Predictive Demand Forecasting Framework for Adaptive Retail Inventory Planning

Abstract

The predictive demand forecasting framework links forecasts to adaptive retail inventory planning, presenting a systematic procedure to monitor forecast performance and tune replenishment rules. Demand-forecasting accuracy is pivotal for inventory effectiveness, and quantifying forecast quality is necessary to establish risk-and-decision controls. However, demand forecasts invariably hold uncertainty, especially for long horizons. Forecast-information value diminishes when forecasting-accuracy declines. Consequently, instead of pursuing Accuracy Maximization for every forecast, Practical Action takes precedence. Decision-centric approaches advocate aligning forecasting and decision-making goals by planning inventories consistent with forecast quality; as forecast-driven replenishment-disciplines and stock-out penalties evolve, adaptation provides a sensible response to forecast performance. An integrated framework links forecast performance to inventory decisions, delineating necessary risk-and-decision controls. Application in retail environments steers the demonstration. Consumer demand in these settings is inherently volatile and nonstationary, confirming primacy of univariate time-series analyses. Despite the availability of granular household data, store-level aggregation is required for robust prediction; recurrence patterns in the level of aggregation hold significance, guiding product hierarchies, assortment selections, and geographical penetration decisions. Seasonal and promotional effects are particularly strong. Seasonality and holiday events are identified explicitly, while event-effect estimation permits formal modeling of idiosyncratic events. Promotions impact not only the facilitated products but also their neighbors, substitutes, and cannibals. Accurate forecasting of promotion-induced effects is thus crucial, as is the assessment of forecast bias for periods when promotion-based demand uplifts occur.