Location Optimization of Offline Physical Stores Based on MNL Model under BOPS Omnichannel

Abstract

With the continuous upgrading of consumer demand and retail modes, more and more retailers are switching to an omnichannel retail mode. In order to study the location problem of offline physical stores of online retail enterprises that plan to implement the BOPS (Buy Online and Pickup in Store) omnichannel retail model, this paper considers consumers’ choice behavior under the omnichannel retail model; uses the MNL (Multinomial Logit) model to depict customers’ choice behavior between the online channel, BOPS channel, and offline physical channel; and constructs a location optimization model of offline physical stores with the goal of minimizing the enterprise’s cost. According to the characteristics of the model, an improved genetic algorithm was designed; the algorithm was improved on chromosome selection mode, crossover, and mutation rules. Finally, an example is calculated, and the physical store location scheme of a retail enterprise and the vehicle routing optimization scheme under the two-level distribution network are obtained, which verifies the effectiveness of the model and algorithm and provides a scientific reference for the physical store location decision of online retail enterprises planning to implement the BOPS omnichannel retail model. The impact of freight, return rate, and service level of physical stores on the location of offline physical stores is analyzed. The results show that in the process of expanding offline physical stores to implement the BOPS omnichannel retail model, online retail enterprises can reduce enterprise costs by improving the freight level and service level of the physical store. The higher the return rate of the online channel, the more necessary it is to expand offline physical stores, and the lower the enterprise cost. At the same time, management suggestions are put forward for the enterprise operation under the BOPS omnichannel retail mode.

The Role of Location Mediation on Purchase Interest Towards Consumer Purchase Decision in Retail Industry

“…Brand perception and associations can significantly impact consumer decision-making, as consumers tend to choose brands that align with their values and meet their expectations. Furthermore, consumers consider factors such as convenience, availability, reputation, and trustworthiness of the supplier when making their choice (Rachmawati et al, 2019;Wan et al, 2022). The location and accessibility of the supplier, as well as the availability of online and offline channels, can also influence consumer decisions (Wan et al, 2022).…”

“…Furthermore, consumers consider factors such as convenience, availability, reputation, and trustworthiness of the supplier when making their choice (Rachmawati et al, 2019;Wan et al, 2022). The location and accessibility of the supplier, as well as the availability of online and offline channels, can also influence consumer decisions (Wan et al, 2022). Consumers may consider factors such as bulk discounts, package deals, or the need to stock up on certain products when determining the quantity of their purchase.…”

The Future of E-Commerce: Overview and Prospects of Multichannel and Omnichannel Retail

“…In particular, and even though some contributions were related to technology through the creation, use, assessment, and application of mathematical models and algorithms (e.g., [23][24][25]), none of the final accepted research studies had a technology focus. One of the studies [26] offered a nice starting point to set the tone of the special issue by exploring the previous literature on multichannel and omnichannel retailing, three of them had a mostly strategic focus (e.g., [27] on promotional strategy, [23] on digital marketing strategy, [28] on location selection for physical stores), four studies took a customer-centric view ( [5] on channel integration from a consumer perspective, [29] on emotional responses to omnichannel systems, [30] on green communication and channel switching, and [31] on perceived quality of online and offline servicescapes), two studies had a retailer-centric approach ( [24] on inventory replenishment in the fashion industry and [25] on optimization of last mile delivery). Finally, while the last contribution [32] leaned toward the retailcentric approach, we considered it to be a good representative of the mixed and integrative views mentioned in the previous section, since the authors explicitly stated related research objectives that combined both views.…”

“…With the shift to omnichannel operation, the location of physical stores becomes a key element in the company's omnichannel strategy. Wan et al [28] develop in Location Optimization of Offline Physical Stores Based on MNL Model under BOPS Omnichannel a location optimization model for offline physical stores that considers consumers' choice behavior and aims to minimize costs on the retailer side. The model seeks to provide a solution for online retailers that are initiating physical operations and implementing BOPS (Buy Online and Pick Up in Store).…”