Impact of future price increase on ordering policies for deteriorating items under quadratic demand

Abstract: When a supplier announces a price increase at a certain time in the future, for each retailer it is important to choose whether to purchase supplementary stock to take benefit of the current lower price or procure at a new price. This article focuses on the possible effects of price increase on a retailer’s replenishment strategy for constant deterioration of items. Here, quadratic demand is debated; which is appropriate for the products for which demand increases initially and subsequently it starts to decrease with the new version of the substitute. We discuss two scenarios in this study: (I) when the special order time coincides with the retailer’s replenishment time and (II) when the special order time falls during the retailer’s sales period. We determine an optimal ordering policy for each case by maximizing total cost savings between special and regular orders during the depletion time of the special order quantity. Scenarios are established and illustrated with numerical examples. Through, sensitivity analysis important inventory parameters are classified. Graphical results, in two and three dimensions, are exhibited with supervisory decision.

How to cite this paper

 Shah, N., Jani, M & Chaudhari, U. (2016). Impact of future price increase on ordering policies for deteriorating items under quadratic demand.International Journal of Industrial Engineering Computations , 7(3), 423-436.

Refrences

Bakker, M., Riezebos, J., & Teunter, R.H. (2012). Review of inventory systems with deterioration since 2001. European Journal of Operational Research, 221(2), 275–284.

Begum, R., Sahoo, R.R., & Sahu, S.K. (2012). A replenishment policy for items with price-dependent demand, time proportional deterioration and no shortages. International Journal of Systems Science, 43(5), 903–910.

Covert, R. P., & Philip, G.C. (1973). An EOQ model for items with Weibull distribution deterioration. AIIE transactions, 5(4), 323–326.

Deng, P.S., Lin, R.H., & Chu, P.A. (2007). A note on the inventory models for deteriorating items with ramp type demand rate. European Journal of Operational Research, 178(1), 112–120.

Erel, E. (1992). The effect of continuous price change in the EOQ. Omega, 20(4), 523–527.

Ghare, P.M., & Schrader, G.H. (1963). A model for exponentially decaying inventory system. Journal of Industrial Engineering, 163, 238–243.

Ghosh, A.K. (2003). On some inventory models involving shortages under an announced price increase. International Journal of Systems Science, 34(2), 129–137.

Goyal, S.K. (1979). A note on the paper: An inventory model with finite horizon and price changes. Journal of the Operational Research Society, 30, 839–842.

Goyal, S.K., & Giri, B.C. (2001). Recent trends in modeling of deteriorating inventory. European Journal of Operational Research, 134(1), 1–6.

Goyal, S.K., Srinivasan, G.F., & Arcelus, F. (1991). One time only incentives and inventory policies. European Journal of Operational Research, 54(1), 1–6.

Huang, W., & Kulkarni, V.G. (2003). Optimal EOQ for announced price increases in infinite horizon. Operations Research, 51(2), 336–339.

Khanra, S., Sana, S.S., & Chaudhuri, K. (2010). An EOQ model for perishable item with stock and price dependent demand rate. International Journal of Mathematics in Operational Research, 2(3), 320–335.

Khouja, M., & Park, S. (2003). Optimal lot sizing under continuous price decrease. Omega, 31(6), 539–545.

Lev, B., & Soyster, A.L. (1979). An inventory model with finite horizon and price changes. Journal of the Operational Research Society, 30(1), 43–53.

Lev, B. and Weiss, H.J. (1990). Inventory models with cost changes. Operations Research, 38(1), 53–63.

Min, J., Zhou, Y.W., Liu, G.Q., & Wang, S.D. (2012). An EPQ model for deteriorating items with inventory-level-dependent demand and permissible delay in payments. International Journal of Systems Science, 43(6), 1039–1053.

Mishra, P., & Shah, N.H. (2008). Inventory management of time dependent deteriorating items with salvage value. Applied Mathematical Sciences, 2(16), 793–798.

