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Demand Forecasting Techniques for Build-to-Order Lean Manufacturing Supply Chains

  • Rodrigo Rivera-CastroEmail author
  • Ivan Nazarov
  • Yuke Xiang
  • Alexander Pletneev
  • Ivan Maksimov
  • Evgeny Burnaev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

Abstract

Build-to-order (BTO) supply chains have become commonplace in industries such as electronics, automotive and fashion. They enable building products based on individual requirements with a short lead time and minimum inventory and production costs. Due to their nature, they differ significantly from traditional supply chains. However, there have not been studies dedicated to demand forecasting methods for this type of setting. This work makes two contributions. First, it presents a new and unique data set from a manufacturer in the BTO sector. Second, it proposes a novel data transformation technique for demand forecasting of BTO products. Results from thirteen forecasting methods show that the approach compares well to the state-of-the-art while being easy to implement and to explain to decision-makers.

Keywords

Demand forecasting Supply chain modelling Kernels Neural networks 

Notes

Acknowledgements

The research in Sect. 2 was partially supported by the Russian Foundation for Basic Research grant 16-29-09649 ofi m. The research presented in other sections was supported by the Mexican National Council for Science and Technology (CONACYT), 2018-000009-01EXTF-00154.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rodrigo Rivera-Castro
    • 1
    Email author
  • Ivan Nazarov
    • 1
  • Yuke Xiang
    • 2
  • Alexander Pletneev
    • 1
  • Ivan Maksimov
    • 1
  • Evgeny Burnaev
    • 1
  1. 1.Skolkovo Institute of Science and TechnologyMoscowRussia
  2. 2.Huawei Noah’s Ark LabHong KongChina

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