EOQ for perishable goods: modification of Wilson’s model for food retailers
Abstract
A timely response to a fluctuating and ever-changing consumer demand is an important decision for a company, as it may impact its position in the market. Thus, proper inventory management becomes a focal point in retail business process management and can provide a substantial competitive advantage. In this paper, we introduce a modified version of Wilson’s model, which takes into account trends in consumer demand and offer flexibility in reordering time. The illustration of the proposed model is presented, showing the significant economic benefit under particular conditions.
Keyword : economic order quantity, Wilson’s model, inventory management, retail
How to Cite
Zeng, S., Nestorenko, O., Nestorenko, T., Morkūnas, M., Volkov, A., Baležentis, T., & Zhang, C. (2019). EOQ for perishable goods: modification of Wilson’s model for food retailers. Technological and Economic Development of Economy, 25(6), 1413-1432. https://doi.org/10.3846/tede.2019.11330
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Khmelnitsky, E., & Singer, G. (2015). An optimal inventory management problem with reputationdependent demand. Annals of Operations Research, 231(1), 305-316. https://doi.org/10.1007/s10479-014-1600-z
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Liu, M., Feng, M., & Wong, C. Y. (2014). Flexible service policies for a Markov inventory system with two demand classes. International Journal of Production Economics, 151, 180-185. https://doi.org/10.1016/j.ijpe.2013.10.010
Manna, A. K., Dey, J. K., & Mondal, S. K. (2017). Imperfect production inventory model with production rate dependent defective rate and advertisement dependent demand. Computers & Industrial Engineering, 104, 9-22. https://doi.org/10.1016/j.cie.2016.11.027
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Beyer, W. H. (Ed.). (1987). CRC standard mathematical tables (28th ed., pp. 299-300). Boca Raton, FL: CRC Press.
Beyer, D., Cheng, F., Sethi, S. P., & Taksar, M. (2010). Markovian demand inventory models. New York: Springer. https://doi.org/10.1007/978-0-387-71604-6
Boute, R. N., Disney, S. M., Lambrecht, M. R., & Van Houdt, B. (2007). An integrated production and inventory model to dampen upstream demand variability in the supply chain. European Journal of Operational Research, 178(1), 121-142. https://doi.org/10.1016/j.ejor.2006.01.023
Broyles, J. R., Cochran, J. K., & Montgomery, D. C. (2010). A statistical Markov chain approximation of transient hospital inpatient inventory. European Journal of Operational Research, 207(3), 1645-1657. https://doi.org/10.1016/j.ejor.2010.06.021
Budd, J. K., & Taylor, P. G. (2019). Bounds for the solution to the single-period inventory model with compound renewal process input: An application to setting credit card limits. European Journal of Operational Research, 274(3), 1012-1018. https://doi.org/10.1016/j.ejor.2018.11.022
Cárdenas-Barrón, L. E., Chung, K. J., & Treviño-Garza, G. (2014). Celebrating a century of the economic order quantity model in honor of Ford Whitman Harris. International Journal of Production Economics, 155, 1-7. https://doi.org/10.1016/j.ijpe.2014.07.002
Chen, X., Wang, X., & Chan, H. K. (2017). Manufacturer and retailer coordination for environmental and economic competitiveness: A power perspective. Transportation Research Part E: Logistics and Transportation Review, 97, 268-281. https://doi.org/10.1016/j.tre.2016.11.007
Crist, E., Mora, C., & Engelman, R. (2017). The interaction of human population, food production, and biodiversity protection. Science, 356(6335), 260-264. https://doi.org/10.1126/science.aal2011
De Matteis, J. J., & Mendoza, A. G. (1968). An economic lot-sizing technique. IBM Systems Journal, 7, 30-46. https://doi.org/10.1147/sj.71.0030
De, S. K., & Sana, S. S. (2014). A multi-periods production–inventory model with capacity constraints for multi-manufacturers – A global optimality in intuitionistic fuzzy environment. Applied Mathematics and Computation, 242, 825-841. https://doi.org/10.1016/j.amc.2014.06.075
Dubelaar, C., Chow, G., & Larson, P. D. (2001). Relationships between inventory, sales and service in a retail chain store operation. International Journal of Physical Distribution & Logistics Management, 31(2), 96-108. https://doi.org/10.1108/09600030110387480
Durach, C. F., & Nitsche, B. (2016). Successfully managing challenges in German-Chinese logistics networks (Vol. 6). Universitätsverlag der TU Berlin.
