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MRO Inventory Demand Forecast Using Support Vector Machine – A Case Study

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Industrial Engineering and Operations Management (IJCIEOM 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 400))

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Abstract

Today’s world is living in the age of digital transformation, the so-called Industry 4.0, in which technological advances have revolutionized the decision-making process in supply chain management. In this domain, inventory management can represent 50% of all organizational costs, and still a challenging task to keep the trade-off between maintaining inventory levels as low as possible, meeting clients’ demands, and maintaining satisfactory service levels. Forecasting the MRO inventory demand is even a more difficult task. To address this problem, machine learning (ML) applications, which deal well with nonlinear data, can predict irregular and intermittent demand with better accuracy than traditional approaches. This study employed the Support Vector Machine model to predict maintenance parts demand in a railroad logistic operator case study. This technique can deal with the nonlinear data encompassed by demand variations, avoid overfitting, and produce very accurate classifiers. Results indicated a considerable improvement in the demand forecast performance of the selected SKUs. This model can enhance the reliability of the purchasing and stock maintenance process and generate financial gains by reducing the need for large volumes of safety stock and greater assertiveness in meeting internal demands. It also contributes by showing a real case with an ML approach to predict inventory demands.

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Acknowledgments

This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil – CAPES [Finance Code 001] & [Grant Number 88881.198822/2018-01]; Brazilian National Council for Scientific and Technological Development – CNPq [311757/2018-9]; Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro – FAPERJ [Grant number E-26/201.363/2021; E26/211.298/2021].

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Authors and Affiliations

  1. Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

    Guilherme Henrique de Paula Vidal, Rodrigo Goyannes Gusmão Caiado , Luiz Felipe Scavarda & Renan Silva Santos

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  1. Guilherme Henrique de Paula Vidal
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  2. Rodrigo Goyannes Gusmão Caiado
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  3. Luiz Felipe Scavarda
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  4. Renan Silva Santos
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Correspondence to Guilherme Henrique de Paula Vidal .

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Editors and Affiliations

  1. Engineering, Universidad Anáhuac, Anáhuac Huixquilucan, Mexico

    Victor Manuel López Sánchez

  2. Mechanical Engineering, Federal University of Bahia, Salvador, Bahia, Brazil

    Francisco Gaudêncio Mendonça Freires

  3. Industrial Engineering and Management, Universidade Lusófona, Lisbon, Portugal

    João Carlos Gonçalves dos Reis

  4. DEGEIT, University of Aveiro, Aveiro, Portugal

    Joana Maria Costa Martins das Dores

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de Paula Vidal, G.H., Caiado , R.G.G., Scavarda, L.F., Santos, R.S. (2022). MRO Inventory Demand Forecast Using Support Vector Machine – A Case Study. In: López Sánchez, V.M., Mendonça Freires, F.G., Gonçalves dos Reis, J.C., Costa Martins das Dores, J.M. (eds) Industrial Engineering and Operations Management. IJCIEOM 2022. Springer Proceedings in Mathematics & Statistics, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-031-14763-0_18

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