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Methodological analysis of supply chains management applications

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  • Di Giacomo, Laura
  • Patrizi, Giacomo

Abstract

Formal modelling may be used to express management operational plans to achieve the desired normative objectives of firms. The plans so formulated should be demonstrably optimal with regard to certain specific objectives assumed by top management and ought to provide accurate results, when enacted, with a given tolerance at a prespecified probability. Modelling Decision Support Systems is based on various alternative methodologies: managerial-situational, interpretative or formal-deductive, which affect the results and precision obtainable. The third approach requires dynamical nonlinear stochastic modelling to determine precise Supply Chain Management (SCM) plans, without incurring in the limitations that may characterize the former approaches. The aim of this paper is to examine different management methodologies, to determine the most appropriate implementation for accurate SCM plans. Two well known SCM implementations, the bullwhip effect and the collaborative planning and extensions will be examined under different methodologies for clarity and to verify their limitations.

Suggested Citation

  • Di Giacomo, Laura & Patrizi, Giacomo, 2010. "Methodological analysis of supply chains management applications," European Journal of Operational Research, Elsevier, vol. 207(1), pages 249-257, November.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:1:p:249-257
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    References listed on IDEAS

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    1. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.
    2. Chou, Jui-Sheng & Ongkowijoyo, Citra Satria, 2015. "Reliability-based decision making for selection of ready-mix concrete supply using stochastic superiority and inferiority ranking method," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 29-39.

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