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Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company

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Abstract

Performance impacts of ordering and production control policies in the presence of capacity disruptions are studied on the real-life example of a retail supply chain with product perishability considerations. Constraints on product perishability typically result in reductions in safety stock and increases in transportation frequency. Consideration of the production capacity disruption risks may lead to safety stock increases. This trade-off is approached with the help of a simulation model that is used to compare supply chain performance impacts with regard to coordinated and non-coordinated ordering and production control policies. Real data of a fast moving consumer goods company is used to perform simulations and to derive novel managerial insights and practical recommendations on inventory, on-time delivery and service level control. In particular, for the first time, the effect of ‘postponed redundancy’ has been observed. Moreover, a coordinated production–ordering contingency policy in the supply chain within and after the disruption period has been developed and tested to reduce the negative impacts of the ‘postponed redundancy’. The lessons learned from experiments provide evidence that a coordinated policy is advantageous for inventory dynamics stabilization, improvement in on-time delivery, and variation reduction in customer service level.

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Acknowledgements

The author thanks Guest Editor and anonymous reviewers for their valuable comments that greatly improved the manuscript. We cordially thank Mr. John Davis, lecturer in operations management and research associate at Berlin School of Economics and Law for a thorough proof-reading of this manuscript.

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Correspondence to Dmitry Ivanov.

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Ivanov, D., Rozhkov, M. Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company. Ann Oper Res 291, 387–407 (2020). https://doi.org/10.1007/s10479-017-2643-8

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