Fernie, J., & Sparks, L. (Eds.). (2018). Logistics and retail management: emerging issues and new challenges in the retail supply chain. Kogan Page Publishers.
Feng, J., Zhao, L., Jia, H., & Shao, S. (2019). Silk Road Economic Belt strategy and industrial totalfactor productivity: Evidence from Chinese industries. Management of Environmental Quality: An International Journal, 30(1), 260-282. https://doi.org/10.1108/MEQ-06-2018-0109
Fleisch, E., & Tellkamp, C. (2005). Inventory inaccuracy and supply chain performance: a simulation study of a retail supply chain. International Journal of Production Economics, 95(3), 373-385. https://doi.org/10.1016/j.ijpe.2004.02.003
Ganesan, S., George, M., Jap, S., Palmatier, R. W., & Weitz, B. (2009). Supply chain management and retailer performance: emerging trends, issues, and implications for research and practice. Journal of Retailing, 85(1), 84-94. https://doi.org/10.1016/j.jretai.2008.12.001
Goldman, A., Ramaswami, S., & Krider, R. E. (2002). Barriers to the advancement of modern food retail formats: theory and measurement. Journal of Retailing, 78(4), 281-295. https://doi.org/10.1016/S0022-4359(02)00098-2
Greenstone, M. (2017). The continuing impact of Sherwin Rosen’s “Hedonic prices and implicit markets: product differentiation in pure competition”. Journal of Political Economy, 125(6), 1891-1902. https://doi.org/10.1086/694645
Gustafsson, K., Jönson, G., Smith, D., & Sparks, L. (2006). Retailing logistics and fresh food packaging: managing change in the supply chain. Kogan Page Publishers.
Hackl, P., Scharitzer, D., & Zuba, R. (2000). Customer satisfaction in the Austrian food retail market. Total Quality Management, 11(7), 999-1006. https://doi.org/10.1080/09544120050135524
Harris, I., Naim, M., Palmer, A., Potter, A., & Mumford, C. (2011). Assessing the impact of cost optimization based on infrastructure modelling on CO2 emissions. International Journal of Production Economics, 131(1), 313-321. https://doi.org/10.1016/j.ijpe.2010.03.005
Hayakawa, H., & Venieris, Y. (2016). Consumer interdependence via reference groups. In Behavioral Interactions, Markets, and Economic Dynamics (pp. 81-99). Tokyo: Springer. https://doi.org/10.1007/978-4-431-55501-8_3
Hinsley, A., Verissimo, D., & Roberts, D. L. (2015). Heterogeneity in consumer preferences for orchids in international trade and the potential for the use of market research methods to study demand for wildlife. Biological Conservation, 190, 80-86. https://doi.org/10.1016/j.biocon.2015.05.010
Hosken, D. S., Olson, L. M., & Smith, L. K. (2018). Do retail mergers affect competition? Evidence from grocery retailing. Journal of Economics & Management Strategy, 27(1), 3-22. https://doi.org/10.1111/jems.12218
Isotupa, K. S. (2006). An (s, Q) Markovian inventory system with lost sales and two demand classes. Mathematical and Computer Modelling, 43(7-8), 687-694. https://doi.org/10.1016/j.mcm.2005.09.027
Karimi, B., Ghomi, S. F., & Wilson, J. M. (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5), 365-378. https://doi.org/10.1016/S0305-0483(03)00059-8
Khmelnitsky, E., & Singer, G. (2015). An optimal inventory management problem with reputationdependent demand. Annals of Operations Research, 231(1), 305-316. https://doi.org/10.1007/s10479-014-1600-z
Konstantaras, I., Skouri, K., & Lagodimos, A. G. (2019). EOQ with independent endogenous supply disruptions. Omega, 83, 96-106. https://doi.org/10.1016/j.omega.2018.02.006
Liberopoulos, G., Tsikis, I., & Delikouras, S. (2010). Backorder penalty cost coefficient “b”: What could it be? International Journal of Production Economics, 123(1), 166-178. https://doi.org/10.1016/j.ijpe.2009.07.015
Liu, M., Feng, M., & Wong, C. Y. (2014). Flexible service policies for a Markov inventory system with two demand classes. International Journal of Production Economics, 151, 180-185. https://doi.org/10.1016/j.ijpe.2013.10.010
Manna, A. K., Dey, J. K., & Mondal, S. K. (2017). Imperfect production inventory model with production rate dependent defective rate and advertisement dependent demand. Computers & Industrial Engineering, 104, 9-22. https://doi.org/10.1016/j.cie.2016.11.027
Minoux, M. (2018). Robust and stochastic multistage optimisation under Markovian uncertainty with applications to production/inventory problems. International Journal of Production Research, 56(1-2), 565-583. https://doi.org/10.1080/00207543.2017.1394597
Myerson, P. (2012). Lean supply chain and logistics management. New York, NY: McGraw-Hill.
