Machine Learning PI Correction for Overstated Inventory
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Solution Overview
Problem
Perpetual inventory (PI) values often become overstated, leading to inaccurate stock assessments and potential customer service failures due to insufficient restocking.
Innovation Solution
A machine learning-based system that dynamically adjusts PI values by using a suite of models trained on historical data and real-time updates, iteratively monitoring out-of-stock conditions and adjusting inventory levels based on probability thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PI tracking methods are used, then system simplicity is maintained, but measurement precision of inventory values deteriorates due to overstated PI values
Solution Approach 1:
The system segments the inventory verification process into multiple independent machine learning models, each handling specific aspects of PI value validation. This allows complex accuracy improvement to be achieved through modular components rather than a monolithic complex system.
Solution Approach 2:
Machine learning models serve as intermediaries between raw inventory data and final PI values. These models process and analyze multiple data sources (scanner data, POS transactions, receiving records) to produce corrected PI values, acting as a mediator that improves accuracy without requiring direct complex intervention in the entire inventory system.
2Reliability
If real-time PI correction is implemented, then reliability of inventory data is improved, but loss of time for processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing inventory data through multiple machine learning models before PI values become critically inaccurate. This proactive approach allows corrections to be made more efficiently, reducing the urgent processing time needed when discrepancies are finally identified.
Solution Approach 2:
The machine learning models operate continuously in the background, constantly analyzing inventory data streams from scanners, POS systems, and receiving records. This continuous processing maintains reliable PI values without requiring periodic intensive corrections, thereby minimizing time loss while sustaining high reliability.
3Measurement precision
If multiple machine learning models are deployed, then measurement precision of out-of-stock probability is improved, but device complexity increases
Solution Approach 1:
The system divides the probability assessment task into multiple specialized machine learning models, each trained on specific data patterns and scenarios. This segmentation allows each model to focus on particular aspects of inventory prediction, improving overall accuracy while maintaining manageable complexity through clear division of labor.
Solution Approach 2:
The suite of machine learning models serves multiple functions: detecting out-of-stock conditions, predicting future inventory status, validating PI values, and identifying data anomalies. This multi-functionality allows the system to achieve high measurement precision across various inventory scenarios without requiring entirely separate systems for each function.
Data Source
AI summary
An out-of-stock indicator is received that indicates a product is out-of-stock or believed to be out-of-stock. Information about the features of the products and store are obtained or determined. The features are applied to a first machine learning model, which yields a probability that the item is out-of-stock. The obtained probability is compared to a threshold, and if the probability value is above a threshold, then the PI value is adjusted. If not above the threshold, then scans are monitored for out-of-stock conditions, and some time later the features will be applied to a different model, and the above-process repeated. In aspects, this process occurs over a certain time period or until the PI is adjusted.