OPTIMIZATION OF SALES FORCE ALLOCATION AND SCHEDULING: A CASE STUDY IN EIGHT BRAZILIAN CITIES

Efficient sales force management is a complex challenge, particularly for retail chains with broad geographical distribution. This article presents a real world case study focused on optimizing commercial agent allocation, developed using authentic operational data provided by corporate partner Dom Rock. The proposed methodology is structured into three business applied stages: strategic selection of points of sale, spatial clustering via the k-means algorithm, and optimization using an Integer Linear Programming model. The objective of the model is to maximize visit coverage while adhering to strict day to day practical constraints, such as working hours, service frequency, and in store dwell time. Applied to a real scenario spanning 8 Brazilian cities, the solution demonstrated high operational feasibility, achieving full coverage in over 90% of the clusters and ensuring efficient distribution, averaging 1.57 stores per agent. These results highlight the power of integrating data clustering with operations research to optimize retail workforce management.