Machine Learning Supply Chain Failure Prediction Without Real-Time Integration
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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 achieve 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 measures to prevent failures without real-time data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If distinct and dissimilar processes are used for supply chain planning, then specialized planning for each process is achieved, but synchronization difficulty increases
Solution Approach 1:
The supply chain planning system is divided into distinct specialized processes (demand planning, production planning, supply planning, distribution planning) that each operate independently with their own data requirements and planning periods, while a separate synchronization mechanism coordinates them
2Adaptability or versatility
If asynchronous data requirements and planning periods are maintained for each process, then process independence is preserved, but globally-optimized plan generation is prevented
Solution Approach 1:
A synchronization mechanism acts as an intermediary between the asynchronous specialized planning processes, translating and coordinating their outputs to achieve globally-optimized plans while preserving each process's independence and specific data requirements
3Stability of the object's composition
If demand or production variation detection is delayed until after planning periods, then process stability is maintained, but service level targets are not achieved
Solution Approach 1:
The system performs preliminary detection of demand and production variations before planning periods conclude, using synchronized data collection and analysis to identify variations early enough to adjust plans and achieve service level targets while maintaining process stability
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.