With large volume of product flows and complex supply chain processes, more data than ever before is being generated and collected in supply chains through various tracking and sensory technologies.The purpose of this study is to show a potential scenario of using a prototype tracking tool that facilitate the utilization of sensor data, which is often unstructured and enormous in nature, to support supply chain decisions. The research investigates the potential benefits of the chilled food chain management innovation through sensor data driven pricing decisions. Data generated and recorded through the sensor network are used to predict the remaining shelf-life of perishable foods. Numerical analysis is conducted to examine the benefit of proposed approach under various operational situations and product features. The research findings demonstrate a way of modelling pricing and potential of performance improvement in chilled food chains to provide a vision of smooth transfer and implementation of the sensor data driven supply chain management. The research finding would encourage firms in the food industry to explore innovation opportunities from big data and develop proper data driven strategies to improve their competitiveness.
International audienceThe food industry has been under pressure of improving food safety, implementing efficient risk management, and managing quality ‘from farm to fork'. Individual parties in a food supply chain must make appropriate strategic and operational adjustments, enhance product quality and increase operational efficiency and profits. In this paper, an optimisation model is developed integrating traceability initiatives with operation factors to achieve desired product quality and minimum impact of product recall in an economic manner. A case study with numerical example is provided to illustrate the theory. Sensitivity analyses are conducted. A comparison between the proposed model and the existing model operating in the case company is also presented
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