1. Proceedings
  2. I3M
  3. 2020
  4. FOODOPS
  5. 10.46354/i3m.2020.foodops.004

Optimization of a perishable inventory system: A simulation study in a Ho.Re.Ca. company

  • a Elena Piva  ,
  • b Letizia Tebaldi , 
  • c Giuseppe Vignali , 
  • d Eleonora Bottani  
  • a Logistics and Procurement Office, CIRFOOD, Via Nobel 19, 42124, Reggio Emilia, Italy
  • b, c, d Department of Engineering and Architecture, University of Parma, Viale delle Scienze 181/A, 43124, Parma, Italy
Cite as
Piva E., Tebaldi L., Vignali G., Bottani E. (2020). Optimization of a perishable inventory system: A simulation study in a Ho.Re.Ca. company. Proceedings of the 6th International Food Operations and Processing Simulation Workshop (FoodOPS 2020), pp. 20-29. DOI: https://doi.org/10.46354/i3m.2020.foodops.004

Abstract

The main goal of this paper is to describe the optimization of the inventory management process in a real context of perishable food products. The study involves one of the largest Italian HO.RE.CA. companies, located in the north of Italy and operating as a provider of the catering, commercial and welfare services. A simulation model was set up with the purpose of adapting three traditional reordering policies (i.e. Re-Order Point, Re-Order Cycle, and (s,S)) to a set of products belonging to company’s assortment and evaluating the resulting economic outcomes. To this end, each policy was modelled on Microsoft ExcelTM, so as to compute the total cost of inventory management and determine of the minimum cost strategy. A comparison with the current company’s performance and that achievable with the optimized policy is also proposed. 

