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

Rodrigo Rivera-Castro, Ivan Nazarov, Yuke Xiang, Alexander Pletneev, Ivan Maksimov, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationAdvances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
    EditorsHuchuan Lu, Huajin Tang, Zhanshan Wang
    PublisherSpringer Verlag
    Pages213-222
    Number of pages10
    ISBN (Print)9783030227951
    DOIs
    Publication statusPublished - 2019
    Event16th International Symposium on Neural Networks, ISNN 2019 - Moscow, Russian Federation
    Duration: 10 Jul 201912 Jul 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11554 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference16th International Symposium on Neural Networks, ISNN 2019
    Country/TerritoryRussian Federation
    CityMoscow
    Period10/07/1912/07/19

    Keywords

    • Demand forecasting
    • Kernels
    • Neural networks
    • Supply chain modelling

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