Machine Learning Supply Chain Failure Prediction from Historical Data
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Solution Overview
Problem
Supply chain planning systems often fail to account for variations in demand and production, leading to service level failures due to asynchronous data requirements and planning periods across different processes, making it difficult to generate globally-optimized plans and resulting in undesirable customer service level targets.
Innovation Solution
A system utilizing machine learning techniques to predict supply chain failures using historical data, generate alerts, and provide contextual visualizations to identify underlying causes, allowing for proactive adjustments before failures occur, without real-time data integration with planning and execution processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distinct and dissimilar processes (demand planning, production planning, supply planning, distribution planning) are used for supply chain planning, then each process can operate with its own data requirements and planning periods, but it becomes difficult to synchronize these processes and generate globally-optimized plans
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that receives data from multiple distinct planning processes (demand planning, production planning, supply planning, distribution planning) and generates predictions about service level failures. This intermediary synthesizes information from the dissimilar processes without requiring direct synchronization between them, enabling globally-optimized planning while maintaining process independence.
Solution Approach 2:
The system performs preliminary actions by using the machine learning model to predict potential service level failures before they occur. By analyzing historical data and identifying risk patterns in advance, the system enables proactive adjustments to be made to the supply chain plan, preventing failures rather than reacting to them after synchronization issues arise.
2Productivity
If supply chain plans are generated without accounting for demand and production variations, then planning can be completed within standard timeframes, but service level failures occur and customer service level targets are not met
Solution Approach 1:
The machine learning model performs preliminary analysis of historical supply chain data to identify patterns and risk factors associated with service level failures. This preliminary action enables the system to predict potential failures before finalizing the supply chain plan, allowing for proactive adjustments that maintain both planning speed and service level reliability.
Solution Approach 2:
The system implements feedback by using the machine learning model to continuously analyze outcomes and identify variations in demand and production that may lead to service level failures. This feedback mechanism enables the planning system to account for variations in future plans while maintaining efficient planning cycles, thereby improving both reliability and productivity.
3Measurement precision
If real-time data integration with planning and execution processes is implemented, then accurate predictions can be made, but implementation time and costs increase significantly
Solution Approach 1:
The patent extracts the essential predictive functionality from complex real-time data integration systems. By using a machine learning model trained on historical data, the system captures the core predictive insights without requiring full real-time integration with planning and execution processes. This extraction maintains prediction accuracy while significantly reducing implementation time and costs.
Solution Approach 2:
The system uses a relatively simple machine learning model that can be deployed and updated independently of expensive real-time integration infrastructure. This approach provides accurate predictions at lower cost and with faster implementation, effectively replacing the need for complex, costly real-time data integration systems.
Data Source
AI summary
A system and method are disclosed for a low-touch centralized system to predict service level failure in a supply chain using machine learning. Embodiments include receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a predictive model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken.