In an omnichannel environment, customers switch channels from product discovery to eventual purchase decision strategically. Hence, the biggest challenge for retailers nowadays is how to operate an effective omnichannel strategy. To improve inventory operational efficiency, this chapter investigates the influences of price setting and customers’ return probability on inventory forecasting. Subsequently, we explore how retailers participate in providing appropriate information delivery and product fulfillment. Specifically, a stylized newsvendor model, which incorporates customers’ showrooming behavior, is developed to address retailers’ inventory problem. Furthermore, we compare the benefits of buy-online-and-pick-up-in-store (BOPS) and showroom strategy which originates offline but is completed online. Three main findings are obtained as follows: (1)online and offline inventory order quantities augment with the ascending of pricing offline and online, respectively. Meanwhile, the inventory decisions increase when customers’ return probability declines; (2) the implementation of showroom helps retailers expand their pure online market coverage than BOPS, while it reduces the total inventory quantity if the proposition of unit online inventory cost accounting for product price exceeds physical store; and (3) showroom strategy is more profitable than BOPS option as long as unit online inventory cost is small enough. In addition, we find this boundary where showroom increases total profit expands with the attenuating of return probability.
The authors would like to thank National Natural Science Foundation of China (Grant nos. 71631006, 71701195, and 71601173) for their financial support.
Yang, F., Li, X. and Huang, Z. (2019), "Buy-online-and-pick-up-in-store Strategy and Showroom Strategy in the Omnichannel Retailing", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 13), Emerald Publishing Limited, pp. 25-49. https://doi.org/10.1108/S1477-407020190000013007Download as .RIS
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