On explainability of cluster prototypes with rough sets: A case study in the FMCG market

Despite the growing popularity of machine learning (ML), such solutions are often incomprehensible to employees and difficult to control. Addressing this issue, we discuss some essential problems of explainable ML applications in the fast-moving consumer goods (FMCG) market. This research puts forward a new approach to effective supply management by utilizing rough sets (RST), distance-based clustering, and dimensionality reduction techniques. In the presented case study, we aim to reduce the work done by experts by applying a single delivery plan to many similar points of sale (PoS). We achieve this objective by clustering vending machines based on historical sales patterns. To verify the feasibility of such an approach, we performed a series of experiments related to demand prediction on two data representations with various clustering techniques. The conducted experiments confirmed that, without losing quality in terms of MAE and RMSE, we could operate on PoS in an aggregate manner, thus reducing the workload of preparing delivery plans.