Cluster-Based Assortment Optimization Strategies in Fmcg: An Empirical Assessment of Impact on Profitability (Ebitda) and Losses in Russian Retail Chains Vitaliy Borisovich Kolomiets

This study provides empirical validation of a cluster-based approach (CBA) for evaluating the economic efficiency (EE) of federal FMCG retail chains in Russia [1], and substantiates the application of cluster strategies (CS) for managing profitability and losses. Two CS scenarios were investigated: 1) the "Gross Margin (GM) Maximization" CS and 2) the "Optimal GM vs. Sales Turnover (ST) Balance" CS. Pilot interventions involved matrix-based store reclustering against benchmarks, replacing non-performing FRESH category SKUs with non-alcoholic/low-alcohol beverages (NALB/LAB) in refrigerated display cases (RDCs), and assessing the impact on operational and financial KPIs: • Scenario 1 (GM Maximization): Achieved significant FRESH losses reduction of 1,41 pp (>27%), but resulted in a 2,18% revenue decline for this category due to aggressive assortment reduction (~32% of FRESH SKUs). • Scenario 2 (GM vs. ST Balance): Generated substantial store revenue growth of 5,16% and GM growth of 7,57%, alongside moderate losses reduction through light assortment optimization (~17% FRESH SKU replacement). Adaptive RDC repurposing proved superior to radical reduction, maximizing ROI while preserving operational benefits. Scenario 2 is recommended for sustainable growth. Scenario 1 CS is effective as a corrective measure for high-losses stores. Flexible RDC space management to reduce "dead zones" can increase SKU turnover by up to 18,4%. However, limitations of the CBA for EE were identified regarding the projected 1,54 pp EBITDA increase (actual: 0,22–0,54 pp). The EE CBA model requires refinement in: incorporating operational costs (incl. associated CAPEX) for RDC repurposing/decommissioning; accounting for the adaptation time lag of assortment changes (up to 10 weeks for FRESH SKU delisting); incorporating cross-demand elasticity in FRESH → NALB/LAB substitutions; assessing store share within chains with complex logistics, extreme demand volatility, e-commerce services, and geographical specificities. This research establishes a new standard for integrating cluster analysis, A/B testing, and dynamic shelf-space EE management into the operational processes of large-scale retail chains.