References

  1. Alrjoub, A.M.S., and Ahmad, M.A. (2017). Inventory management, cost of capital and firm performance: evidence from manufacturing firms in Jordan. Invest. Manag. Financial Innov., 14(3):4-14.
  2. Arnold, T.J.R., (1991). Introduction to Materials Management. New York, Prentice- Hall International Incorporated.
  3. Blackburn, J., and Scudder, G. (2009). Supply chain strategies for perishable products: the case of fresh produce. Prod. Oper. Manag., 18(2): 129–137
  4. Blankley, A.I., Khouja, M., and Wiggins, C.E. (2008). An Investigation into the Effect of Full-Scale Supply Chain Management Software Adoptions on Inventory Balances and Turns. J. Bus. Logist.,29(1):201.
  5. Bottani, E., and Montanari, R. (2010). Supply chain design and cost analysis through simulation. Int. J. of Prod., 48(10): 2859–2886.
  6. Bottani, E., Ferretti, G., Montanari, R., and Vignali, G. (2012). The impact of (S, s) policy on supply network
    performance: a simulation study. IJBPSCM, 4(2): 164–201
  7. Bottani, E., Ferretti, G., Montanari, R., and Rinaldi, M. (2014). Analysis and optimization of inventory management policies for perishable food products: a simulation study. Int.J. of Simulat. Process Model., 9(1-2): 16-32.
  8. Chaudhary, V., Kulshrestha, R., and Routroy, S. (2018). State-of-the-art literature review on inventory models for perishable products. J. Adv. in Manag. Res., 15(3): 306-346.
  9. Deniz, B., Scheller-Wolf, A., and Karaesman, I. (2004). Managing Inventories of Perishable Goods: The Effect of Substitution, Working Paper of the Carnegie-Mellon University Pittsburgh. Available at:
    http://faculty.fuqua.duke.edu/seminarscalendar/Scheller-WolfSeminar.pdf
  10. Ferguson, M., Jayaraman, V., and Souza, G.C. (2007). Note: an application of the EOQ model with nonlinear holding cost to inventory management of perishables. Eur. J. Oper. Res., 180(1): 485–490. 
  11. Ghare, P.M., and Schrader, G.F. (1963). A model for exponentially decaying inventory. J. of Ind. Eng., 14(5): 238-243.
  12. Goyal, S.K., and Giri, B.C. (2001). Recent trends in modelling of deteriorating inventory. Eur. J. Oper. Res., 134: 1–16.
  13. Goyal, S.K. (1985). Economic order quantity under conditions of permissible delay in payments. J. Oper. Res. Soc., 36(4): 335–338.
  14. Harris, F.W. (1913). How many parts to make at once, Factory, The Magazine of Management, 10 (2):135–136.
  15. Iannone, R., Miranda, S., and Riemma, S. (2007). Supply chain distributed simulation: an efficient architecture for multimodel synchronization. Simul. Model. Pract. Th., 15(3): 221–236.
  16. Kolias, G. D., Dimelis, S. P., and Filios, V. P. (2011). An empirical analysis of inventory turnover behaviour in Greek retail sector: 20002005. Int. J. Prod. Econ., 133(1):143-153.
  17. Liu, L., and Lian, Z. (1999). (s, S) continuous review models for products with fixed lifetimes. Oper. Res., 47(1): 150–158.
  18. Longo, F., and Mirabelli, G. (2008). An advanced supply chain management tool based on modeling & 
    simulation. Comput. Ind. Eng., 54(3): 570–588.
  19. Nahmias, S. (1982). Perishable inventory theory: a review. Oper. Res., 30(3): 680–708.
  20. Pirard, F., Iassinovski, S., and Riane, F. (2011). A simulation based approach for supply network control. Int. J. Prod. Res., 49(24): 7205–7226.
  21. Raafat, F. (1991). Survey of literature on continuously deteriorating inventory models. J. Oper. Res. Soc., 42(1): 27–37.
  22. Rajan, A., Steinberg, R., and Steinberg, R. (1992). Dynamic pricing and ordering decisions by a monopolist. Manag. Sci., 38(2): 240–262.
  23. Ravichandran, N. (1995). Stochastic analysis of a continuous review perishable inventory system with positive lead time and Poisson demand. Eur. J. Oper. Res., 84: 444–457.
  24. Schmidt, C.P., and Nahmias, S. (1985). (S-1,S) policies for perishable inventory. Manag. Sci., 31: 719–728.
  25. Schneider, H., and Ringuest, J.L. (1990). Power approximation for computing (s, S) policies using service level. Manag. Sci., 36: 822–834.
  26. Schwarz, L., and Weng, K.Z. (2000). The design of a JIT supply chain: The effect of lead time uncertainty on safety stock. J. Bus. Logist., 21(2):231-254.
  27. Shapiro, J.F., and Wagner, S.N. (2009). Strategic Inventory Optimization. J. Bus. Logist., 30(2):161-173.
  28. Silver, E., 1981. Operations Research in Inventory Management: A review and Critique. University of Calgary, Calgary, Alberta. 
  29. Silver, E.A., Naseraldin, H., and Bischak, D.P. (2009). Determining the reorder point and order-up-tolevel in a periodic review system so as to achieve a desired fill rate and a desired average time between replenishments. J. Oper. Res. Soc., 60(9): 1244–1253.
  30. Van Der Vorst, J.G.A.J. (2000). Effective food supply chains: generating, modelling and evaluating supply chain scenarios. PhD thesis, Wageningen University & Research, Wageningen. Available at: https://library.wur.nl/WebQuery/wurpubs/66572
  31. Waller, M., Johnson, M. E., and Davis, T. (1999). Vendor-managed inventory in the retail supply chain. J. Bus. Logist., 20:183-204.
  32. Wang, K.-J., Lin, Y.S., and Jonas, C.P. (2011). Optimizing inventory policy for products with time-sensitive deteriorating rates in a multi-echelon supply chain. Int. J. Prod. Eco., 130(1): 66-76.
  33. Wang, X., and Li, D. (2012). A dynamic product quality evaluation based pricing model for perishable food
    supply chains. Omega, 40: 906–917.
  34. Waters, D. (2003). Logistics: An Introduction to Supply Chain Management. Palgrave MacMillan, New York.
  35. Weiss, H.J. (1980). Optimal ordering policies for continuous review perishable inventory models. Oper. Res., 28(2): 365–374
  36. Whitin, T.M. (1957). Theory of Inventory Management,Princeton University Press, Princeton, NJ.
  37. Yu, M. & Nagurney, A. (2013). Competitive food supply chain networks with application to fresh produce. Eur . J . Oper .Res ., 224(2): 273 -282.