2024
DOI: 10.18280/isi.290120
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Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments

Nawfal Berbiche,
Mustapha Hlyal,
Jamila El Alami

Abstract: In light of recent mutations and economic volatility stemming from unforeseen global events and increasing security concerns, supply chains are confronted with the complex challenge of fulfilling uncertain customer demands while ensuring sustained value addition. This study introduces a novel approach utilizing fuzzy logic decision-making automation to address and mitigate the impact of current disruptions. By employing if-then scenarios, this methodology facilitates the generation of more accurate predictions… Show more

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Cited by 2 publications
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“…A comparative study of LINGO integrated and decoupled approaches' outputs of the model would be an interesting avenue of reflection, that can be extended with predictive analytics, especially demand forecasting like the Mamdanifuzzy logic decision model just recently proposed by three authors of the present article in [87] and last-mile logistics [88]. Indeed, we point out stimulating research perspectives that can enhance the proposed supply chain planning model, including sustainability-based risk pooling [89] and fuzzy optimization to satisfy and adapt scenario-based demand for uncertainty mitigation in a real-world environment.…”
Section: Conclusion and Future Researchmentioning
confidence: 96%
“…A comparative study of LINGO integrated and decoupled approaches' outputs of the model would be an interesting avenue of reflection, that can be extended with predictive analytics, especially demand forecasting like the Mamdanifuzzy logic decision model just recently proposed by three authors of the present article in [87] and last-mile logistics [88]. Indeed, we point out stimulating research perspectives that can enhance the proposed supply chain planning model, including sustainability-based risk pooling [89] and fuzzy optimization to satisfy and adapt scenario-based demand for uncertainty mitigation in a real-world environment.…”
Section: Conclusion and Future Researchmentioning
confidence: 96%