Abstract
With the increasing availability and use of the internet of things (IoT) sensors and platforms in food supply chains, there is an interest in studying their efficiency and impact on supply chain operations. To illustrate how classical operations management models could be affected by the introduction of IoT, we present an inventory model with deteriorating quality that can be monitored by IoT-enabled technologies with applications in the food supply chain. We develop a novel demand function that incorporates quality and its deterioration through three supply chain parties: producer, distributor and retailer. We use the model to analyse the impact of IoT on the retailer and distributor performance in a Stackelberg game context. The model can be used to justify IoT investment and to decide where to deploy IoT technologies in the supply chain. We find that the introduction of IoT technologies allows supply chain partners to make rational decisions that are quality-informed, reduce waste and improve revenues. As for where it is best to invest in IoT, we find that if only one party is to invest in IoT, it should be the retailer.
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References
Aday, M. S., & Yener, U. (2015). Assessing consumers’ adoption of active and intelligent packaging. British Food Journal, 117(1), 157–177.
Adenso-Díaz, B., Lozano, S., & Palacio, A. (2017). Effects of dynamic pricing of perishable products on revenue and waste. Applied Mathematical Modelling, 45, 148–164.
Akkerman, R., Farahani, P., & Grunow, M. (2010). Quality, safety and sustainability in food distribution: A review of quantitative operations management approaches and challenges. Or Spectrum, 32(4), 863–904.
Bahroun, Z., Ben-Daya, M., & Hassini, E. (2020). IoT quality-controlled demand pricing model in food supply chains. Smart and sustainable supply chain and logistics-trends, challenges, methods and best practices (pp. 381–394). Cham: Springer.
Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742.
Ben-Daya, M., Hassini, E., Bahroun, Z., & Banimfreg, B. (2020). The Role of Internet of Things in Food Supply Chain Quality Management: A Review. Quality Management Journal, 28(1), 17–40.
Blandon, J., Henson, S., & Cranfield, J. (2009). Small-scale farmer participation in new agri-food supply chains: Case of the supermarket supply chain for fruit and vegetables in Honduras. Journal of International Development: THe Journal of the Development Studies Association, 21(7), 971–984.
Bowman, P., Ng, J., Harrison, M., Lopez, T. S., & Illic, A. (2009). Sensor based condition monitoring. Building Radio Frequency IDentification for the Global Environment (Bridge) Euro RFID project.
Chen, X., Wu, S., Wang, X., & Li, D. (2019). Optimal pricing strategy for the perishable food supply chain. International Journal of Production Research, 57(9), 2755–2768.
Chung, J., & Li, D. (2014). A simulation of the impacts of dynamic price management for perishable foods on retailer performance in the presence of need-driven purchasing consumers. Journal of the Operational Research Society, 65(8), 1177–1188.
Dada, A., & Thiesse, F. (2008). Sensor applications in the supply chain: the example of quality-based issuing of perishables. The Internet of Things (pp. 140–154). Berlin: Springer.
Drago, E., Campardelli, R., Pettinato, M., & Perego, P. (2020). Innovations in smart packaging concepts for food: an extensive review. Foods, 9(11), 1628.
Fu, B., & Labuza, T. P. (1993). Shelf-life prediction: Theory and application. Food Control, 4(3), 125–133.
Giannakourou, M. C., & Taoukis, P. S. (2002). Systematic application of time temperature integrators as tools for control of frozen vegetable quality. Journal of Food Science, 67(6), 2221–2228.
Giannakourou, M. C., & Taoukis, P. S. (2003). Application of a TTI-based distribution management system for quality optimization of frozen vegetables at the consumer end. Journal of Food Science, 68(1), 201–209.
Gunders, D. (2012). Wasted: How America is losing up to 40 percent of its food from farm to fork to landfill. Natural Resources Defense Council, 26, 1–26.
Gustavsson, J., Cederberg, C., Sonesson, U., Van Otterdijk, R., & Meybeck, A. (2011). Global food losses and food waste. International Congress SAVE FOOD, Düsseldorf, Germany.
Haijema, R., & Minner, S. (2016). Stock-level dependent ordering of perishables: A comparison of hybrid base-stock and constant order policies. International Journal of Production Economics, 181, 215–225.
