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Project network-oriented materials management policy for complex projects: a Fuzzy Set Theoretic approach

Author

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  • Vijaya Dixit
  • Rajiv Srivastava K.
  • Atanu Chaudhuri

Abstract

This work devises a materials management policy integrated with project network characteristics of complex projects. It proposes a relative quantitative measure, overall criticality (OC), for prioritisation of items based on penalty incurred due to their non-availability. In complex projects, practicing managers find it difficult to measure OC of items because of the subjective factors and intractable nature of penalties involved. However, using their experience, they can linguistically identify the antecedents and relate them to consequent OC. This work adopts Fuzzy Set Theory to capture managerial tacit knowledge which provides them enough flexibility to provide information in real form. Computed OC values can be used for items prioritisation and as shortage cost coefficient in inventory models. The revised materials management policy was applied to a shipbuilding project. OC values were analysed to justify the importance of incorporating project network characteristics and potential cost savings were calculated.

Suggested Citation

  • Vijaya Dixit & Rajiv Srivastava K. & Atanu Chaudhuri, 2015. "Project network-oriented materials management policy for complex projects: a Fuzzy Set Theoretic approach," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 2904-2920, May.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:10:p:2904-2920
    DOI: 10.1080/00207543.2014.948971
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    Cited by:

    1. da Cunha, Richard Alex & Rangel, Luís Alberto Duncan & Rudolf, Christian A. & Santos, Luiza dos, 2022. "A decision support approach employing the PROMETHEE method and risk factors for critical supply assessment in large-scale projects," Operations Research Perspectives, Elsevier, vol. 9(C).
    2. Fausto Pedro García Márquez & Isaac Segovia Ramírez & Alberto Pliego Marugán, 2019. "Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing," Energies, MDPI, vol. 12(9), pages 1-17, May.

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