, so that they can select the best option in consideration of the system's operating conditions.

The general goal of this research and innovation priority is the study and development of methodologies and tools geared to improve the use of DTs in real time/ near-real time, for the ultimate purpose of exploiting their high potential for performance prediction and operational management of production and logistics systems with high flexibility.

In particular, the introduction of this type of DT is intended to complement existing architectures for the control and coordination of production, based on systems classed as Manufacturing Execution Systems (MES), and on advanced systems built with Internet of Things (IoT) infrastructures, introduced in order to integrate information on the state of the process, machinery and other resources involved in factory production and logistics also by applying Artificial Intelligence technologies for the classification and prediction of the state of the processes, technical assets (such as machines, handling systems, equipment, …) and the activities of the operators in the operating stations.

The objectives of this research and innovation priority concern in particular:

**Integration of DTs with IoT platforms for monitoring the real state of the production-logistics system**, and in general with standard connections and protocols to ensure communication with sensors and local controllers from the field, to improve the identification of the critical factors that emerge from the shop floor in relation to the status of the process, the machinery and other resources involved in factory production and logistics. In particular, these DTs must be able to generate a feedback on the system-control process and the related machinery and equipment in progress, and facilitate the synchronization of material flows in the Digital Twin's real time near-real time simulation.

**Development of DT's advanced functionalities for the monitoring and analysis of material flows**, starting from the logical and physical traceability of specific elements (the marking, labelling of products or, in general, from information collected through tracking devices).

**Integration of DT with Artificial Intelligence (AI) techniques for the classification and prediction of operating conditions, state of health and future degradation of machines and other technical assets** (with machine learning algorithms to support classification and prediction with a view to integrating the simulation capabilities of the DT).

**Development of advanced functions in the DT for monitoring purposes and use of augmented intelligence in the execution of operators' tasks,** to improve, including through AI techniques, a human–machine interaction in an increasingly close collaboration scenario, to optimize coordination and efficiency in the execution of operational tasks and facilitate operator productivity by mitigating the complexity due to the variety of products.

**Integration of the DT with methods and tools to monitor and supervise production,** to adapt the production program depending on the status of the process, machinery and activities in the various operating stations, to increase the robustness of performance in the face of process variability and frequent changes in workload conditions.

**Development of advanced functions in the DT to monitor and analyse the sustainability of production technologies**, to analyse consumption and limit waste of resources employed as part of a process of continuous performance improvement that combines DT simulation with "traditional" analysis techniques.

Eventually, the production-logistics system will draw overall benefits from the prediction, adaptability and resilience driven by a supervisory control in real time/ near-real time, supported by the DT and by the systems backing operational decisions that are connected to it. In particular, real time/near-real time DTs for productionlogistics systems will guarantee:


# **Interaction with Other Lines of Action**

LI5—Innovative production processes: DTs should be developed in a way that is functional to technologies.

LI7—Digital platforms, modelling, AI, security: The technologies developed in the research and innovation priority can be applied to develop the platforms under LI7 and vice versa.

#### **Time Horizon**

Short-term goals of 2–3 years (starting from existing technologies).


Mid-term goals of 4–6 years (significant development required).


Long-term goals of 7–10 years (requiring the integration of all the technologies developed by the research and innovation priority).

• Development of advanced functionalities in the DT for the monitoring and analysis of the sustainability of manufacturing technologies

# **5 PRI6.4 Smart Inspection & Machine Learning**

Competition on global markets is determined by the possibility of ensuring, on the one hand, the quality of finished products and, on the other, the perfect efficiency of production systems, in a dual relationship in which the production system determines the quality of the product, while the quality of the product is evidence of the efficiency of the system. In an adaptive and resilient production system, the ability to verify production quality in a simple and intuitive way is essential, and smart inspection systems based on image analysis are the most attuned to this need. At the same time, the complexity of relations between system and product, in a context of continuous changes in products and operating conditions, can be managed through continuously adapting artificial intelligence systems that do not need reprogramming.

The goal of this research and innovation priority is the study and development of smart inspection systems and algorithms constantly connected to the production system. Compared to the state of the art, it is necessary to develop new smart inspection systems that can be intuitively programmed and that exploit the data exchanged in the factory to obtain a prediction of the quality of production while, on the other hand, helping to generate reliable information for the maintenance of the production system. This activity will have to allow a "Zero"-defects oriented strategy, with a production quality management that leverages the capabilities useful to other company areas, such as product design and machine design.

