Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company
dc.contributor.author | Fotso Mtope, Franck Romuald | |
dc.contributor.author | Joneidy, Sina | |
dc.contributor.author | Pandit, Diptangshu | |
dc.contributor.author | Pour Rahimian, Farzad | |
dc.date.accessioned | 2024-04-02T15:46:24Z | |
dc.date.available | 2024-04-02T15:46:24Z | |
dc.date.issued | 2023 | |
dc.identifier | ONIX_20240402_9791221502893_65 | |
dc.identifier.issn | 2704-5846 | |
dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/89096 | |
dc.description.abstract | Efficient optimization of business processes required a profound understanding of expertise provided by domain specialists. However, extracting such insights can indeed be a laborious and time-consuming endeavour. This paper introduces the Multi-Aspectual Knowledge Elicitation framework (MAKE4ML) — a novel approach designed to effortlessly and effectively extract valuable information from domain experts. This framework inherently facilitates the development of machine-learning models capable of optimizing business processes, thereby diminishing reliance on experts. The framework's application within a food warehouse company is showcased, specifically targeting the enhancement of the procurement process. The employed methodology revolves around conducting comprehensive interviews with procurement experts, thereby enabling a meticulous exploration of diverse facets inherent to a business process. Subsequently, the gathered insights are employed to conceive and calibrate a machine learning model (time series forecasting). This model effectively emulates the domain experts' proficiency, offering invaluable decision-oriented insights. The outcomes of this study show that our framework allows efficient knowledge elicitation, which is a pivotal factor in formulating and deploying a bespoke machine-learning model. The proposed approach can be extended into various other business processes, thereby paving the way for operational refinement, cost reduction, and amplified efficiency | |
dc.language | English | |
dc.relation.ispartofseries | Proceedings e report | |
dc.subject.classification | thema EDItEUR::U Computing and Information Technology | |
dc.subject.other | domain experts | |
dc.subject.other | knowledge elicitation | |
dc.subject.other | multi-aspects | |
dc.subject.other | machine learning | |
dc.subject.other | procurement optimization | |
dc.subject.other | warehouse | |
dc.subject.other | technology acceptance | |
dc.title | Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company | |
dc.type | chapter | |
oapen.identifier.doi | 10.36253/979-12-215-0289-3.36 | |
oapen.relation.isPublishedBy | bf65d21a-78e5-4ba2-983a-dbfa90962870 | |
oapen.relation.isbn | 9791221502893 | |
oapen.series.number | 137 | |
oapen.pages | 12 | |
oapen.place.publication | Florence |