Mar Vazquez-Noguerol¹, Iago Portela-Caramés¹ and J. Carlos Prado-Prado¹

¹ Business Organization & Marketing Department, School of Industrial Engineering, Campus Lagoas-Marcosende c/Maxwell, University of Vigo 36310, Spain.

marfernandezvazquez@uvigo.es

Keywords: E-grocery, Order picking, Store-based model.

1. Introduction

Online and physical sales are increasingly connected, although it has been shown that the online channel is substituting the offline one [1]. Moreover, e-commerce has been boosted by the effects of the COVID-19 pandemic as food distribution had to be adapted quickly. For that reason, supermarket chains are seeking to optimize their processes. Focusing on the online order picking process, it is worth noting that most e-grocers use existing physical sales points as order preparation stores. One main drawback of this strategic model is that these sales points are not designed for online sales. The main discrepancies between stores are found in size, in selling concepts, in aisle traffic and in backroom availability [2]. The great diversity existing among stores gives rise to discrepancies in preparation times, which constitutes a potentially interesting problem to be considered.

In this study it has been identified a gap in the literature so a case study has been carried out at a Spanish firm with a store-based model. The aim is to improve the online order picking process based on store features, since this process needs to simultaneously achieve low costs, high accuracy and high velocity.

2. Methodology

To carry out this study, the researchers have used the action research approach. It is a cyclical process of diagnosis, planning, action taking, and evaluation of the learning, in which the researcher participates acting as an agent of change [3]. Under these considerations, the research process begins with a contextualization phase to discover which store features can affect the preparation process. Then, the study of methods and times is carried out and the features of the preparation stores are identified. Data collection led to information on a total of 200 orders in different physical stores. Those data were analyzed to extract the results. Finally, the findings were evaluated to determine which store features minimize the order picking process time.

The firm of the study is a supermarket chain with stores throughout Spain. The case study has been limited to Galicia and Euskadi, where sales are most representative. The stores used for preparation have been selected based on their location, all of them in the middle of population centers. Even so, the stores show logistical differences that affect online order picking.

3. Empirical analysis

The first stage of the case study centers on analyzing the problems linked to the stores and their working method. We identified that the management and working methods are the same for all of them. With regard to general aspects of the store we also find similarities. To collect comparable measurements, the researchers paid special attention to the day and time slot when preparation took place in order to avoid bias. Four store variables were identified for each order that affect the online order picking process: the store size, the product assortment, backroom availability and store congestion.

A one-way ANOVA was used for the analysis in order to determine the effect of each of the four store features identified in time per item. The initial results show that three of the four store features identified are statistically significant. To go deeper, a pair analysis is performed for those store features that were statistically significant.

4. Findings and Discussion

By applying this statistical analysis, it has been identified that the most agile way to carry out the online order picking process is to do it in a convenience store (less than 700 m2 surface area), with a small product assortment (fewer than 32000 items) and to use the aisles of the store for the picking process. Regarding the store congestion variable, it has been determined that this is not a decisive variable. This result agrees with that presented by [4], however, two of the results are surprising. Firstly, convenience stores seemed to be the most efficient stores for carrying out picking [5] and secondly, preparing in the aisles of the store is the most effective alternative.

As a result, e-grocers can use this information to focus their efforts on optimizing preparation times to become more competitive in this continually growing market.

References

1. Suel, E., Daina, N., & Polak, J. W.: A hazard-based approach to modelling the effects of online shopping on intershopping duration. Transp 45(2), 415-428 (2018).
2. Seidel, S., Blanquart, C., Ehrler, V.: Spatial patterns of food retailers and consequences for logistics. A comparison between France and Germany. Transport research Arena (2014).
3. Middel, R., Coghlan, D., Coughlan, P., Brennan, L., McNichols, T.: Action research in collaborative improvement. Int J Technol Manag 33(1), 67-91 (2006).
4. Salgado, T. M. F. D. N.: In-store Order Picking Routing: A Biased Random-Key Genetic Algorithm Approach. Master´s thesis, Universidade do Porto, Portugal (2015).
5. Do, V. C., Omdahl, K.: Efficiency of e-grocery: Challenges and suggestions. Master’s thesis, University of Stavanger, Norway (2018).