The main gap in state of the art systems is the lack of availability of smart inspection systems that are at the same time representative of a large number of product characteristics and that can be interpreted for decision-making purposes. For example, the development of supervised Machine Learning techniques, both predictive and classifying, should support the collection of information that comes from products and should automatically adapt to ever different products, without the need to be reprogrammed. Finally, it is necessary to develop advanced predictive maintenance systems that are related to production quality as well as to a machine's sensor system.

The availability of reliable smart inspection systems will allow operators to enter controls online, increase production reliability and reduce costs and time to market at the same time.

The objectives of this research and innovation priority are:


product quality, but also the prediction of future events connected to the quality of the product, with a view to anticipating their occurrence. These algorithms must be able to optimize their parameters, adapting to different products and contexts, with no need to be reprogrammed. They should also allow an evaluation of the vision system while it is being designed and implemented, so that it produces as accurate a rating as possible.


#### **Interaction with Other Strategic Action Lines**

LI4—High efficiency integrated systems & zero-defect production: Smart inspection systems can be exploited to implement high efficiency systems.

LI5—Innovative production processes: Smart inspection systems can be integrated into the development of innovative production processes.

# **Time Horizon**

Short-term goals of 2–3 years (starting from existing technologies).


Mid-term goals of 4–6 years (significant development required).


Long-term goals of 7–10 years (requiring the integration of all the technologies developed by the research and innovation priority).

• Design of self-repairing machines, with self-diagnosis, reshaping and dynamic adaptation of process conditions, identification of maintenance actions, choice of optimal intervention strategies.

# **6 PRI6.5 Human Robot Co-Working**

Today, collaborative robots are available on the market and provide safe interaction between humans and robots. However, the performance that can be achieved with collaborative robots is still limited.

Compared to the state of the art, this research and innovation priority is based on the fact that human–robot collaboration aims to optimize production and improve work quality to obtain robotic systems that make the work of operators easier and efficient.

The goal of this research and innovation priority is to promote the study and development of algorithms for human–robot collaboration in the context of production systems. The interaction between the operator and the robot must be efficient, natural and intuitive. Furthermore, robots should contribute to the improvement of working conditions, improving posture and relieving users of the heaviest tasks (e.g. lifting loads in excess of 10 kg).

The objectives of this research and innovation priority concern in particular:

• **Awareness.**Robots should be designed to be aware of the logistics and nature of its surroundings to optimise performance in human–robot interaction. It is therefore essential that robots process the data collected by on-board and off-board sensors to gain awareness of the surrounding environment and of the operator with whom it is collaborating. In particular, it is necessary to research and develop new sensors and data processing algorithms to provide robots with a kinematic and semantic representation of the surrounding environment and of the operator. Robots and their control systems will thus be aware of the nature of and movement in their surroundings.


#### **Interaction with Other Lines of Action**

LI5—Innovative production processes: the human–robot collaboration techniques developed in this research and innovation priority can be exploited for the development of innovative production processes.

#### **Time Horizon**

Short-term objectives (2–3 years) start from existing technologies to optimize:


Medium-term objectives (4–6 years) require a significant development as regards:


Long-term objectives (7–10 years) require the integration of all the technologies developed by the research and innovation priority in the short and medium term in order to improve:


# **References**


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# **Strategic Action Line LI7: Digital Platforms, Modelling, AI, Cybersecurity**

**Pierpaolo Pontrandolfo, Guido Colombo, Rosanna Fornasiero, Maria Cristina Vistoli, Angelo Messina, and Francesco Zanichelli**

**Abstract** The objective of this chapter is to describe the strategic action line related to digital technologies for production processes and systems (LI7). In particular, this chapter proposes research and innovation priorities aimed at innovative digital architectures for the monitoring, control and management of manufacturing activities and related assets, modelling new products/services and production processes, use of Al, Big data and adequate Cybersecurity systems. More specifically, the research and innovation priorities are based on the assumption that criteria need to be defined for the management and transformation of raw production data into strategic information for decision makers, identifying the information to be collected from each digital access point by means of suitable enabling technologies and then delivered as appropriate. Digital platforms and cybersecurity also play a significant role in the definition of dynamic supply chain models.

