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Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain

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  • Dong Li
  • Xiaojun Wang

Abstract

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 utilisation 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.

Suggested Citation

  • Dong Li & Xiaojun Wang, 2017. "Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5127-5141, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:17:p:5127-5141
    DOI: 10.1080/00207543.2015.1047976
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    References listed on IDEAS

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    6. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    7. Damianos P. Sakas & Ioannis Dimitrios G. Kamperos & Panagiotis Reklitis, 2021. "Estimating Risk Perception Effects on Courier Companies’ Online Customer Behavior during a Crisis, Using Crowdsourced Data," Sustainability, MDPI, vol. 13(22), pages 1-26, November.
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    9. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    10. Mohamed Ben-Daya & Elkafi Hassini & Zied Bahroun & Hafsa Saeed, 2023. "Optimal pricing in the presence of IoT investment and quality-dependent demand," Annals of Operations Research, Springer, vol. 324(1), pages 869-892, May.
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    13. Ahmed Zainul Abideen & Veera Pandiyan Kaliani Sundram & Jaafar Pyeman & Abdul Kadir Othman & Shahryar Sorooshian, 2021. "Food Supply Chain Transformation through Technology and Future Research Directions—A Systematic Review," Logistics, MDPI, vol. 5(4), pages 1-24, November.

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