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A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains

Author

Listed:
  • Gabrielle Gauthier Melançon

    (Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada; Element AI, Montréal, Québec H2S 3G9, Canada)

  • Philippe Grangier

    (IVADO Labs, Montréal, Québec H2S 2J9, Canada)

  • Eric Prescott-Gagnon

    (Element AI, Montréal, Québec H2S 3G9, Canada)

  • Emmanuel Sabourin

    (Element AI, Montréal, Québec H2S 3G9, Canada)

  • Louis-Martin Rousseau

    (Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada)

Abstract

Despite advanced supply chain planning and execution systems, manufacturers and distributors tend to observe service levels below their targets, owing to different sources of uncertainty and risks. These risks, such as drastic changes in demand, machine failures, or systems not properly configured, can lead to planning or execution issues in the supply chain. It is too expensive to have planners continually track all situations at a granular level to ensure that no deviations or configuration problems occur. We present a machine learning system that predicts service-level failures a few weeks in advance and alerts the planners. The system includes a user interface that explains the alerts and helps to identify failure fixes. We conducted this research in cooperation with Michelin. Through experiments carried out over the course of four phases, we confirmed that machine learning can help predict service-level failures. In our last experiment, planners were able to use these predictions to make adjustments on tires for which failures were predicted, resulting in an improvement in the service level of 10 percentage points. Additionally, the system enabled planners to identify recurrent issues in their supply chain, such as safety-stock computation problems, impacting the overall supply chain efficiency. The proposed system showcases the importance of reducing the silos in supply chain management.

Suggested Citation

  • Gabrielle Gauthier Melançon & Philippe Grangier & Eric Prescott-Gagnon & Emmanuel Sabourin & Louis-Martin Rousseau, 2021. "A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains," Interfaces, INFORMS, vol. 51(3), pages 200-212, May.
    • Handle: RePEc:inm:orinte:v:51:y:2021:i:3:p:200-212
    DOI: 10.1287/inte.2020.1055
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    References listed on IDEAS

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    1. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
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    4. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    5. Sarker, Ruhul & Essam, Daryl, 2017. "A quantitative model for disruption mitigation in a supply chainAuthor-Name: Paul, Sanjoy Kumar," European Journal of Operational Research, Elsevier, vol. 257(3), pages 881-895.
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    Cited by:

    1. Liu, Feng & Long, Xiao & Dong, Lin & Fang, Mingjie, 2023. "What makes you entrepreneurial? Using machine learning to investigate the determinants of entrepreneurship in China," China Economic Review, Elsevier, vol. 81(C).

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