Heising, J. K., Claassen, G. D. H., & Dekker, M. (2017). Options for reducing food waste by quality-controlled logistics using intelligent packaging along the supply chain. Food Additives & Contaminants: Part A, 34(10), 1672–1680.
Herbon, A., Levner, E., & 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, 147, 605–613.
Iverson, J. (2021). Future of Packaging: Technology & Design in 10 Years and Beyond. Retrieved May 15, 2021 from https://tinyurl.com/4dcwzech
Kouki, C., Babai, M. Z., & Minner, S. (2018). On the benefit of dual-sourcing in managing perishable inventory. International Journal of Production Economics, 204, 1–17.
Kuswandi, B. (2017). Freshness sensors for food packaging. Reference Module in Food Science, 2017, 1–11.
Kuswandi, B., & Murdyaningsih, E. A. (2017). Simple on package indicator label for monitoring of grape ripening process using colorimetric pH sensor. Journal of Food Measurement and Characterization, 11(4), 2180–2194.
Labuza, T. P. (1982). Shelf-life dating of foods. Food & Nutrition Press, Inc..
Leithner, M., & Fikar, C. (2019). A simulation model to investigate impacts of facilitating quality data within organic fresh food supply chains. Annals of Operations Research, 1, 1–22.
Li, D., & Wang, X. (2017). Dynamic supply chain decisions based on networked sensor data: An application in the chilled food retail chain. International Journal of Production Research, 55(17), 5127–5141.
Liu, G., Zhang, J., & Tang, W. (2015). Joint dynamic pricing and investment strategy for perishable foods with price-quality dependent demand. Annals of Operations Research, 226(1), 397–416.
Lu, J., Zhang, J., & Zhang, Q. (2018). Dynamic pricing for perishable items with costly price adjustments. Optimization Letters, 12(2), 347–365.
Man, C. D., & Jones, A. A. (Eds.). (1994). Shelf life evaluation of foods. Blackie Academic & Professional.
Muth, M. K., Birney, C., Cuéllar, A., Finn, S. M., Freeman, M., Galloway, J. N., Meyer, E., et al. (2019). A systems approach to assessing environmental and economic effects of food loss and waste interventions in the United States. Science of the Total Environment, 685, 1240–1254.
Opara, U. L., & Mditshwa, A. (2013). A review on the role of packaging in securing food system: Adding value to food products and reducing losses and waste. African Journal of Agricultural Research, 8(22), 2621–2630.
Osvald, A., & Stirn, L. Z. (2008). A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food. Journal of Food Engineering, 85(2), 285–295.
Ozbilge, A., Hassini, E., & Parlar, M. (2019). Donate more to earn more. Submitted for publication.
Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131(1), 421–429.
Ross, D. F. (2016). Introduction to supply chain management technologies. St. Lucie Press.
Sahin, E., Babaï, M. Z., Dallery, Y., & Vaillant, R. (2007). Ensuring supply chain safety through time temperature integrators. The International Journal of Logistics Management.
Soysal, M., Bloemhof-Ruwaard, J. M., Meuwissen, M. P., & van der Vorst, J. G. (2012). A review on quantitative models for sustainable food logistics management. International Journal on Food System Dynamics, 3(2), 136–155.
Tsang, Y. P., Choy, K. L., Wu, C. H., Ho, G. T. S., & Lam, H. Y. (2019). An Internet of Things (IoT)-Based Shelf Life Management System in Perishable Food e-Commerce Businesses. In 2019 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1–8). IEEE.
Wang, X., & Li, D. (2012). A dynamic product quality evaluation based pricing model for perishable food supply chains. Omega, 40(6), 906–917.
Acknowledgements
We thank anonymous reviewers for their helpful and constructive comments, in particular the suggestion to add Sect. 8.
Funding
Funding was provided by Natural Sciences and Engineering Research Council of Canada [Grant No. RGPIN-2020–06792] and American University of Sharjah [Grant No. EFRG18-SCR-CEN-33].
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Ben-Daya, M., Hassini, E., Bahroun, Z. et al. Optimal pricing in the presence of IoT investment and quality-dependent demand. Ann Oper Res 324, 869–892 (2023). https://doi.org/10.1007/s10479-022-04595-6
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DOI: https://doi.org/10.1007/s10479-022-04595-6