Retail execution in fast-moving consumer goods (FMCG) depends on the ability to convert dispersed outlet-level observations into timely managerial action. This study examines how structured field data capture and performance measurement can support retail execution in the context of Hindustan Coca-Cola Beverages (HCCB). The study combines an empirical retailer-survey dataset with an exploratory case-study perspective and draws on the Technology Acceptance Model (TAM), the Resource-Based View (RBV), and Dynamic Capabilities theory. The raw dataset contains 257 submitted responses. After excluding records without both shop name and market area and deduplicating repeated outlet-area combinations, 240 unique outlet-area observations were retained. Coca-Cola assets were reported in 81.7% of these observations. Observed availability was high for Thums Up (95.8%), Coca-Cola (93.3%), Sprite (90.8%), Fanta (85.4%) and Maaza (74.2%), while lower availability was recorded for Diet Coke (17.1%), Coca-Cola 0 Sugar (15.4%), Charged (17.9%), Minute Maid (30.4%) and Rim Zim Soda (36.2%). Pepsi was reported in 36.7% of observations, Campa in 19.2%, and 41.7% reported no competition. Chi-square tests identified significant associations between asset availability and P&G outlet classification, no-competition status, and Pepsi presence; these results indicate association rather than causation. The study proposes a research-oriented RED/GREEN measurement architecture linking structured capture, execution measurement, dashboards, managerial prioritisation and verification. Machine-learning applications for outlet-risk prediction, route optimisation, demand forecasting and image-assisted compliance are proposed as future capabilities rather than documented HCCB systems. The study contributes an empirical FMCG retail-execution case while maintaining a clear boundary between observed field evidence, secondary contextual evidence and proposed analytics.

Keywords: FMCG; digital transformation; retail execution; sales force automation; RED/GREEN; data-driven decision making; HCCB; outlet analytics; dynamic capabilities