A decision support system based on a multivariate supervised regression strategy for estimating supply lead times

Júlio Barros*, João N. C. Gonçalves, Paulo Cortez, M. Sameiro Carvalho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Supply lead time constitutes a core parameter in inventory management and plays a critical role in supply chain performance. Yet, how to promote better supply lead time estimations that account for multivariate effects of historical supplier dynamics remains poorly understood. This paper proposes a decision support system that uses a supervised regression strategy with multivariate information for estimating supply lead times. We combine ideas from big data analytics and data mining to explore the effects of different supply-related variables on the dynamics of supply lead time. We design a robust rolling window evaluation scheme to compare both the statistical and inventory performance of different well-known data mining models. Numerical tests with empirical data from a large automotive manufacturer demonstrate that the Random Forest model consistently outperforms other competing models, leading to median decreases of 18%–24% in the mean absolute errors of supply lead time estimations. As a consequence of our results, we also provide insights on how these estimations contribute to the proactive management of safety stocks.

Original languageEnglish
Article number106671
Number of pages16
JournalEngineering Applications of Artificial Intelligence
Volume125
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Big data
  • Data mining
  • Lead time uncertainty
  • Safety stock
  • Supply chain risks

Fingerprint

Dive into the research topics of 'A decision support system based on a multivariate supervised regression strategy for estimating supply lead times'. Together they form a unique fingerprint.

Cite this