Analytical modeling demonstrates linear revenue gains from increased substitution acceptance in e-grocery retail, highlighting the economic viability of automated shortage management.
The article presents an analytical model for the quantitative assessment of the economic effect of introducing an intelligent automatic product substitution subsystem in online grocery retail (e-grocery). The relevance of the topic stems from the fact that stock-outs at the order picking stage are among the most significant operational phenomena directly affecting basket integrity and customer retention. An analytical dependence is formulated linking daily and annual retained revenue with the increment of the substitution acceptance rate. The model parameters are introduced: average daily volume of proposed substitutions, average order line value, baseline and target values of the acceptance rate. A sensitivity analysis of the annual effect with respect to the target acceptance rate is carried out, and a linear dependence of the financial result on the rate increment is established. A scheme is proposed for translating the offline metrics of a machine learning ranking model into managerial indicators suitable for investment decision-making. The results are applicable for justifying investments in shortage-handling automation projects in retail chains performing in-store order picking.
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Guseynov et al. (2026) studied this question.
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