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Impact of lead time variability in supply chain risk management

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  • Bandaly, Dia
  • Satir, Ahmet
  • Shanker, Latha

Abstract

In this article, we study the impact of lead time variability on the performance of supply chain risk management in the beer industry. The stakeholders considered are the empty aluminum can supplier, the brewery and the distributor. The stochastic lead time model developed is an extension of the previously developed base model (with deterministic lead time) under which commodity price risk and demand uncertainty are managed via an integrated risk management approach using operational methods and financial derivatives. Simulation-based optimization is used to model and analyze such a complex system. We find that lead time variability does not always deteriorate the supply chain performance. Only high levels of lead time variability are found to justify more the need for a coordinated supply chain. The findings also reveal the need for using different hedging strategies to manage products flows across the supply chain under stochastic lead time versus the deterministic case. Managerial insights for the supply chain studied are argued based on experimental design findings.

Suggested Citation

  • Bandaly, Dia & Satir, Ahmet & Shanker, Latha, 2016. "Impact of lead time variability in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 180(C), pages 88-100.
    • Handle: RePEc:eee:proeco:v:180:y:2016:i:c:p:88-100
    DOI: 10.1016/j.ijpe.2016.07.014
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    4. Polo, Andrés & Peña, Numar & Muñoz, Dairo & Cañón, Adrián & Escobar, John Willmer, 2019. "Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria," Omega, Elsevier, vol. 88(C), pages 110-132.
    5. Ponte, Borja & Costas, José & Puche, Julio & Pino, Raúl & de la Fuente, David, 2018. "The value of lead time reduction and stabilization: A comparison between traditional and collaborative supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 165-185.
    6. Sanjoy Kumar Paul & Ruhul Sarker & Daryl Essam & Paul Tae-Woo Lee, 2019. "A mathematical modelling approach for managing sudden disturbances in a three-tier manufacturing supply chain," Annals of Operations Research, Springer, vol. 280(1), pages 299-335, September.
    7. Chowdhury, Nighat Afroz & Ali, Syed Mithun & Mahtab, Zuhayer & Rahman, Towfique & Kabir, Golam & Paul, Sanjoy Kumar, 2019. "A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 102-113.
    8. Ben-Ammar, Oussama & Dolgui, Alexandre & Wu, Desheng Dash, 2018. "Planned lead times optimization for multi-level assembly systems under uncertainties," Omega, Elsevier, vol. 78(C), pages 39-56.
    9. Romero-Silva, Rodrigo & Shaaban, Sabry & Marsillac, Erika & Hurtado, Margarita, 2018. "Exploiting the characteristics of serial queues to reduce the mean and variance of flow time using combined priority rules," International Journal of Production Economics, Elsevier, vol. 196(C), pages 211-225.
    10. Wu, Desheng & Olson, David L. & Wang, Shouyang, 2019. "Finance-operations interface mechanism and models," Omega, Elsevier, vol. 88(C), pages 1-3.
    11. M. A. Hoque, 2021. "An optimal solution policy to an integrated manufacturer-retailers problem with normal distribution of lead times of delivering equal and unequal-sized batches," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 483-512, June.

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