Discretion in Automated Supermarket Replenishment: Censorship Bias and Self-inflicted Stockouts

Problem definition: We study a paradox in which decision-makers deviate downward from proposals of an automated store replenishment system after a stockout. We argue that censorship bias explains this curious ordering behavior and it has negative performance implications. We compare the effect of censorship bias to that of anchoring bias to understand the relative importance of censorship bias in retail practice. Understanding the impact of certain biases on decision-makers’ inventory replenishment decisions in the presence of an algorithm is imperative to maximize the benefit from decision-makers' discretionary power. Methodology/results: We analyze data on perishable products from an upmarket supermarket chain by employing exclusion restrictions in our recursive bivariate probit model to account for the endogeneity of deviations. We complement our analysis with endogenous switching regression and seemingly unrelated regression models to further test the robustness of our results. We find that after a stockout, decision-makers’ likelihood of deviating downward is higher, a behavior aligned with censorship bias. We find that anchoring bias is more powerful than censorship bias in predicting downward deviations. Regarding the performance implications, we show that censorship bias is more detrimental than anchoring bias in terms of increasing the likelihood of a new stockout. Therefore, we suggest that this insight into censorship bias can be used to distinguish uninformed downward deviations from the informed ones. Managerial implications: To suggest actionable policies, we collect more data to test the idea of blocking downward deviations when censorship bias is suspected. With this additional data analysis, we show that by blocking the downward deviations susceptible to censorship bias, retail managers can reduce self-inflicted stockouts with reasonable inventory cost implications.