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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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    1. Chatwin, Richard E., 2000. "Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices," European Journal of Operational Research, Elsevier, vol. 125(1), pages 149-174, August.
    2. Wen Zhao & Yu-Sheng Zheng, 2000. "Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand," Management Science, INFORMS, vol. 46(3), pages 375-388, March.
    3. Wedad Elmaghraby & P{i}nar Keskinocak, 2003. "Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions," Management Science, INFORMS, vol. 49(10), pages 1287-1309, October.
    4. Wang, Xiaojun & Li, Dong, 2012. "A dynamic product quality evaluation based pricing model for perishable food supply chains," Omega, Elsevier, vol. 40(6), pages 906-917.
    5. Herbon, Avi & Levner, Eugene & Cheng, T.C.E., 2014. "Perishable inventory management with dynamic pricing using time–temperature indicators linked to automatic detecting devices," International Journal of Production Economics, Elsevier, vol. 147(PC), pages 605-613.
    6. Abad, P. L. & Aggarwal, Vijay, 2005. "Incorporating transport cost in the lot size and pricing decisions with downward sloping demand," International Journal of Production Economics, Elsevier, vol. 95(3), pages 297-305, March.
    7. Rong, Aiying & Akkerman, Renzo & Grunow, Martin, 2011. "An optimization approach for managing fresh food quality throughout the supply chain," International Journal of Production Economics, Elsevier, vol. 131(1), pages 421-429, May.
    8. van Donselaar, K. & van Woensel, T. & Broekmeulen, R. & Fransoo, J., 2006. "Inventory control of perishables in supermarkets," International Journal of Production Economics, Elsevier, vol. 104(2), pages 462-472, December.
    9. Fujiwara, Okitsugu & Perera, U. L. J. S. R., 1993. "EOQ models for continuously deteriorating products using linear and exponential penalty costs," European Journal of Operational Research, Elsevier, vol. 70(1), pages 104-114, October.
    10. Praveen K. Kopalle & Ambar G. Rao & João L. Assunção, 1996. "Asymmetric Reference Price Effects and Dynamic Pricing Policies," Marketing Science, INFORMS, vol. 15(1), pages 60-85.
    11. Chen, Xu & Wang, Xiaojun, 2015. "Free or bundled: Channel selection decisions under different power structures," Omega, Elsevier, vol. 53(C), pages 11-20.
    12. Bhattacharjee, Sudip & Ramesh, R., 2000. "A multi-period profit maximizing model for retail supply chain management: An integration of demand and supply-side mechanisms," European Journal of Operational Research, Elsevier, vol. 122(3), pages 584-601, May.
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    3. Kabadurmus, Ozgur & Kayikci, Yaşanur & Demir, Sercan & Koc, Basar, 2023. "A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
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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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