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GAME MODEL FOR ONLINE AND OFFLINE RETAILERS UNDER BUY-ONLINE AND PICK-UP-IN-STORE MODE WITH DELIVERY COST AND RANDOM DEMAND

  • YING OUYANG (a1), ZHAOMAN WAN (a1) and ZHONG WAN (a1)

Abstract

Online retailers are increasingly adding buy-online and pick-up-in-store (BOPS) modes to order fulfilment. In this paper, we study a system of BOPS by developing a stochastic Nash equilibrium model with incentive compatibility constraints, where the online retailer seeks optimal online sale prices and an optimal delivery schedule in an order cycle, and the offline retailer pursues a maximal rate of sharing the profit owing to the consignment from the online retailer. By an expectation method and optimality conditions, the equilibrium model is first transformed into a system of constrained nonlinear equations. Then, by a case study and sensitivity analysis, the model is validated and the following practical insights are revealed. (I) Our method can reliably provide an equilibrium strategy for the online and offline retailers under BOPS mode, including the optimal online selling price, the optimal delivery schedule, the optimal inventory and the optimal allocation of profits. (II) Different model parameters, such as operational cost, price sensitivity coefficient, cross-sale factor, opportunity loss ratio and loss ratio of unsold goods, generate distinct impacts on the equilibrium solution and the profits of the BOPS system. (III) Optimization of the delivery schedule can generate greater consumer surplus, and makes the offline retailer share less sale profit from the online retailer, even if the total profit of the BOPS system becomes higher. (IV) Inventory subsidy is an indispensable factor to improve the applicability of the game model in BOPS mode.

