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Congress of APDIO, the Portuguese Operational Research Society

IO 2018: Operational Research pp 143–157Cite as

Improving Inventory Management in an Automotive Supply Chain: A Multi-objective Optimization Approach Using a Genetic Algorithm

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 278))

Abstract

Inventory management represents a cornerstone inherent to any supply chain, regardless of industry type. Nevertheless, uncertainty phenomena related to demand and supply can induce overstock or even inventory stock-outs occurrences which, in turn, jeopardize one of the major principles of supply chain management: deliver the right product at the right place, at the right time and to the right cost. This situation may also be aggravated in automotive supply chains, due to their complexity in terms of entities involved. This research paper explores a multi-objective optimization model and applies it to a real industrial company, to address an inventory management problem. Moreover, a genetic algorithm is used to determine solutions corresponding to the order size and to a safety factor system. The obtained results are compared to the current strategy adopted by the company. At this point, the advantages and the drawbacks of the model implementation are assessed. Based on a set of logistic performance indicators, it is showed that the adoption of a smaller order size is potentially beneficial to the overall levels of inventory and to the value of inventory on–hand, without compromising the service level. Assertively, the proposed model reveals to be an useful tool to practitioners involved in automotive electronic supply chains.

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Acknowledgements

The authors would like to acknowledge the comments and suggestions from the two reviewers, which improved the quality of the paper. This work has been supported by ALGORITMI R&D Center, under COMPETE: POCI-01-0145-FEDER-007043 and FCT–Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

Author information

Authors and Affiliations

  1. Algoritmi R&D Center, University of Minho, Braga, Portugal

    João N. C. Gonçalves, M. Sameiro Carvalho & Lino Costa

  2. Department of Production and Systems, University of Minho, Braga, Portugal

    João N. C. Gonçalves, M. Sameiro Carvalho & Lino Costa

Authors
  1. João N. C. Gonçalves
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  2. M. Sameiro Carvalho
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  3. Lino Costa
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Corresponding author

Correspondence to João N. C. Gonçalves .

Editor information

Editors and Affiliations

  1. Faculty of Economics/CeBER, University of Coimbra, Coimbra, Portugal

    Maria João Alves

  2. Department of Mathematics/CeDRI-IPB and LIAAD-INESC TEC, Polytechnic Institute of Bragança, Bragança, Portugal

    João Paulo Almeida

  3. Department of Industrial Engineering and Management/INESC TEC, University of Porto, Porto, Portugal

    José Fernando Oliveira

  4. Department of Mathematics/LIAAD-INESC TEC, University of Porto, Porto, Portugal

    Alberto Adrego Pinto

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Gonçalves, J.N.C., Carvalho, M.S., Costa, L. (2019). Improving Inventory Management in an Automotive Supply Chain: A Multi-objective Optimization Approach Using a Genetic Algorithm. In: Alves, M., Almeida, J., Oliveira, J., Pinto, A. (eds) Operational Research. IO 2018. Springer Proceedings in Mathematics & Statistics, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-10731-4_10

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