Reimagining On-shelf Availability and Phantom Inventory with a Novel AI-Driven System

On-shelf availability remains a critical challenge in retail, where delays in restocking or misplaced inventory lead to significant revenue losses despite sufficient stock in stores. Even if the system indicates that there is sufficient stock of an item, customers may not be able to find the product on the shelves. Phantom inventory amplifies on-shelf availability issues by creating a false sense of sufficient stock in the system, leading to unaddressed out-of-stock situations on the shelves. This paper presents a scalable, AI-powered solution deployed across major retailers in North America, leveraging advanced Machine Learning (ML) models and distributed cloud processing to address these inefficiencies.

The system integrates a heterogeneous forecasting ensemble of models, ranging from statistical approaches like exponential smoothing or ARIMA to machine learning techniques such as XGBoost and neural networks. It also includes an adaptive alert fusion system that integrates priority alerts, random alerts and those generated by ML algorithms like XGBoost, which analyze operational data— including stock-in-transit, shelf capacities, and store layouts— to detect and flag potential disruptions in shelf availability and phantom inventory. Targeted alerts generated by this framework enable field brokers to investigate, resolve, and provide feedback, creating a continuous improvement loop that enhances system accuracy and adaptability. Built on distributed cloud platforms, the architecture processes high-velocity, heterogeneous datasets from thousands of stores using scalable compute clusters and automated ML retraining pipelines.

Insights into key metrics, such as lost sales, Out of Stock (OOS) due to phantom inventory, and on-shelf availability scores, are delivered through advanced visualization tools, enabling timely and strategic interventions. The system can be used further to improve store inventory planning parameters such as safety stock, ordering policy, and delivery frequency. Early detection of phantom inventory can mitigate the whiplash effect in the upstream supply chain by providing leading signals for inventory anomalies. This advanced system redefines on-shelf availability and inventory management, transforming it into a proactive, scalable, and efficient solution that maximizes revenue potential and operational performance across expansive retail networks.