A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain
| Autor: | M. Sameiro Carvalho, João N.C. Gonçalves, Nuno M. Frazão, Paulo Cortez |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Information Systems and Management Computer science Supply chain Engenharia e Tecnologia::Outras Engenharias e Tecnologias 02 engineering and technology Management Information Systems Arts and Humanities (miscellaneous) Economic indicator 020204 information systems Component (UML) 0502 economics and business Machine learning 0202 electrical engineering electronic engineering information engineering Developmental and Educational Psychology Econometrics Autoregressive integrated moving average Supply chain management Science & Technology 05 social sciences Univariate Ciências Naturais::Ciências da Computação e da Informação Demand forecasting Decision support Production planning Outras Engenharias e Tecnologias [Engenharia e Tecnologia] 050211 marketing Ciências da Computação e da Informação [Ciências Naturais] ARIMAX Information Systems Forecasting |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | Preprint 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. INCT-EN - Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UIDB/00319/2020) |
| Databáze: | OpenAIRE |
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