2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 0 , Issue 2 , PP: 83-89, 2019 | Cite this article as | XML | Html | PDF
Rehab Mohamed 1 * , Mahmoud Ismail 2
In today’s ever-changing world the ability of supply chains to withstand disruptions is crucial for businesses to maintain operations. This paper focuses on supply chain resilience, from an Operations Research perspective exploring how theoretical frameworks and practical applications work together to strengthen supply chains against events. By analyzing a dataset related to Makeup product supply chains this study demonstrates the effectiveness of Long Short-Term Memory (LSTM) networks in capturing time patterns and highlights the importance of data normalization in improving accuracy. Comparing models trained on normalized and unnormalized data provides insights into the significance of preprocessing techniques in predicting outcomes within the Fashion and Beauty industry. Additionally, this study combines theory with real-world case studies underscoring the importance of risk management, adaptive decision-making, and resilient network design. With the integration of methodological consistency, and applicability, we demonstrate the significance of our approach in sustaining supply chain resilience against disruptive events.
Operation Research , Supply Chain , Business Intelligence , Operational Management , Decision-Making.
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