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Decision support for lead time and demand variability reduction

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  • Fang, Xin
  • Zhang, Cheng
  • Robb, David J.
  • Blackburn, Joseph D.

Abstract

Companies undertaking operations improvement in supply chains face many alternatives. This work seeks to assist practitioners to prioritize improvement actions by developing analytical expressions for the marginal values of three parameters – (i) lead time mean, (ii) lead time variance, and (iii) demand variance – which measure the marginal cost of an incremental change in a parameter. The relative effectiveness of reducing lead time mean versus lead time variance is captured by the ratio of the marginal value of lead time mean to that of lead time variance. We find that this ratio strongly depends on whether the lead time mean and variance are independent or correlated. We illustrate the application of the results with a numerical example from an industrial setting. The insights can help managers determine the optimal investment decision to modify demand and supply characteristics in their supply chain, e.g., by switching suppliers, factory layout, or investing in information systems.

Suggested Citation

  • Fang, Xin & Zhang, Cheng & Robb, David J. & Blackburn, Joseph D., 2013. "Decision support for lead time and demand variability reduction," Omega, Elsevier, vol. 41(2), pages 390-396.
    • Handle: RePEc:eee:jomega:v:41:y:2013:i:2:p:390-396
    DOI: 10.1016/j.omega.2012.03.005
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    3. Li, Xiaoming, 2020. "Valuing lead-time and its variance in batch-ordering inventory policies," International Journal of Production Economics, Elsevier, vol. 228(C).
    4. Dominguez, Roberto & Cannella, Salvatore & Ponte, Borja & Framinan, Jose M., 2020. "On the dynamics of closed-loop supply chains under remanufacturing lead time variability," Omega, Elsevier, vol. 97(C).
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    6. Ding, Yi & Gao, Xing & Huang, Chao & Shu, Jia & Yang, Donghui, 2018. "Service competition in an online duopoly market," Omega, Elsevier, vol. 77(C), pages 58-72.
    7. Belvedere, Valeria & Goodwin, Paul, 2017. "The influence of product involvement and emotion on short-term product demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 652-661.
    8. Williams, Brent D. & Waller, Matthew A. & Ahire, Sanjay & Ferrier, Gary D., 2014. "Predicting retailer orders with POS and order data: The inventory balance effect," European Journal of Operational Research, Elsevier, vol. 232(3), pages 593-600.
    9. Heydari, Jafar & Mahmoodi, Mansour & Taleizadeh, Ata Allah, 2016. "Lead time aggregation: A three-echelon supply chain model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 215-233.
    10. Tyworth, John E., 2018. "A note on lead-time paradoxes and a tale of competing prescriptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 139-150.
    11. Tyworth, John E. & Saldanha, John, 2014. "The lead-time reliability paradox and inconsistent value-of-reliability estimates," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 76-85.
    12. Juan Manuel Izar Landeta & Carmen Berenice Ynzunza Cortés & Orlando Guarneros García, 2016. "Lead time demand variability, safety stock and the inventory cost," Contaduría y Administración, Accounting and Management, vol. 61(3), pages 499-513, Julio-Sep.
    13. Pedro Domingos Antoniolli, 2016. "Information Technology Framework for Pharmaceutical Supply Chain Demand Management: a Brazilian Case Study," Brazilian Business Review, Fucape Business School, vol. 13(2), pages 27-55, March.
    14. Sarkar, Biswajit & Kar, Sumi & Basu, Kajla & Seo, Yong Won, 2023. "Is the online-offline buy-online-pickup-in-store retail strategy best among other product delivery strategies under variable lead time?," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

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