Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87096
Título: | A decision support system based on a multivariate supervised regression strategy for estimating supply lead times |
Autor(es): | Barros, Júlio Dinis Lopes Gonçalves, João N. C. Cortez, Paulo Carvalho, Maria Sameiro |
Palavras-chave: | Big data Data mining Lead time uncertainty Safety stock Supply chain risks |
Data: | 2023 |
Editora: | Elsevier 1 |
Revista: | Engineering Applications of Artificial Intelligence |
Citação: | Barros, J., Gonçalves, J. N. C., Cortez, P., & Carvalho, M. S. (2023, October). A decision support system based on a multivariate supervised regression strategy for estimating supply lead times. Engineering Applications of Artificial Intelligence. Elsevier BV. http://doi.org/10.1016/j.engappai.2023.106671 |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/87096 |
DOI: | 10.1016/j.engappai.2023.106671 |
ISSN: | 0952-1976 |
Versão da editora: | https://www.sciencedirect.com/science/article/pii/S0952197623008552 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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eaai.pdf Acesso restrito! | 1,59 MB | Adobe PDF | Ver/Abrir |