Abstract
Supply chains are very complex systems and their correct and efficient management represents a fundamental challenge, in which the practical needs of the corporate world can find answers together with the advanced skills of the academic world. This paper fits exactly in this area. In particular, starting from a project by the company Code Architects, we will illustrate how it is possible to make forecasts on shipments with machine learning tools, which can support business decisions.
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Acknowledgements
The authors thank the project PON “Ricerca Innovazione” 2014-2020 (PON) risorse FSE-REACT EU - DM 10 agosto 2021, n. 1062.
R. D’Ambrosio and C. Scalone are supported by GNCS-INDAM project and PRIN2017-MIUR project 2017JYCLSF “Structure preserving approximation of evolutionary problems“.
This paper is part of the project: OR.F.E.O.- ORchestrator For Enterprise Omniplatform Decreto n r.0001497 del 11/03/2020, Progetto n. F/190189/00/X44 Fondo per la Crescita Sostenibile - Sportello “FABBRICA INTELLIGENTE” PON I &C 2014-2020, di cui al D.M. 5 marzo 2018 Capo III.
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Carissimo, N., D’Ambrosio, R., Guzzo, M., Labarile, S., Scalone, C. (2023). Forecasting in Shipments: Comparison of Machine Learning Regression Algorithms on Industrial Applications for Supply Chain. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13957. Springer, Cham. https://doi.org/10.1007/978-3-031-36808-0_33
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