Data-Driven Prediction of Order Lead Time in Semiconductor Supply Chain

Xin Shen (), Patrick Moder, Christian Pfeiffer, Grit Walther and Hans Ehm
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Xin Shen: RWTH Aachen University
Patrick Moder: Infineon Technologies AG
Christian Pfeiffer: RWTH Aachen University
Grit Walther: RWTH Aachen University
Hans Ehm: Infineon Technologies AG

Chapter Chapter 77 in Operations Research Proceedings 2022, 2023, pp 645-652 from Springer

Abstract: Abstract This study proposes an AI-empowered order lead time prediction integrating a multidimensional real-world dataset from a semiconductor manufacturer’s supply chain. Examined features capture order–, delivery–, planning–, customer–, and product– related information. We thoroughly analyze a broad spectrum of machine learning algorithms ranging from linear regression and tree-based models to neural networks and compare them with respect to prediction performance, computation time, and understandability. We find that boosting algorithms demonstrate solid predictive performance with the highest accuracy and most efficient computation time. Our results allow supply chain experts to obtain data-informed estimations of order lead times and an understanding of the predictive mechanisms.

Keywords: Machine learning; Supply chain; Predictive analytics (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-24907-5_77

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DOI: 10.1007/978-3-031-24907-5_77

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