Real-time Shrinkage Risk Heatmap for Retail Stores
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Solution Overview
Problem
Retailers face significant annual losses due to shrinkage, with existing detection technologies primarily focused on post-event detection rather than prevention, lacking the ability to pinpoint specific conditions and locations within a store that are most likely to experience shrinkage.
Innovation Solution
A system utilizing machine learning models trained on various store-maintained data features to provide real-time risk assessments, generating scores for likelihood of shrinkage on a per-resource and per-combination basis, visualized through an interactive heatmap interface that overlays color-coded resource identifiers within a store layout, allowing for actionable insights on high-risk areas and times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detection technologies (AI, computer vision, anomaly detection) are invested in, then shrink detection capability is improved, but shrink prevention capability remains insufficient
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical shrink data to predict future shrink risks before events occur. The model continuously learns from past transactions, customer behaviors, and store patterns to generate advance warnings about potential shrinkage incidents, enabling preventive rather than reactive responses.
Solution Approach 2:
The system implements feedback loops where shrink detection results and preventive actions are fed back into the machine learning model to continuously improve predictions. The model learns from actual shrink events and the effectiveness of preventive measures, gradually improving its accuracy in predicting and preventing future shrinkage incidents.
2Loss of information
If general shrink detection is implemented, then overall shrink awareness is improved, but specific location and condition identification is lacking
Solution Approach 1:
The system segments the store into specific locations, departments, and time periods, analyzing shrink risks at granular levels. Instead of providing only store-wide shrink alerts, the system identifies and reports shrink risks at specific counters, aisles, and time windows, enabling precise targeted responses.
Solution Approach 2:
The system applies local quality analysis by examining unique characteristics of different store locations and conditions. The machine learning model identifies which specific areas, product categories, and time periods have higher shrink risks based on historical data patterns, allowing differentiated preventive measures for each location rather than uniform store-wide approaches.
3Reliability
If reactive detection technologies are used, then post-event shrink detection is improved, but preventive action timing is delayed
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring store data and using machine learning models to predict future shrink risks before they occur. The model processes historical patterns, customer behaviors, and store operations data to generate advance warnings, enabling preventive actions to be taken before shrinkage incidents actually happen.
Solution Approach 2:
The system maintains continuous monitoring and prediction operations throughout store operating hours. The machine learning model continuously learns from new data and updates its predictions in real-time, ensuring that preventive actions can be taken at any moment without interruption, rather than relying on periodic post-event detection cycles.
Data Source
AI summary
Transaction data, customer data, employee data, and security data are obtained for a store. Transactions associated with shrink events are identified and features are derived. The data is labeled and used to train a machine-learning model to produce, as output, scores for the features and combinations of the features, where each score represents a likelihood of shrink for a given feature or a given feature combination. The scores are mapped to a heatmap data structure that includes labels or icons for resources associated with the features and indicia such as colors that are indicative of the scores. The heatmap data structure is overlaid onto a map or a physical layout for the store to show locations of the resources within the store. The labels or icons are user-selectable within the heatmap and the indicia corresponding to a selected combination of icons is adjusted based on the combination's assigned score. Additionally, the heatmap is animated over time intervals corresponding to the operating hours of the store.