The Zero-Latency Omnichannel Grid: Optimizing SAP Customer Activity Repository (CAR) and Snowflake Integration for Real-Time Inventory Visibility

Abstract

Modern retail operations face a critical challenge in maintaining accurate inventory visibility across multiple sales channels, leading to the phenomenon known as "ghost inventory," where digital records misalign with physical stock availability. This article examines the implementation of a zero-latency data architecture integrating SAP Customer Activity Repository with Snowflake to eliminate inventory synchronization gaps that plague omnichannel retailers. Traditional batch-processing systems create temporal blind spots lasting up to a full day, during which inventory sold through one channel remains visible to others, resulting in order cancellations, customer dissatisfaction, and substantial revenue loss. Through a case study methodology in multi-location retail environments, this article demonstrates how streaming architecture using Change Data Capture technology and real-time data pipelines transforms inventory management from reactive to predictive operations. The article validates that precise, immediate information can effectively replace physical safety stock buffers, releasing working capital while improving product availability. Integration of artificial intelligence for demand sensing enables retailers to anticipate market shifts using external signals like weather patterns and social trends. The article reveals significant improvements in fulfillment efficiency, markdown reduction, and system resilience during peak trading periods. This article contributes a practical framework bridging operational and analytical systems while establishing economic models demonstrating the financial imperative of real-time inventory visibility in competitive retail markets.