Spare Parts Management in the Automotive Industry Considering Sustainability

  • David Alejandro Baez Diaz
  • Sophie Hennequin
  • Daniel RoyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


Spare parts are a fundamental part of the automotive industry, even if they are intended for out of series market and aftermarket products. Throughout this study, a process for formulating an inventory model for spare parts is presented through this paper, a proposal for an inventory management system applied to automotive spare parts and based on forecast and simulation is presented. The objective is to improve the implementation of an inventory system in order to reduce transportation, storage and production costs by studying the behavior of demand, the applicability of an inventory management policy and the use of simulations to test the proposed results. This allows correcting parameters to avoid stock shortage and to integrate sustainable paradigms.


Inventory management system Demand forecast Simulation based on mathematical model 


  1. 1.
    Alfarez, H., Turnadi, R.: General model for single-item lot sizing with multiple suppliers, quantity discounts, and backordering. Proc. CIRP 56, 199–202 (2016)Google Scholar
  2. 2.
    Bussay, A., Van der Velde, M., Fumagalli, D., Seguini. L.: Improving operational maize yield forecasting in Hungary. Agric. Syst. 141, 94–106 (2015)Google Scholar
  3. 3.
    Hubert, T.: Prévision de la demande et pilotage des flux en approvisionnement lointain. Ph.D. Thesis. École Centrale Paris (2013). (Chapitre 1. Pages 10–12 – in French)Google Scholar
  4. 4.
    Gupta, S., Dangayach, G., Kumar, A., Rao, P.: Analytic hierarchy process (AHP) model for evaluating sustainable manufacturing practices in Indian electrical panel industries. Proc. Soc. Behav. Sci. 189, 208–216 (2015)Google Scholar
  5. 5.
    Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)Google Scholar
  6. 6.
    Rahjans, N., Samak, S.: Determination of optimum inventory model for minimizing total inventory cost. Proc. Eng. 51, 803–809 (2013)Google Scholar
  7. 7.
    Erol, S., Jäger, A. Hold, P. Ott, K., Sihn, W.: Tangible industry 4.0: a scenario-based approach to learning for the future of production. Proc. CIRP 54, 13–18 (2016)Google Scholar
  8. 8.
    Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D.: An Introduction to Management Science: Quantitative Approaches to decision making, p. 912, 15th edn. Cengage Learning (2018)Google Scholar
  9. 9.
    Hennequin, S., Ramirez Restrepo L.M.: Fuzzy model of a joint maintenance and production control under sustainability constraints. In: Proceedings of the 8th IFAC MIM 2016 Conference, Troyes, France (2016)Google Scholar
  10. 10.
    Hu, H., Zhou, M.C.: A Petri net-based discrete-event control of automated manufacturing systems with assembly operations. IEEE Trans. Control Syst. Technol. 23(2), 513–524 (2015)Google Scholar
  11. 11.
    Sanjoy, K.: Determination of exponential smoothing constant to minimize mean square error and mean absolute deviation. Global J. Res. Eng. 11(3), 31–33 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • David Alejandro Baez Diaz
    • 1
  • Sophie Hennequin
    • 2
  • Daniel Roy
    • 2
    Email author
  1. 1.LARIS LaboratoryAngers UniversityAngersFrance
  2. 2.LGIPM LaboratoryLorraine UniversityMetzFrance

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