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The digital transformation is having a major impact on the consumer product market, pushing food retailers to foster online sales. To avoid large investments, e-grocers are tending to use their existing physical stores to undertake the online order picking process. In this context, these companies must choose in which traditional stores must prepare online orders. The aim of this study is to identify which store features affect order preparation times. The action research approach has been used at a Spanish e-grocer to analyze the characteristics that differentiate picking stores from each other; furthermore, the preparation times for a sample of online orders have been measured. The data were analyzed statistically using one-way ANOVA to define the optimal store in terms of size, assortment, backroom and congestion. The study shows that three of the four characteristics are significant on the preparation time. Therefore, e-grocers using a store-based model can use this information to focus their efforts on optimizing this process, assigning online order picking to the most appropriate stores. The approach used allows the study to be suitable for different retail context. Moreover, the results serve as support for strategic decision-making of researchers and e-grocers seeking to become more competitive in this continually growing market.

The digital transformation is having a major impact on the consumer product market, pushing food retailers to foster online sales. To avoid large investments, e-grocers are tending to use their existing physical stores to undertake the online order picking process. In this context, these companies must choose in which traditional stores must prepare online orders. The aim of this study is to identify which store features affect order preparation times. The action research approach has been used at a Spanish e-grocer to analyze the characteristics that differentiate picking stores from each other; furthermore, the preparation times for a sample of online orders have been measured. The data were analyzed statistically using one-way ANOVA to define the optimal store in terms of size, assortment, backroom and congestion. The study shows that three of the four characteristics are significant on the preparation time. Therefore, e-grocers using a store-based model can use this information to focus their efforts on optimizing this process, assigning online order picking to the most appropriate stores. The approach used allows the study to be suitable for different retail context. Moreover, the results serve as support for strategic decision-making of researchers and e-grocers seeking to become more competitive in this continually growing market. [ABSTRACT FROM AUTHOR]