Incorporating E‐commerce into Retail Location Models

Abstract

The use of location models in retail businesses is well‐established, particularly in the grocery sector. Many alternative methods are in use today but the spatial interaction model (SIM) has a proven record of success. To date, that success relates purely to face‐to‐face activities, modeling and predicting visits by consumers to retail outlets. However, grocery retailers are cutting back on store investments and concentrating on investment in the convenience market and e‐commerce: the latter has now reached a 7.2% share of the U.K. grocery market, with continued growth forecast. Although spatial models are used extensively for helping to locate new convenience stores, so far e‐commerce has not been built into existing retail location models. Yet e‐commerce seems to be a spatial activity. Extensive evidence demonstrates the geography of demand and supply are as important in groceries e‐commerce as they are in face‐to‐face grocery retailing. We therefore take up the challenge of incorporating e‐commerce into classic location models. Methodologically, we find the standard distance deterrent term in the production‐constrained SIM unsuitable for modeling e‐commerce flows: we explore inverting this term and find extensive gains in prediction accuracy, an interesting finding that contributes to the ongoing applied SIM literature.

Last-Mile Capacity Constraints in Online Grocery Fulfilment in Great Britain

“…These studies tend to focus exclusively on consumer interactions with the physical store network, rather than the online channel, within which the drivers of consumer store-choice and expenditures are different. With the exception of Beckers et al [20], there are a dearth of studies that consider the spatial and logistical implications of groceries e-commerce from a geographical and supply-side perspective. This is surprising given the relatively abundant body of literature focusing solely on the online proponents of running the service, e.g., see [21,22].…”

“…The capacity data represent a store's ability to fulfil home-delivery orders. The academic literature commonly uses store floor space as a proxy for e-commerce capacity [20,26] and so the provision of more detailed capacity data are novel and a key strength of this research. In order to preserve commercial data sensitivity, all indicators of capacity presented in this paper are a relative value, with higher values identifying greater capacity within the order-fulfilment system, enabling higher order volumes to be fulfilled.…”

Post-script—Retail forecasting: Research and practice

“…Online retailers can cover wider geographical locations, have the ability to adopt personalised pricing and promotions, and potentially compete in far more product categories, selling many more SKUs including the rapid introduction of many new products. There is also a potential interaction for an omni-retailer with complementary effects, leading to changes in emphasis on store format and location and the need to incorporate e-commerce into location models ( Beckers et al, 2021 ; see also the discussion in Caro, Gürhan Kök, & Martínez-De-Albéniz, 2020 ). In addition, the integration of the two channels provides a means for operational improvements ( Pereira & Frazzon, 2021 ).…”

Duopoly Pricing Strategy in Spatial Competition Using Constant - sum Games

“…Analysis of impact of spatial differentiation on pricing mechanism of online car hailing platform is presented in [24]. Also [25] point to growth of e-commerce of grocery retail and importance of incorporating it into the location models. Use of Hotelling's model is presented in [26] for analysis of customization strategy of offline retailer to make him competitive to online retailer with the same product.…”