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        Inventory Analytics

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        Author(s)
        Rossi, Roberto
        Collection
        ScholarLed
        Language
        English
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        Abstract
        "Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike."
        URI
        https://library.oapen.org/handle/20.500.12657/49414
        Keywords
        demand forecasting; deterministic inventory control; inventory control; inventory systems; Python; stochastic inventory control; supply chain planning;
        DOI
        10.11647/OBP.0252
        ISBN
        9781800641778, 9781800641761
        Publisher
        Open Book Publishers
        Publisher website
        https://www.openbookpublishers.com/
        Publication date and place
        2021
        Classification
        Economic theory and philosophy
        Biography and non-fiction prose
        Social research and statistics
        Economics, Finance, Business and Management
        Stochastics
        Business and Management
        Management of specific areas
        Probability and statistics
        Pages
        184
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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