Reducing Invisible Losses with Shrinkage Detection

Solution Shrinkage Detection
Solution Shrinkage Detection
Industry CPG
Region US
Technology Microsoft Azure
Context
For CPG enterprises, inventory shrinkage is one of the costliest blind spots in the value chain. Complex, multi-tier supply chains and limited retailer data visibility make it difficult to pinpoint where product and revenue are being lost — and even harder to act on it before losses compound. Left unresolved, this drives revenue leakage in the supply chain, distorts demand signals, and skews performance metrics, leading to misaligned commercial strategies and higher operational costs. More CPG leaders are turning to a purpose-built shrinkage detection solution rather than retrofitting legacy inventory tools to reduce inventory shrinkage at scale. An AI-driven shrinkage detection approach changes the equation: by unifying fragmented data and flagging anomalies in real time, it restores supply chain visibility analytics, strengthens inventory shrinkage reduction efforts, and improves decision-making enterprise-wide.
Problem Statement

The client needed a shrinkage detection solution robust enough to cut through operational noise and support real revenue leakage detection. Limited retailer data visibility made it difficult to validate true inventory loss, while shrinkage stayed hidden behind merchandising resets, planogram changes, availability gaps, and pricing shifts. Static shrink thresholds couldn’t adapt to seasonality or local demand patterns, making it nearly impossible to isolate anomalies at scale. The business needed dynamic, real-time visibility to catch losses before they compounded.

Impact

  • Reduced baseline shrink rate by ~9% 
  • $16M revenue opportunity unlocked within 12 months 
  • Achieved an organization-wide adoption rate of 85% 
  • Adjusted operating margins by ~20% 

 

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