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[1]Adida, E. and Ratisoontorn, N., “Consignment contracts with retail competition”, European J. Oper. Res. 215 (2011) 136148; doi:10.1016/j.ejor.2011.05.059.
[2]Cao, J., So, K. C. and Yin, S., “Impact of an ‘online-to-store’ channel on demand allocation, pricing and profitability”, European J. Oper. Res. 248 (2016) 234245; doi:10.1016/j.ejor.2015.07.014.
[3]Cao, L. and Li, L., “The impact of cross-channel integration on retailers’ sales growth”, J. Retail. 91 (2015) 198216; doi:10.1016/j.jretai.2014.12.005.
[4]Chen, X. R., Liu, Y. and Wan, Z., “Optimal decision making for online and offline retailers under BOPS mode”, ANZIAM J. 58 (2016) 187208; doi:10.1017/S1446181116000201.
[5]Chopra, S., “The evolution of omni-channel retailing and its impact on supply chains”, Transp. Res. Proc. 30 (2018) 413; doi:10.1016/j.trpro.2018.09.002.
[6]Deng, S. H. and Wan, Z., “A three-term conjugate gradient algorithm for large-scale unconstrained optimization problems”, Appl. Numer. Math. 92 (2015) 7081; doi:10.1016/j.apnum.2015.01.008.
[7]Di, C., Dimitrov, S. and He, Q. M., “Incentive compatibility in prediction markets: costly actions and external incentives”, Int. J. Forecast. 35 (2019) 351370; doi:10.1016/j.ijforecast.2018.07.005.
[8]Gallino, S. and Moreno, A., “Integration of online and offline channels in retail: the impact of sharing reliable inventory availability information”, Mark. Sci. 60 (2014) 14341451; doi:10.2139/ssrn.2149095.
[9]“Gap between online and offline commerce is shrinking”, Retail & Ecommerce (10 October 2016),https://www.emarketer.com/Article/Gap-Between-Online-Offline-Commerce-Shrinking/1014575.
[10]Hariga, M., Gumus, M. and Daghfous, A., “Storage constrained vendor managed inventory models with unequal shipment frequencies”, Omega 48 (2014) 94106; doi:10.1016/j.omega.2013.11.003.
[11]Huang, S., Wan, Z. and Zhang, J., “An extended nonmonotone line search technique for large-scale unconstrained optimization”, J. Comput. Appl. Math. 330 (2018) 586604; doi:10.1016/j.cam.2017.09.026.
[12]Ishfaq, R. and Raja, U., “Evaluation of order fulfillment options in retail supply chains”, Decis. Sci. 49 (2017) 487521; doi:10.1111/deci.12277.
[13]Jin, M., Li, G. and Cheng, T. C., “Buy online and pick up in-store: design of the service area”, European J. Oper. Res. 268 (2018) 613623; doi:10.1016/j.ejor.2018.02.002.
[14]Keser, C. and Willinger, M., “Principals’ principles when agents’ actions are hidden”, Int. J. Ind. Organ. 360 (2000) 163185; doi:10.1016/S0167-7187(99)00038-7.
[15]Kim, E., Park, M. C. and Lee, J., “Determinants of the intention to use buy-online, pickup in-store (BOPS): the moderating effects of situational factors and product type”, Telemat. Inform. 34 (2017) 17211735; doi:10.1016/j.tele.2017.08.006.
[16]Lee, Y. C. E., Chan, C. K. and Langevin, A., “Integrated inventory–transportation model by synchronizing delivery and production cycles”, Transp. Res. Part E: Logist. Transp. Rev. 91 (2016) 6889; doi:10.1016/j.tre.2016.03.017.
[17]Levitt, S. D., “Incentive compatibility constraints as an explanation for the use of prison sentences instead of fines”, Int. Rev. Law Econ. 17 (1997) 179192; doi:10.1016/S0144-8188(97)00002-1.
[18]Li, T. and Wan, Z., “New adaptive Barzilar–Borwein step size and its application in solving large scale optimization problems”, ANZIAM J. 61 (2019) 7698; doi:10.1017/S1446181118000263.
[19]Li, Y. X., Wan, Z. and Liu, J. J., “Bi-level programming approach to optimal strategy for vendor-managed inventory problems under random demand”, ANZIAM J. 59 (2017) 247270; doi:10.1017/S1446181117000384.
[20]Liu, Y. M. and Zhou, D., “Is it always beneficial to implement BOPS? A comparative research with traditional dual channel”, Oper. Res. Manag. Sci. 2268 (2018) 613623; http://en.cnki.com.cn/Article_en/CJFDTotal-YCGL201802023.htm.
[21]MacCarthy, B. L., Zhang, L. and Muyldermans, L., “Best performance frontiers for buy-online-pickup-in-store order fulfilment”, Int. J. Prod. Econ. 211 (2019) 251264; doi:10.1016/j.ijpe.2019.01.037.
[22]Ofek, E., Katona, Z. and Sarvary, M., “‘Bricks and clicks’: the impact of product returns on the strategies of multichannel retailers”, Mark. Sci. 360 (2011) 4260; doi:10.1287/mksc.1100.0588.
[23]Rapp, A., Baker, T. L. and Bachrach, D. G., “Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance”, J. Retail. 91 (2015) 358369; doi:10.1016/j.jretai.2014.12.007.
[24]Sadigh, A. N., Mozafari, M. and Karimi, B., “Manufacturer–retailer supply chain coordination: a bi-level programming approach”, Adv. Eng. Softw. 45 (2012) 144152; doi:10.1016/j.advengsoft.2011.09.008.
[25]Saghiri, S., Wilding, R., Mena, C. and Bourlakis, M., “Toward a three-dimensional framework for omni-channel”, J. Bus. Res. 77 (2017) 5367; doi:10.1016/j.jbusres.2017.03.025.
[26]Shi, X. T., Dong, C. W. and Cheng, T. C. E., “Does the buy-online-and-pick-up-in-store strategy with pre-orders benefit a retailer with the consideration of returns?”, Int. J. Prod. Econ. 206 (2018) 134145; doi:10.1016/j.ijpe.2018.09.030.
[27]Wan, Z., Tang, J. Y., Ren, L., Xiao, Y. and Liu, S., “Optimization techniques to deeply mine the transcriptomic profile of the sub-genomes in hybrid fish lineage”, Front. Genet. 10 (2019) 117; doi:10.3389/fgene.2019.00911.
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GAME MODEL FOR ONLINE AND OFFLINE RETAILERS UNDER BUY-ONLINE AND PICK-UP-IN-STORE MODE WITH DELIVERY COST AND RANDOM DEMAND

  • YING OUYANG (a1), ZHAOMAN WAN (a1) and ZHONG WAN (a1)

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