Shrinkage is a significant and recurring issue in retail, affecting both margins and operational efficiency. In complex, high-volume environments, traditional methods of detection often fall short, relying on infrequent audits and manual processes that fail to keep pace with real-time risks.
Our data-driven shrinkage detection approach enhances operational resilience by continuously analyzing transactional patterns, inventory movements, and store-level anomalies. By using predictive models and store-specific baselines, we surfaced shrink hotspots, unusual stock variances, and behavioral patterns indicative of loss. This enabled proactive interventions, improved inventory integrity, and reduced overall shrink. The solution not only safeguarded revenue but also equipped store teams with actionable insights to mitigate future risks.
Problem statement
A nationwide leading grocery retail giant was facing key challenges in proactively identifying and managing shrinkage across its extensive supply chain network. The absence of an effective anomaly detection framework, coupled with manual processes for identifying and acting on shrink-related issues, resulted in missed signals and reactive decision-making. These limitations impacted on the organization’s ability to prevent losses, optimize operations, and maintain overall inventory accuracy. MathCo partnered with the said client to develop a predictive solution with a tracking dashboard that would identify anomalies across multiple functions (supply chain, finance, accounting etc.)
Impact
- Achieved an organization-wide adoption rate of 85%.
- 8.5+ NPS Consumption Score.
- $150M+ impact on reported revenue.
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