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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schelthoff, K., Jacobi, C., Schlosser, E., Plohmann, D., Janus, M., & Furmans, K. (2022). Feature selection for waiting time predictions in semiconductor wafer fabs. IEEE Transactions on Semiconductor Manufacturing.
Lingitz, L., Gallina, V., Ansari, F., Gyulai, D., Pfeiffer, A., Sihn, W., & Monostori, L. (2018). Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer. Procedia Cirp, 72.
Burggräf, P., Wagner, J., Koke, B., & Steinberg, F. (2020). Approaches for the prediction of lead times in an engineer to order environment-A systematic review. IEEE Access .
Singh, S., & Soni, U. (2019). Predicting order lead time for just in time production system using various machine learning algorithms: A case study. In: 2019 9th International Conference on Cloud Computing, Data Science and Engineering (Confluence) (pp. 422-425). IEEE.
Zhang, C., Yella, J., Huang, Y., & Bom, S. (2021). Learning from failures in large scale soft sensing. IEEE International Conference on Big Data .
Mahajan, T., Singh, G., Bruns, G., Bruns, G., Mahajan, T., & Singh, G. (2021). An Experimental assessment of treatments for cyclical data. In Proceedings of the 2021 Computer Science Conference for CSU Undergraduates, Virtual , (vol. 6).
Vien, B. S., Wong, L., Kuen, T., Rose, L. F., & Chiu, W. K. (2021). A machine learning approach for anaerobic reactor performance rediction using long short-term memory recurrent neural network. Structure Health Monitoring, 18, 61
Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation. Research. Part C: Emerging Technologies.
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (Vol. 26). Springer.
Parvandeh, S., Yeh, H. W., Paulus, M. P., & McKinney, B. A. (2020). Consensus features nested cross-validation. Bioinformatics, 36(10).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shen, X., Moder, P., Pfeiffer, C., Walther, G., Ehm, H. (2023). Data-Driven Prediction of Order Lead Time in Semiconductor Supply Chain. In: Grothe, O., Nickel, S., Rebennack, S., Stein, O. (eds) Operations Research Proceedings 2022. OR 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-24907-5_77
Download citation
DOI: https://doi.org/10.1007/978-3-031-24907-5_77
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24906-8
Online ISBN: 978-3-031-24907-5
eBook Packages: Business and ManagementBusiness and Management (R0)