An Analytic Network Process Model to Support Decision Making in a Pharmaceutical Supply Chain

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Abstract

This paper intends to design an Analytic Network Process model to assists managers of different pharmaceutical supply chain entities in identifying and prioritize the management practices they use or should use to achieve a competitive position in the market. The model involves interactions between various criteria for management practice selection. To this purpose a set of clusters and elements related to a Portuguese pharmaceutical supply chain are identified to compose the model and pairwise compared with respect to a given factor. The results obtained from the proposed model show that the most suitable management practice for the pharmaceutical supply chain studied is “Just In Time” if the lean supply chain management paradigm was implemented and “Promoting visibility throughout the supply chain” if was the agile paradigm. Concerning the key performance measures, “On-Time-In-Full delivery” and “Inventory value” are selected respectively for lean and agile supply chain management.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2013)
Place of PublicationNew York
PublisherIEEE
Pages1612 - 1616
Number of pages5
DOIs
Publication statusPublished - 2013
EventThe IEEE International Conference on Industrial Engineering and Engineering Management -
Duration: 1 Jan 2013 → …

Conference

ConferenceThe IEEE International Conference on Industrial Engineering and Engineering Management
Period1/01/13 → …

Keywords

  • Supply chain management
  • agile supply chain
  • lean supply chain
  • ANP model
  • pharmaceutical supply chain

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  • Cite this

    Machado, V. H. A. D. C., Barroso, A. P. F., & Machado, V. A. C. (2013). An Analytic Network Process Model to Support Decision Making in a Pharmaceutical Supply Chain. In 2013 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2013) (pp. 1612 - 1616). New York: IEEE. https://doi.org/10.1109/IEEM.2013.6962682