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A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain
Decision Support Systems ( IF 7.5 ) Pub Date : 2020-11-27 , DOI: 10.1016/j.dss.2020.113452
João N.C. Gonçalves , Paulo Cortez , M. Sameiro Carvalho , Nuno M. Frazão

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.



中文翻译:

装配行业中多步骤需求预测的多元方法:来自汽车供应链的经验证据

在许多供应链环境中,需求预测可作为运营,业务和生产计划决策的基础。然而,在最终客户需求不确定的情况下如何准确预测制造商对组件的需求仍然知之甚少。因此,向供应商分配适当的零件订购数量就变得不容易了,对计划,产能和与库存相关的成本产生了重大影响。本文介绍了一种多变量方法,可使用需求变化的不同领先指标在多个预测范围内预测制造商对组件的需求。我们将外生输入(ARIMAX)与机器学习(ML)模型的自回归综合移动平均模型进行了比较。使用实际案例研究 我们根据经验评估多元回归模型在组件生命周期中的预测和供应链绩效。实验表明,与传统的单变量基准相比,所提出的多元方法可提供出色的预测和库存性能。此外,它揭示了在组件的整个生命周期中的适用性,而不仅限于单个阶段。特别是,我们发现ARIMAX模型可以更好地预测生命周期开始时的需求信号,但在生命周期后期则优于基于ML的模型。它揭示了在组件的整个生命周期中的适用性,而不仅限于单个阶段。特别是,我们发现ARIMAX模型可以更好地预测生命周期开始时的需求信号,但在生命周期后期则优于基于ML的模型。它揭示了在组件的整个生命周期中的适用性,而不仅限于单个阶段。特别是,我们发现ARIMAX模型可以更好地预测生命周期开始时的需求信号,但在生命周期后期则优于基于ML的模型。

更新日期:2021-01-28
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