**Keywords** Digital platforms · Industrial IoT · Business analytics · Cybersecurity · Supply chain

P. Pontrandolfo (B) Politecnico Di Bari, Bari, Italy e-mail: pierpaolo.pontrandolfo@poliba.it

G. Colombo Orchestra Srl, Torino, Italy

R. Fornasiero CNR-IEIIT, Padova, Italy

M. C. Vistoli INFN-CNAF, Bologna, Italy

A. Messina ENGINSOFT SpA, Trento, Italy

F. Zanichelli Università Di Parma, Parma, Italy

© The Author(s) 2024 R. Fornasiero and T. A. M. Tolio (eds.), *The Future of Manufacturing: The Italian Roadmap*, Springer Tracts in Mechanical Engineering, https://doi.org/10.1007/978-3-031-60560-4\_11

# **1 Introduction**

The different organizational and technological maturity levels of companies, especially of SMEs, call for a reference framework that is sustainable and feasible over time and that can adapt to each company's specific capabilities and the peculiarities of the Italian context. The following aspects should be considered:


The digitalization of Italian manufacturing focuses on the development of flexible, reconfigurable, easily integrated digital architectures at sustainable costs, enhancing the professional profiles connected to the digitalization of companies.

In particular, cloud-based approaches should be integrated with edge computing solutions, to make the most of the benefits associated with technologies such as AI, digital twin, Industrial IoT. The various enabling technologies are chosen and adopted by the products available on the market, and combined with each other on the basis of actual needs and of the advantages they can guarantee in the short, medium and long term.

Investment in research and innovation activities that go beyond business solutions is necessary. The use of platforms can profoundly change operation methods in the manufacturing sector, opening up scenarios that can be promising for companies, allowing companies to federate and increase their critical mass. There is however an element of risk, as platform managers may gain a dominant position and limit the freedom of movement of the companies that use the platform.

This set of challenges prompts the vision of Industry 5.0, which expands the typical technological aspects of Industry 4.0 to include in the new technological developments the role of humans and add emphasis on sustainability.

Strategic action line LI7 aims to define research and innovation priorities for the development of innovative digital architectures for the monitoring, control and management of the progress of production and its assets, the modelling of new products/services and production processes, the use of AI solutions, Big data and adequate cybersecurity systems (Fig. 1).

In particular, the research and innovation priorities of the LI7 line must take into account that it is becoming increasingly necessary to define criteria for the management of raw data in production and to transform them into strategic information for decision makers, identifying the information to be collected from each digital access point through suitable enabling technologies that need to be appropriately conveyed.

**Fig. 1** Strategic Action Line 7—Digital platforms, modelling, AI and cyber security

The new systems to be developed must ensure that each digital access point provides adequate information at the appropriate organizational level, growing in complexity and granularity depending on the size of the company.

Particular attention must be paid to the advantages offered by solutions that are:


LI7 aims to formulate its goals taking into account that:


systems/products, and promoting their functional interoperability. At factory level, it is necessary to integrate production, logistics, quality and maintenance to increase efficiency and effectiveness of production systems.


Because of the value-added services it has introduced—such as predictive maintenance and, more generally, connectivity to IoT platforms inside and outside company networks—digital transformation increases exposure to cybersecurity risks. Developing a series of architectures, platforms and services that guarantee resilience in a manufacturing plant's activity is therefore an essential business and regulatory compliance requirement.

*Expected impact*: pervasive use of digital platforms, technological infrastructures, advanced services for production chain management and distribution of product/ service systems targeted to the end customer, to increase efficiency and productivity and achieve an adequate connection between supply and demand; improvement of trust creation processes in business networks; greater interconnection between the players in the supply chain; faster delivery of advanced digital services; full integration and normalization of data; interoperability and scalability of the deployed systems; greater effectiveness and efficiency of technologies thanks to cybersecurity; increased safety of production systems.

The research and innovation priorities of the strategic action line on Digital Platforms, Modelling, AI and Cybersecurity are:

PRI7.1. Models and tools for the management of collaborative companies and dynamic supply chains

PRI7.2. Design of integrated product-service solutions

PRI7.3. Models and tools for production monitoring and asset management

PRI7.4. IIoT models and tools for factory data collection

PRI7.5. Business and industrial analytics methodologies PRI7.6. Tools for modelling and management of information based on digital twins

PRI7.7. Models and tools to support Information and Cybersecurity.

