ML Shrinkage Prediction with Prescriptive Actions
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Solution Overview
Problem
Retailers face significant losses due to shrinkage, with existing detection technologies focusing on post-event detection rather than prevention, and lacking prescriptive actions to mitigate predicted shrinkage, leading to inefficient use of resources and subjective decision-making by retail staff.
Innovation Solution
A method using machine learning models trained on historical store data to predict shrinkage events and provide prescriptive actions, integrating real-time data from transaction, security, and forecasting systems to offer actionable insights for proactive prevention, with a heatmap interface for visualizing high-risk areas and automating security adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive shrink technologies are used, then the ability to predict future shrinkage events is improved, but prescriptive actions to prevent shrinkage are not provided, leading to subjective decision-making by retail staff
Solution Approach 1:
The system performs preliminary actions by predicting shrinkage events before they occur and preparing prescriptive recommendations in advance. The machine learning model analyzes historical data and current store conditions to forecast potential shrinkage incidents, then proactively generates preventive actions before the actual events happen, allowing retailers to take preventive measures rather than reacting after loss occurs.
Solution Approach 2:
The system implements feedback by continuously monitoring store data and comparing predicted shrinkage events against actual outcomes. The machine learning model receives feedback from labeled shrinkage events and adjusts its predictions accordingly, while also providing feedback to retail staff through prescriptive recommendations that can be tracked for effectiveness, creating a closed-loop system that improves over time.
2Adaptability or versatility
If retail staff use intuition and experience to prevent shrinkage, then flexibility in decision-making is maintained, but objectivity and consistency of prevention actions are reduced
Solution Approach 1:
The system applies local quality by providing customized prescriptive recommendations tailored to specific store conditions, locations, and risk factors. Rather than applying uniform rules everywhere, the machine learning model analyzes local characteristics of different stores and departments to generate context-specific prevention actions that adapt to local conditions while maintaining objective consistency through data-driven decision-making.
Solution Approach 2:
The system utilizes parameter changes by transforming subjective intuition into objective measurable parameters through the machine learning model. Historical shrinkage data, store conditions, and transaction information are converted into quantifiable features that the model processes to generate consistent, reproducible predictions and recommendations, replacing subjective judgment with objective parameter-based decision-making.
3Measurement precision
If comprehensive historical data is collected and processed, then prediction accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features and data elements needed for shrinkage prediction from the comprehensive historical data. The machine learning model identifies and extracts key predictive features from transaction data, store conditions, and historical shrinkage events, filtering out unnecessary information to reduce processing complexity while maintaining prediction accuracy.
Solution Approach 2:
The system segments the data processing task into manageable components: data collection from multiple sources, feature extraction and labeling, model training on labeled data, and prediction generation. This segmentation allows the complex system to process comprehensive historical data through modular stages, making the overall process more manageable and scalable.
Data Source
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AI summary
Transaction, customer, employee, security, and sales forecast data are obtained for a store. Known actions to prevent shrink events are maintained. Transactions associated with shrink events are identified and features are derived. The data is labeled and used to train a machinelearning model to produce, as output, scores for the features, combinations of the features, and prescriptive action identifiers. Each score represents a likelihood of shrink for a given feature or a given combination of features. The output scores and prescriptive action identifiers are predicted at intervals over a period of future time. At each interval, the output for remaining intervals is updated based on real-time store data generated for transactions at the store in a previous interval. An action identifier can be provided to a security application causing the application to increase sensitivity of security detection on a terminal based on the predicted likelihood of shrink.