ML Supply Chain Prediction via Weighted Historical Indicators
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Solution Overview
Problem
Current supply chain management methods rely on unreliable predictions of future demand and inventory, which can lead to mismatches between order fulfillment and actual shipments, making it difficult for logistics organizations to proactively manage their supplies effectively.
Innovation Solution
A machine learning-based approach that utilizes historical supply chain performance indicators, weighted by temporal proximity and weeks-of-stock parameters, to train models that predict future supply chain performance, allowing for more accurate assessments of case fill rates and weeks of supply metrics, thereby improving prediction reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional supply chain prediction methods are used, then the process is simple and easy to implement, but the prediction reliability is low
Solution Approach 1:
The patent replaces traditional mechanical/statistical prediction methods with machine learning models that utilize historical supply chain performance indicators. The system trains machine learning models on weighted historical data to generate predictions, substituting conventional calculation approaches with intelligent algorithms that adapt and learn from patterns in the data, thereby improving prediction reliability while managing complexity through automated training processes.
2Measurement precision
If machine learning models are implemented, then prediction accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and weighting historical supply chain performance indicators before training the machine learning models. The system prepares the training corpus in advance by accessing historical data, applying weights based on temporal proximity and weeks-of-stock parameters, and organizing the data structure beforehand. This preliminary preparation reduces the computational burden during actual prediction operations and improves training efficiency.
Solution Approach 2:
The patent utilizes parameter changes by varying the weighting parameters for historical data based on temporal proximity and weeks-of-stock metrics. The system dynamically adjusts the importance of different historical periods and inventory levels, changing the parameters that control how historical data influences predictions. This allows the model to adapt to different supply chain conditions and improve accuracy without requiring a complete model restructuring.
3Reliability
If historical data is weighted by temporal proximity, then recent performance indicators have higher impact, but the weighting process becomes more complex
Solution Approach 1:
The patent applies dynamics by implementing a dynamic weighting system where the importance of historical data points changes based on their temporal proximity to the current period. The weighting parameter is not static but varies continuously, giving higher weights to more recent performance indicators and lower weights to older data. This dynamic approach allows the model to adapt to changing supply chain conditions while providing a systematic method for handling the complexity of temporal weighting.
Data Source
AI summary
Historical information features that each comprise a supply chain performance indicator (such as a case fill rate performance indicator) for each of a plurality of different temporal windows are accessed and then at least some of the historical information features are weighted differently for at least some of the historical information features according to at least a first criterion to provide a training corpus. At least one machine learning model can then be trained using the training corpus to generate a machine learning model (or models) that are configured to predict supply chain performance.