Enhancing Retail Forecast Accuracy with AI

Abstract

In today’s consumer-centric retail market, where innovation and digitization are rapidly transforming traditional operating norms, correctly balancing inventory levels and supply chain requirements with constantly moving consumer demand for a better anytime, anywhere shopping experience is fast becoming a cornerstone for business supremacy, profitability, and even survival and stock valuation. Product unavailability is costing U.S. retailers $223 billion annually. To address this problem and to better communicate between buyers and suppliers, there is a need to capture the future requirements of the retailer accurately. In essence, the forecasting method of the retailer holds the key to forecasting accuracy and the success of almost all forecasting algorithms.

The prime goal of this study is to address the above challenge and to enable retail forecasting technology to keep pace with AI technologies. The objective of this paper is to show how the presence of an expert AI retailer layer, positioned atop a planning system, can keep the system’s enhanced forecasting tool at the cutting edge of forecasting science. Worldwide, the artificial intelligence market is being propelled at an unprecedented pace; the AI market is projected to grow at a significant CAGR of 61.5% between 2017 and 2022 and become a $16.06 billion market by 2022. In this paper, we address a new area of application pertaining to the deployment of artificial intelligence technologies in retail, principally forecasting, and espouse its major transformational benefits and challenges. The annotated bibliography summarizes recent contributions to forecasting, AI for retail, demand and sales forecasting, inventory forecasting, and data exchange under the six clear sub-themes as shown in the mind map - The Evolution of Forecasting and Data Exchange.