# **2 PRI7.1. Models and Tools for the Management of Collaborative Businesses and Dynamic Supply Chains**

The strategic scenario that Italian manufacturing is facing is characterized by high international competition, both in terms of cost and technological innovation, turbulence and uncertainty in the upstream and downstream markets (recently emphasized by the tensions on trade regulations). Moreover, there is a need to manage complex and dynamic business networks where reconfigurations can be associated with political events, such as custom duties or Brexit, and environmental, or health care events, such as the Covid 19 pandemic.

The scenario shows comparatively higher stresses in Italy, due to typical aspects like the well-known small scale of companies and the heavy dependence on foreign countries in both upstream raw material procurement markets and downstream final product markets.

With regard to raw material procurement, approaches based on circular economy have considerable importance. They show advantages not only in terms of environmental sustainability, but also in terms of reduction of procurement risk (both in relation to resource availability and cost).

As described above, it is essential for companies to operate effectively and efficiently within their global Supply Chains (SC). They should also aim to enter new SCs dynamically, and with similar efficiency, based on specific business opportunities.

Integration should be increased, in the first place, by improving machine connectivity within the various supply chain plants, and the capability for monitoring and controlling work-in-progress in real time, keeping track of both products and operating conditions, at different production sites. Integration, however, relies on data and information that are often available in different formats, and are a key prerequisite for collaborative integration in global SCs.

The full exploitation of this data is based on collaborative management within the SC and ensures integration of processes and flows that go beyond the corporate scope of each SC member. For these opportunities to become operational, it is necessary to develop methods and approaches that encourage trust among the participant, extract value from information and share the benefits among them.

The aim of this research and innovation priority is to study and develop new technological and organisational solutions and foster the creation and adoption of digital platforms according to an open model, based on interoperability of the various systems adopted by the different SC actors, and characterized by specific vertical process applications mapped onto mobile (e.g. 5G) or fixed (fibre) network architectures. These infrastructures are a key prerequisite for implementing approaches based on big data analytics and AI algorithms in support of decision-making processes. In this sense, the distributed ledger paradigm offers features consistent with the distributed nature of the organizational processes and supply-chain flows.

The solutions developed should be geared to overcome trust problems between the various SC actors and increase transparency in the exchange of information all along the supply chain, possibly in a selective and suitably manageable way, and overcome the fragmentation of information thanks to an ontology-based vision of data.

#### **Digital platforms become a key asset for the collaborative management of dynamic supply chains and are based on the following specific research goals:**

	- o Systems for expanding the physical traceability of each product and component: ensuring tracking systems so that the causes of a problem can be easily identified throughout the supply chain. Such systems would have a useful application also in terms of managing counterfeiting issues;
	- o Information-certification systems related, for example, to the processes carried out by suppliers, subcontractors, and distributors to extract reliable data through suitable mechanisms, making such data selectively accessible through distributed ledger and blockchain mechanisms.
	- o Big Data analytics and Artificial Intelligence systems to support supplier analysis and selection based on specific performance not only in terms of operations (costs, time, and quality) but also of sustainability.
	- o Definition of trust improvement mechanisms in collaborative contexts. In order to improve coordination within the SC, especially when decision-making is distributed, approaches to design and develop mechanisms for the definition of smart contracts also by means of blockchain as enabling technology;
	- o Methods and tools to evaluate and optimize supply-chain robustness and to design supply chains to ensure the expected performances in different reference scenarios.

• **Digital twin for the SC**: study and development of a digital twin for the SC, based on methods to connect different digital models of the products, machines, lines of the SC's actors, ensuring the overall consistency and the modelling of complex interactions that generate emerging, hard-to-predict behaviours.

# **Interaction with Other Strategic Action Lines**

Possible interactions with other Strategic Action Lines are as follows:


# **Time Horizon**

Short-medium term goals (2–3 years).

• Models and systems for the configuration of the SC, to speed decisions at project level (e.g. selection of a new supplier) by using suitable indicators for company profiling.

Medium-term goals (4–6 years):

• Development of models and systems for the operational management of the SC, and development of distributed digital twins that allow the evaluation of emerging behaviours, thus supporting decisions concerning the configuration of the SC itself and operations management.

# **3 PRI7.2. Models and Tools for Designing Integrated Product/service Solutions**

Durable equipment is increasingly being managed through a service-based model while until recently durable assets were mainly purchased in ownership. The commercial transaction is thus valorised not so much on an ownership basis but through the use of the asset itself. This trend is already established in both the B2B and B2C segments, for example in the aeronautical sector, where engines are supplied to aircraft manufacturers as a cost-per-flight-hour service, or in the automotive sector where vehicles or construction equipment are rented long term and paid per mile or working hour.

