Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/70911
Título: | A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain |
Autor(es): | Gonçalves, João N. C. Cortez, Paulo Carvalho, Maria Sameiro Frazão, Nuno M. |
Palavras-chave: | ARIMAX Decision support Forecasting Machine learning Supply chain management |
Data: | 2021 |
Editora: | Elsevier 1 |
Revista: | Decision Support Systems |
Citação: | Gonçalves, J. N., Cortez, P., Carvalho, M. S., & Frazão, N. M. (2021). A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain. Decision Support Systems, 142, 113452 |
Resumo(s): | Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventory-related costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages. |
Tipo: | Artigo |
Descrição: | Preprint |
URI: | https://hdl.handle.net/1822/70911 |
DOI: | 10.1016/j.dss.2020.113452 |
ISSN: | 0167-9236 |
Versão da editora: | https://www.sciencedirect.com/science/article/abs/pii/S0167923620302074 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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DSS_2020.pdf | Accepted manuscript | 383,16 kB | Adobe PDF | Ver/Abrir |