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Exploring a two-product unreliable manufacturing system as a capacity constraint for a two-echelon supply chain dynamic problem

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  • Antonio Costa
  • Salvatore Cannella
  • Roberto R. Corsini
  • Jose M. Framinan
  • Sergio Fichera

Abstract

In this paper, we investigate a two-echelon, two-product Supply Chain (SC) inspired by a real-world production/distribution firm, in which the product change-over time, necessary to switch from a product to another, induces a variable capacity in the factory. Such a varying production capacity is further exacerbated by the machine breakdowns that may occur in the manufacturing system. Since the two products share the same production system, a production planning rule has to be executed to decide the change-over, i.e. to select the product to be manufactured over time. An extended experimental campaign has been performed to investigate how the fill rate and the standard deviation of inventories vary as a series of operational and tactical parameters changes. Several ANOVA analyses revealed a strong interaction between the production planning model and the adopted smoothing replenishment strategy, which remarkably affects the performance of the SC. Interestingly, a higher value of the proportional controller negatively affects the service levels since the adopted production planning policy, under these circumstances, tends to encourage longer production runs for a certain product and, consequently, persistent stock outs for the other one.

Suggested Citation

  • Antonio Costa & Salvatore Cannella & Roberto R. Corsini & Jose M. Framinan & Sergio Fichera, 2022. "Exploring a two-product unreliable manufacturing system as a capacity constraint for a two-echelon supply chain dynamic problem," International Journal of Production Research, Taylor & Francis Journals, vol. 60(3), pages 1105-1133, February.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:3:p:1105-1133
    DOI: 10.1080/00207543.2020.1852480
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    Cited by:

    1. Behnamfar, Reza & Sajadi, Seyed Mojtaba & Tootoonchy, Mahshid, 2022. "Developing environmental hedging point policy with variable demand: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 254(C).

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