The operational management of cold chain logistics has an important impact on the quality of cold chain products, but the service delivery process is subject to a series of potential problems such as product loss and cold storage temperature in the actual operation.
In this paper, the whole cold chain logistics system and risk events are analyzed. A Bayesian network is used for modeling and simulation to identify the main influencing factors and to conduct a sensitivity analysis of the main factors.
It is found that the operation of cold chain logistics systems can be divided into four links according to the degree of influence as follows: transportation and distribution, processing and packaging, information processing and warehousing. Transportation and distribution is the most influential factor of system failure, and extreme weather is the most risky event. At the same time, the four risk events that have the greatest impact on the operation of the cold chain system are in descending order: transportation equipment failure, extreme weather, unqualified pre-cooling and violation operation.
Therefore, enterprises should develop appropriate interventions for securing the transportation services, design strategies to deal with extreme weather conditions prior to and in the early stage of product delivery, and prepare additional effective measures for managing emergency events.
The authors are grateful to the case company for permitting and supporting this research. This study was financially supported by HRSA, US DHHS (Grant number H49MC00068), the National Natural Science Foundation of China (Grant number 71263040), China Manufacturing Development Research Institute’s Opening Project in 2018 (SK20180090-1) Key Project of National Social and Scientific Fund Program (18ZDA052); Project of National Social and Scientific Fund Program (17BGL142).
Zheng, C., Peng, B. and Wei, G. (2020), "Operational risk modeling for cold chain logistics system: a Bayesian network approach", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-10-2019-0653Download as .RIS
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