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A Decision Support System for Better Qualitative Supply Chain Diagnoses

Abstract : In the current supply chain world where instability and variability are the norm, being able to efficiently identify/diagnose the root causes of non-performance is of prime importance. Although numerous methods exist to support quantitative diagnosis step, there are very few materials regarding the qualitative dimension of diagnosis. Additionally, the rare existing methods are very time-intensive, need scarce expertise and often produce poor results. In a such context, the problem is how to make supply chain qualitative diagnoses impactful and fast. Practically, this paper develops a business process and its associated knowledge-based system, inspired by the theory of constraints’ thinking processes approach, to effectively support practitioners in their qualitative diagnosis step. A set of real industrial application cases is analyzed to discuss the implications of the contribution. It notably demonstrates that the proposal supports both increasing the impact of the diagnosis and reducing the time of the process by almost 80%.
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https://hal-mines-albi.archives-ouvertes.fr/hal-03775866
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Submitted on : Tuesday, September 27, 2022 - 12:33:54 PM
Last modification on : Friday, September 30, 2022 - 2:35:45 PM

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Anthony Fouque, Matthieu Lauras, Frederick Benaben, Hamideh Afsarmanesh. A Decision Support System for Better Qualitative Supply Chain Diagnoses. PRO-VE 2022 - 23rd IFIP WG 5.5 Working Conference on Virtual Enterprises, Sep 2022, Lisbonne, Portugal. 434-446 (chap. 35), ⟨10.1007/978-3-031-14844-6_35⟩. ⟨hal-03775866⟩

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