ML Replenishment Policy Prediction for Supply Chain Metrics
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Solution Overview
Problem
Conventional supply chain simulation techniques are resource-intensive and often run infrequently, leaving many products without comprehensive data, making it challenging to predict effective replenishment policies efficiently.
Innovation Solution
A machine learning model, such as a random forest or XG Boost model, is trained using diverse simulation data to predict supply chain metrics, enabling quick and efficient selection of replenishment policies without extensive overhead, leveraging readily available demand and product features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supply chain simulation techniques are used to predict replenishment policies, then comprehensive data can be obtained, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The patent creates a simplified copy of the complex simulation process by training a machine learning model on simulation data. The model replicates the predictive capabilities of full simulations without requiring the computational resources to run them repeatedly. This allows rapid policy evaluation while maintaining the reliability benefits of comprehensive simulation data.
Solution Approach 2:
The patent performs comprehensive simulations in advance to train the machine learning model, storing the results for future use. This preliminary action captures the comprehensive data quality benefits upfront, allowing subsequent policy predictions to be made quickly without repeating the resource-intensive simulation process.
2Measurement precision
If conventional simulation techniques are run frequently to update policies, then policy accuracy improves, but computing resources are excessively consumed
Solution Approach 1:
The machine learning model serves as a lightweight copy that can evaluate policies rapidly without consuming significant computing resources. This enables frequent policy updates and accuracy improvements while avoiding the excessive resource consumption of running full simulations repeatedly.
Solution Approach 2:
The patent changes the computational parameters from running full simulations to using a trained machine learning model for evaluation. This parameter change maintains policy accuracy through the model's predictive capabilities while dramatically reducing the computational resources required for each evaluation.
3Reliability
If comprehensive simulation data is collected for all products, then prediction reliability improves, but the time and resources required increase significantly
Solution Approach 1:
The patent collects simulation data in advance to train the machine learning model, performing the time-consuming data collection work upfront. Once trained, the model can provide reliable predictions quickly without requiring additional time for data collection, thus resolving the contradiction between prediction reliability and data collection time.
Solution Approach 2:
The machine learning model captures the patterns learned from comprehensive simulation data and applies them rapidly to new products and scenarios. This copying approach maintains prediction reliability while eliminating the need to repeat the time-consuming simulation process for each new case.
Data Source
AI summary
Embodiments predict supply chain policies using machine learning. A machine learning model trained to predict one or more supply chain metrics for a first product can be stored. The machine learning model can generate a plurality of supply chain metric predictions for the first product using a plurality of candidate replenishment policies for the first product. A candidate replenishment policy with a corresponding supply chain metric prediction that meets a criteria can be selected. The selected replenishment policy can be implemented for the first product within an inventory system, where one or more physical locations are restocked with the first product based on restocking parameters defined by the selected replenishment policy.