Moon, I., Giri, B.C., & Ko, B. (2005). Economic order quantity models for ameliorating/deteriorating items under inflation and time discounting. European Journal of Operational Research, 162(3), 773–785.

Naddor, E. (1966). Inventory systems. New York, NY: Wiley.

Orougi, S. (2015). Recent advances in enterprise resource planning.Accounting, 1(1), 37-42.

Pakkala, T.P.M., & Achary, K.K. (1992). Deterministic inventory model for deteriorating items with two warehouses and finite replenishment rate. European Journal of Operational Research, 57(1), 71–76.

Philip, G.C. (1974). A generalized EOQ model for items with Weibull distribution. AIIE Transactions, 6(2), 159–162.

Sarma, K.V.S. (1987). Deterministic order level inventory model for deteriorating items with two storage facilities. European Journal of Operational Research, 29(1), 70–73.

Shah, N.H. (1998). A discrete-time probabilistic inventory model for deteriorating items under a known price increase. International Journal of Systems Science, 29(8), 823–827.

Sharma, S. (2006). Incorporating fractional backordering in the multi-product manufacturing situation with shelf lives. Journal of Engineering Manufacture, 220(7), 1151–1156.

Sharma, S. (2007a). A procedure to optimize the constrained multiple-item production system. Journal of Engineering Manufacture, 221(3), 467–476.

Sharma, S. (2007b). A procedure for benchmarking in multiproduct manufacturing. Journal of Engineering Manufacture, 221(3), 541–546.

Sharma, S. (2008a). On the flexibility of demand and production rate. European Journal of Operational Research, 190(2), 557–561.

Sharma, S. (2008b). Effects of an increase in manufacturing rate in the context of cyclic production. International Journal of Advanced Manufacturing Technology, 39(7–8), 821–827.

Sharma, S. (2009b). Revisiting the shelf life constrained multiproduct manufacturing problem. European Journal of Operational Research, 193(1), 129–139.

Sharma, S. (2009c). A composite model in the context of a production-inventory system. Optimization Letters, 3(2), 239–251.

Singh, S.R., Khurana, D., & Tayal, S. (2015). An economic order quantity model for deteriorating products having stock dependent demand with trade credit period and preservation technology. Uncertain Supply Chain Management, 4(1), 29-42.

Skouri, K., & Konstantaras, I. (2009). Order level inventory models for deteriorating seasonable/fashionable products with time dependent demand and shortages. Mathematical Problems in Engineering, 1–24. doi:10.1155/2009/679736

Tayal, S., Singh, S.R., & Sharma, R. (2014). An inventory model for deteriorating items with seasonal products and an option of an alternative market. Uncertain Supply Chain Management, 3(1), 69-86.

Taylor, S.G., & Bradley, C.E. (1985). Optimal ordering strategies for announced price increases. Operations Research, 33(2), 312–325.

Tersine, R.J. (1996). Economic replenishment strategies for announced price increases. European Journal of Operational Research, 92, 266–280.

Tripathi, R.P., & Tomar, S.S. (2015). Optimal order policy for deteriorating items with time-dependent demand in response to temporary price discount linked to order quantity. International Journal of Mathematical Analysis, 23(9), 1095 – 1109.

Wee, H.M., & Law, S.T. (1999). Economic production lot size for deteriorating items taking account of the time-value of money. Computers and Operations Research, 26(6), 545–558.

Wee, H.M., & Law, S.T. (2001). Replenishment and pricing policy for deteriorating items taking into account the time-value of money. International Journal of Production Economics, 71(1–3), 213–220.

Yang, H.L. (2006). Two-warehouse partial backlogging inventory models for deteriorating items under inflation. International Journal of Production Economics, 103(1), 362–370.

Yang, H.L. (2011). A partial backlogging production-inventory lot-size model for deteriorating items with time-varying production and demand rate over a finite time horizon. International Journal of Systems Science, 42(8), 1397–1407.