Machine Learning Model for Non-Deliberate Shrink Prediction
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Solution Overview
Problem
Non-deliberate shrinkage in the retail industry, caused by cashiers' failure to scan barcodes or accurately enter produce/deli codes, results in significant financial losses for retailers, with $47 billion annually lost in the U.S. alone.
Innovation Solution
A system and method using machine learning to predict non-deliberate shrink events by identifying relevant features such as cashier, terminal, and store identifiers, time of day, and historical shrink data, and providing prescriptive recommendations to prevent such events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If computer-vision technologies are used to detect non-deliberate shrink, then detection capability is improved, but prevention capability deteriorates
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical shrink data before shrink events occur. The models learn patterns and predictors of non-deliberate shrink in advance, enabling the system to generate predictive scores and prescriptive recommendations before shrink events happen, thus transitioning from reactive detection to proactive prevention
Solution Approach 2:
The system implements feedback loops where actual shrink events are recorded and used to retrain and refine the machine learning models. This continuous feedback mechanism improves the accuracy of predictions over time and enhances the effectiveness of prescriptive recommendations, creating a self-improving prevention system
2Reliability
If machine learning models are trained to predict shrink events, then prevention capability is improved, but system complexity deteriorates
Solution Approach 1:
The machine learning models serve multiple functions: they predict shrink events, generate predictive scores, identify at-risk cashiers, and inform prescriptive recommendations. This multi-functionality reduces the need for separate systems for detection, prediction, and intervention, thereby managing complexity while enhancing prevention capability
Solution Approach 2:
The system performs self-service through automated model training, prediction generation, and recommendation formulation. The machine learning models automatically process historical data, identify patterns, and generate predictions without requiring manual intervention, reducing operational complexity while maintaining high prevention capability
3Reliability
If prescriptive recommendations are provided to store managers, then prevention effectiveness is improved, but information processing requirements deteriorate
Solution Approach 1:
The system applies local quality by providing tailored prescriptive recommendations to specific cashiers based on their individual predictive scores and identified risk factors. Rather than generic advice, each cashier receives customized guidance targeting their specific shrink risks, improving prevention effectiveness while keeping information processing manageable through targeted rather than universal communication
Data Source
AI summary
Features attributable to non-deliberate cashier shrink are identified within a given store's historical transaction data. A machine-learning model is trained on the features to predict shrink events over a future interval of time. Each prediction is also associated with a specific prescriptive recommendation, which if followed, eliminates or otherwise mitigates the likelihood that the predicted shrink event occurs during the corresponding time interval. Each prediction can be specific to a given cashier for the future interval of time. The predictions and corresponding prescriptive recommendations can be provided to store managers in advance of a start of the future interval of time, which allows the manager to follow the prescriptive recommendations and potentially avoid the shrink events altogether.