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A Structured Review of Quantitative Models of the Pharmaceutical Supply Chain

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  • Carlos Franco
  • Edgar Alfonso-Lizarazo

Abstract

The aim of this review is to identify and provide a structured overview of quantitative models in the pharmaceutical supply chain, a subject not exhaustively studied in the previous reviews on healthcare logistics related mostly to quantitative models in healthcare or logistics studies in hospitals. The models are classified into three categories of classification: network design, inventory models, and optimization of a pharmaceutical supply chain. A taxonomy for each category is shown describing the principal features of each echelon included in the review; this taxonomy allows the readers to identify easily a paper based on the actors of the pharmaceutical supply chain. The search process included research articles published in the databases between 1984 and November 2016. In total 46 studies were included. In the review process we found that in the three fields the most common source of uncertainty used is the demand in the 56% of the cases. Within the review process we can conclude that most of the articles in the literature are focused on the optimization of the pharmaceutical supply chain and inventory models but the field on supply chain network design is not deeply studied.

Suggested Citation

  • Carlos Franco & Edgar Alfonso-Lizarazo, 2017. "A Structured Review of Quantitative Models of the Pharmaceutical Supply Chain," Complexity, Hindawi, vol. 2017, pages 1-13, December.
    • Handle: RePEc:hin:complx:5297406
    DOI: 10.1155/2017/5297406
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    References listed on IDEAS

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    Cited by:

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    2. Firoz Ahmad, 2022. "Interactive neutrosophic optimization technique for multiobjective programming problems: an application to pharmaceutical supply chain management," Annals of Operations Research, Springer, vol. 311(2), pages 551-585, April.
    3. Shabnam Rekabi & Ali Ghodratnama & Amir Azaron, 2022. "Designing pharmaceutical supply chain networks with perishable items considering congestion," Operational Research, Springer, vol. 22(4), pages 4159-4219, September.

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