Abstract
We collaborate with Danone, a leading fast-moving consumer goods (FMCG) manufacturer, to study how AI-based shelf monitoring helps with manufacturers' shelf management efforts by using data from a quasi-experiment and a field experiment. We find that AI-powered shelf monitoring significantly and consistently improves product sales; that said this effect is only partially persistent in that it diminishes after monitoring is terminated. We further reveal that this positive effect can be attributed to independent retailers rather than chained retailers. Since the major difference with respect to shelf monitoring between these retailers is the degree of heterogeneity in shelf space rental contracts, this finding indicates that AI-powered monitoring is more effective than human monitoring in managing more heterogeneous shelf displays. More broadly, the finding further suggests that AI-powered monitoring is more scalable, allowing manufacturers to cope more effectively with more heterogeneous objects. Finally, the low marginal costs and great benefits that manufacturers earn from implementing AI-powered shelf monitoring suggest both its long-term applicability and its capacity to generate incremental value. Our research contributes to several literature streams as well as generates managerial insights for practitioners who consider AI-driven operating models.
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