Also in the sector of capital goods for manufacturing, servitization makes it possible to integrate the sale of assets with services that ensure a machine's operating availability through on-condition and predictive monitoring and maintenance services.

This model also includes manufacturing processes supplied as a service (manufacturing as a service) by specialised companies that provide a machine's pay-per-use service to other manufacturing companies for particular processes that cannot be carried out on site.

The goals of this research and innovation priority are:

	- o Innovative APP and HMI for end users in SaaS (Software as a Service) mode and development of new interfaces between products and service to facilitate exchange of information between one component and another of an integrated solution and to enable appropriate support services.
	- o Digital platforms for the management of multi-tenant cloud digital services.
	- o Solutions involving the sensorization of machines to connect them to factory systems, in a safe and minimally invasive way;
	- o Generation of interfaces that automatically and/or semi-automatically integrate existing solutions and ensure communication with a machine;
	- o Automatic and/or semi-automatic systems interconnecting to company's MIS software or low-cost digital cloud service platforms that can be easily integrated to control work-in-progress and the improvement of predictive maintenance and factory automation;

# **Interaction with Other Strategic Action Lines**


# **Time Horizon**

Short-term goals (2–3 years):

• Digital solutions to revamp existing products by providing new interconnection capacities to company systems or digital service platforms.

Medium-term goals (4–6 years):

• Advanced digital services that enable traceability of the use of a product by integrating it with company systems or new digital services managed in the cloud to improve configurability and remote assistance.

Long-term goals (7–10 years):

• PLM design tools for products and services with a view to cybersecurity-by-design and to planning solutions that consider their impact throughout a product's life cycle.

# **4 PRI7.3. Models and Tools for Production Monitoring and Production Asset Management**

It is increasingly urgent that production processes be managed in synchrony with other business processes so that useful information is exchanged in real time and in a reliable way at different levels in the organization. By way of example, real-time management of information could be applied to the monitoring of product availability when the order has been placed, to plan the handling of semi-finished products within the factory, or to optimize transport times and costs, internal and external logistics, and the maintenance and data exchange with management systems.

A production process should generate objective and certified data, to facilitate analysis of the areas in which production should be improved, consolidate budgeting capacity and ensure availability of production resources for an efficient planning.

Industrial implementation of these solutions is at present almost entirely the prerogative of large companies, and that poses an additional limitation to their implementation. Furthermore, the focus is on individual and specific assets that tend to be complex and expensive. It is therefore necessary to make this production process accessible to full production systems at different complexity levels (even distributed and remote), and small and medium-sized companies, which cannot manage to exploit effectively the data they generate despite investing in I4.0-compliant machinery.

More and more frequently, operations technology (machines, automations, controls, SCADA, etc.) produce data that must be transformed into ready-to-use information for MIS (ERP, BI, logistics, etc.), including other production-related systems with which they can interface, such as MES, CMMS, PLM etc.

The objectives of this research and innovation priority concern:

	- o Interoperability in the horizontal and vertical integration of their systems
	- o Flexibility in the reconfiguration of their processes and information flows
	- o Data control and certification for the consolidation of decision-making systems
	- o Integration with digital supply chain platforms.

• **Solutions for factory communication based on 5G:** infrastructures for the dynamic management of assets must be able to convey diverse data in real time and in a massive manner by exploiting the URLLC (Ultra Reliable Low Latency Communication) operating modes of the 5G network. In addition, the development of a process' specific vertical applications (Verticals) will have to ensure reliability and security of communication and, at the same time, minimize latency. In particular, certain features of industrial 5G are inextricably linked to the typical requirements of operation technologies, and should be based on the creation of "connectivity bubbles" that connect the elements found within the corporate campus by integrating 5G and WLAN as required by the standards and ensure an adequate performance of reliability, availability, data-rate and latency. It will also be necessary to study the issues related to Beyong5G which involve the joint use of sensing and communication techniques.

#### **Interaction with Other Strategic Action Lines**


#### **Time Horizon**

Short-term goals (2–3 years):

• Development of Connectivity solutions based on 5G Private Industrial and 4G Public connectivity over WAN. Creation of PoCs and extension of coverage to the entire supply chain. Introduction and integration of standardized wired/ wireless connectivity platforms to ensure the necessary connectivity for Industry 5.0 processes and solutions.

