Neural Demand Forecasting for Modern Retail

Abstract

Retail demand forecasting is crucial for intelligent decision-making, resource allocation, and meeting customer expectations. Algorithms based on machine learning or deep learning have improved the state of the art. However, companies can fall short of realized benefits due to data engineering deficits. Consequently, data ingestion and preparation need to be in place to support such algorithms in production environments. Data acquisition, quality and governance, lineage tracking, and compliance play critical roles. Robust model backtesting and validation, together with systematic monitoring and alerting, are equally vital. The requirements are presented, and an architecture designed around them is described, supporting an ensemble hybrid forecasting algorithm. These capabilities represent a significant step toward a complete AI-augmented demand-forecasting solution.

Demand forecasting influences almost all intelligent decisions in retail operations, from procurement and distribution to store staffing and marketing. Accurate forecasting enables optimal resource allocation and alignment with demand, thereby satisfying customer expectations. Many different quantitative forecasting techniques have been developed over the years, ranging from traditional statistical approaches to more recently developed machine learning or deep learning algorithms. The accuracy of demand forecasts has gradually improved because machine learning and deep learning algorithms have been executed in business environments. Nevertheless, the investment in AI or AI-capable algorithms does not guarantee a return in the form of improved forecasting accuracy. While investment in algorithms is part of the equation, other factors come into play during the tedious—yet often underestimated—task of supply and feature engineering.