Nestorenko, O., Péliová, J., & Nestorenko, T. (2017). Economic and mathematical models of inventory management with deficit and with proportional to waiting time the penal sanctions. Knowledge and skills for sustainable development: The role of Economics, Business, Management and Related Disciplines. In EDAMBA-2017. Conference Proceedings of International Scientific Conference for Doctoral Students and Post-Doctoral Scholars (pp. 351-359), 4–6 April 2017, University of Economics in Bratislava. Retrieved from https://edamba.euba.sk/www_write/files/archive/edamba2017proceedings.pdf
Nobil, A. H., & Taleizadeh, A. A. (2016). A single machine EPQ inventory model for a multi-product imperfect production system with rework process and auction. International Journal of Advanced Logistics, 5(3-4), 141-152. https://doi.org/10.1080/2287108X.2016.1207975
Ortega, D. L., Wang, H. H., Wu, L., & Hong, S. J. (2015). Retail channel and consumer demand for food quality in China. China Economic Review, 36, 359-366. https://doi.org/10.1016/j.chieco.2015.04.005
Ortolani, C., Persona, A., & Sgarbossa, F. (2011). External cost effects and freight modal choice: research and application. International Journal of Logistics Research and Applications, 14(3), 199-220. https://doi.org/10.1080/13675567.2011.609536
Paul, S. K., Sarker, R., & Essam, D. (2018). A reactive mitigation approach for managing supply disruption in a three-tier supply chain. Journal of Intelligent Manufacturing, 29(7), 1581-1597. https://doi.org/10.1007/s10845-016-1200-7
Quesada-Pineda, H. J. (2008). Lean inventory management in the wood products industry: Examples and applications. Retrieved from https://www.pubs.ext.vt.edu/420/420-148/420-148.html
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), 421429. https://doi.org/10.1016/j.ijpe.2009.11.026
Rostamzadeh, R., Esmaeili, A., Shahriyari Nia, A., Saparauskas, J., & Keshavarz Ghorabaee, M. K. (2017). A fuzzy ARAS method for supply chain management performance measurement in SMEs under uncertainty. Transformations in Business & Economics, 16(2A), 319-348.
Sana, S. S. (2011). Price-sensitive demand for perishable items–an EOQ model. Applied Mathematics and Computation, 217(13), 6248-6259. https://doi.org/10.1016/j.amc.2010.12.113
Sarkar, B. (2013). A production-inventory model with probabilistic deterioration in two-echelon supply chain management. Applied Mathematical Modelling, 37(5), 3138-3151. https://doi.org/10.1016/j.apm.2012.07.026
Schwartz, B. L. (1970). Optimal inventory policies in perturbed demand models. Management Science, 16(8), B509-B518. https://doi.org/10.1287/mnsc.16.8.B509
Schwartz, J. D., Wang, W., & Rivera, D. E. (2006). Simulation-based optimization of process control policies for inventory management in supply chains. Automatica, 42(8), 1311-1320. https://doi.org/10.1016/j.automatica.2006.03.019
Silver, E. A., & Meal, H. C. (1973). A heuristic for selecting lot size quantities for the case of a deterministic time – varying demand rate and discrete opportunities for replenishment. Production and Inventory Management, 14(2), 64-74.
Slesarenko, A., & Nestorenko, A. (2014). Development of analytical models of optimizing an enterprise’s logistics information system supplies. Eastern-European Journal of Enterprise Technologies, 5(3(71)), 61-66. https://doi.org/10.15587/1729-4061.2014.27746
Song, M. L., Cui, X., & Wang, S. H. (2018). Simulation of land green supply chain based on system dynamics and policy optimization. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.08.021
Soto-Silva, W. E., Nadal-Roig, E., González-Araya, M. C., & Pla-Aragones, L. M. (2016). Operational research models applied to the fresh fruit supply chain. European Journal of Operational Research, 251(2), 345-355. https://doi.org/10.1016/j.ejor.2015.08.046
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