Medium-term goals (4–6 years):


Medium-term goals (7–10 years).

• Study of Beyong5G communication systems that involve the joint use of sensing and communication techniques.

# **5 PRI7.4. IIoT Models and Tools for Factory Data Management**

A fundamental infrastructural element in the field of digital solutions concerns architectures and technologies for the generation (such as microsensors and connected MEMS), collection, processing, integration and sharing of raw data from the field which, transformed into appropriate information, can lead to an improvement in productivity and a reduction in environmental impact through the smart management of plant assets.

The paradigm of Industrial IoT, borrowed from the Internet of Things (more oriented to the interaction between user and smart object in the home or smart city) and based on the adoption of mission-critical technologies (for timing and QoS, reliability, security and privacy purposes) for M2M interaction, opens the way to a deeper understanding of the manufacturing process, thus enabling efficient and sustainable production, and process innovation (Xu et al., 2018; Sisinni et al., 2018).

The main objectives of this research and innovation priority include:


Such systems must also be designed to ensure the reconfiguration of the network itself.


The identified goals can be summed up as the full achievement of data integration and normalization, including in real-time, and the improvement of security, interoperability and scalability of the systems in the field, at project and implementation level, also considering the large installed base of legacy systems.

#### **Interaction with Other Strategic Action Lines**

There is no doubt that the research and innovation priority has strong interactions with strategic action lines:


#### **Time Horizon**

Short-term goals (2–3 years):


Medium-term goals (4–6 years):

• Design of interaction methods and data fusion systems to obtain information from the set of data collected by sensors useful for an industrial context.

# **6 PRI7.5. Advanced Business and Industrial Analytics Methodologies**

Digital transition opens significant opportunities in the management of systems, components and industrial plants. These opportunities improve efficiency and reduce environmental impact, but they also present significant new challenges.

The possibility to improve machinery and tools with sensors, the increase of communication speed, and the spread of computing capacity both locally (Edge) and remotely (Cloud) make available large volumes of data (including images) that through Artificial Intelligence algorithms can be used, for instance, for predictive maintenance and problem diagnostics, to optimize plant configuration and production strategies, to collaborate in real time during the production phases with customers (B2B). Furthermore, the new methodologies enable a direct feedback to the plant from sales and products usage data, to generate new solutions with an integrated B2B2C approach. In order to get value from industrial data, it is necessary to resolve the constraints posed by the collection of data in a plant environment, and precisely:


In light of the above, the objectives of this research and innovation priority concern:

	- o Ability to manage data applying ontological and reasoning approaches and to evaluate cause-effect relationships from information derived from heterogeneous sources such as tools, machinery and systems.
	- o Ability to combine input information from operators and machines and obtain information that would otherwise escape observation.
	- o Ability to re-train the non-deterministic models within times compatible with production plants.

#### **Interaction with Other Strategic Action Lines**

Interactions with almost all the research priorities of this action line, namely:


This research and innovation priority has strong relations also with LI5 and LI6 as regards the development of technologies at the various factory levels and with LI1-4 as regards the support it can give in defining solutions that can reinforce data management in relation to personalised products, sustainability, staff development and high efficiency.

# **Time Horizon**

Short-term goals (2–3 years):

• Virtual sensors

Medium-term objectives (4–6 years):


Long-term goals (7–10 years):


# **7 PRI7.6 Tools for Modelling and Management of Information Based on Digital Twin**

The progressive implementation of digital technologies (IoT, Advanced Sensors, Connectivity, Cloud and Edge Computing, Big and Small Data, AI) in companies makes it desirable and necessary to study and develop software tools and methodologies that exploit the advantages that can (and must) be obtained from these technologies.

At the moment, especially for PMEs, advantages mainly consist of an improvement of production efficiency. Clearly, that cannot be all. Further advantages will be the exploration and identification of new production paradigms aimed both at a better use of resources (e.g. circular economy, zero-defect manufacturing, reuse, remanufacturing and recycling), and at increasing business potential through greater efficiency in responding to market needs (e.g. mass customization, lot-size one production, reconfigurability of production systems) or new positioning in the value chain (e.g. servitization).

