Analytical model that combines store locations, service areas, population demographics, temperature/precipitation data, and category-level sales history into a single digital-twin structure for sharper demand forecasting.
In retail, accurate demand forecasting is critical for inventory management, category planning, and store performance.
Traditional forecast models often fail to capture spatial differences, weather effects, and the demographics around stores.
Spatial and temporal data layers were unified into a single digital-twin structure; category- and store-level prediction models were built.
Forecast accuracy improves, category management strengthens, and store-level decisions become more data-driven.
Let's talk about strengthening your demand-forecasting and decision-support models.
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