The development and use of dynamic digital models of all the physical entities composing the factory, i.e. digital twins, is desirable and necessary. In fact, a digital twin can be made for a product, machinery, plant, factory, system and should interact with its real twin (in a cyber-physical production system) in operation through a single, continuous, bidirectional and synchronized flow of data. Data coming both from the simulation models used mainly in the design phase, and directly from the field (embedded sensors, artificial vision, etc.) are sent, through IoT systems, both to proximity computers (edge computing), and to remote ones (cloud computing).

**The objective of the research and innovation priority is the development of methodologies and calculation tools to improve the use of the data flow generated by the digital twins**, with a view to exploit their enormous potential for increasing performance (economic and business, environmental and social sustainability) over the entire life cycle of the product. To do so, the virtualization of production systems (local or distributed) should be accompanied by:


#### **Interaction with Other Strategic Action Lines**

This priority is complementary to all research and innovation priorities of this research line.

Strong interaction with all the other Strategic Action Lines and, in particular, with the lines focusing on the various "Production Systems": LI1, LI4, LI5, LI6.

In fact, the development of this research and innovation priority would impact all types of production systems, which could even represent test cases for the development of this research and innovation priority.

#### **Time Horizon**

Medium-term goals (4–6 years):

• Hybrid solutions for simulation of complex production systems and decisionsupport systems and tools (DSS), as developments are essentially methodological and need to be translated into IT tools, which should be based on technologies already present on the market. This is an essential requirement for their use in businesses, especially in SMEs.

Medium-long term goals (4–10 years):

• Algorithms and methodologies simulating human behaviour in production systems with flexible automation currently involve pioneering research activities.

# **8 PRI7.7 Models and Tools to Support Information and Cybersecurity**

The risks associated with cyber-attacks, rated in the last years in the top ten risks of any business and government, must be assessed and managed at all levels (from governments to industries, to individuals).

Recent scientific literature confirms a growing attention to cyber risk and to research and innovation priority due to:

1. **An essential connection between industry 4.0 and cybersecurity, where heterogeneous models can become an obstacle to the prompt and secure adaptation of business to the new requirements**, particularly where there are interconnected technologies with different cyber resilience and people (internal technicians and/or suppliers) who work with different approaches to security;


The security processes that companies must implement to effectively counter growing risks cannot be separated from ongoing research, development and adoption of suitable process solutions and cyber security technologies. Research must necessarily be an iterative process, following both the continuous evolution of threats and the technologies/systems that must be protected.

Research and development in cybersecurity technical solutions should take into account:


In light of all this, the following goals are identified for this research and innovation priority:


recovery of information, if necessary. The main objectives of research on industrial control systems (ICS) are the analysis of vulnerabilities and frameworks for the detection of the safety and security properties of ICS/SCADA systems; b) **enabling micro-segmentation** in isolated cells of industrial systems, and allowing granular isolation in order to prevent lateral propagation of threats on other manufacturing systems; c) **increasing the security of the maintenance processes** of industrial systems, to prevent the propagation of threats from the systems used by the maintenance service to the industrial systems.


The evolution of cybersecurity technologies is based on the creation of ad hoc public libraries to be adopted from time to time as part of the technologies, in order to ensure effectiveness and efficiency. In particular, in terms of effectiveness, the continuous development of increasingly refined algorithms by exploiting the enabling technologies will make it possible to identify/counteract promptly the ever-evolving threats. In terms of efficiency, the development of agile representation and maintenance models throughout the life cycle will allow the management of cybersecurity technologies, avoiding dissipation of value and effort.

#### **Interaction with Other Strategic Action Lines**

Strong integration with the other action lines, in particular:

• LI4-LI5: continuous technological evolution as a result of the other strategic action lines requires new processes, new interconnections and the attribution of ever greater value to data, and a continuous reassessment of risks and necessary mitigating actions;


#### **Time Horizon**

The short-term goals (2–3 years):


The medium-term objectives (4–6 years):


**Acknowledgements** The authors would like to thank Antonio Catalano from Tenova and Nicola Caramella previously working in Ansaldo Energia for their support and contribution during the discussion and collection of materials that have brought to the definition of the chapter.

# **Suggested References**


Digital Twin White Paper (Engineering).

Ding, D., Han, Q. L., Xiang, Y., Ge, X., & Zhang, X. M. (2018). A survey on security control and attack detection for industrial cyber-physical systems. *Neurocomputing, 275*, 1674–1683.


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