Supply Chain Lead Time Clustering for Planning Correction
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Solution Overview
Problem
Existing supply chain management systems struggle to accurately measure and correct discrepancies between designed and actual lead times, leading to inefficiencies and increased costs due to unpredictable factors influencing supply chain operations.
Innovation Solution
A dynamic supply chain planning system utilizing machine learning algorithms to forecast future lead times and cluster historical data, enabling precise adjustments to planned lead times based on actual performance, thereby improving business metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supply chain management systems are used to measure and correct lead time discrepancies, then system simplicity is maintained, but measurement precision and ability to identify root causes deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/statistical analysis methods with machine learning algorithms and AI systems to analyze supply chain data. The system uses trained machine learning models to automatically identify root causes of lead time variations, substituting complex manual analysis and simple statistical methods with intelligent systems that provide both high precision and automated complexity management.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw supply chain data and decision-making processes. These models act as mediators that process historical lead time data, identify patterns, and provide corrected lead time estimates, thereby improving measurement precision while managing system complexity through modular architecture.
2Measurement precision
If machine learning algorithms are implemented to forecast lead times and identify root causes, then measurement precision and forecasting accuracy improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models using historical supply chain data before deployment. The system performs offline training and validation phases where models are prepared in advance with labeled historical data, enabling them to automatically forecast lead times and identify root causes without requiring complex real-time processing during actual supply chain operations.
Solution Approach 2:
The patent utilizes parameter changes by adjusting and optimizing machine learning model parameters through cross-validation and hyperparameter tuning. The system automatically optimizes model parameters such as learning rates, tree depths, and feature weights to achieve optimal forecasting accuracy while managing computational complexity through automated parameter optimization processes.
3Reliability
If comprehensive historical data analysis is performed to identify root causes of lead time deviations, then reliability of supply chain planning improves, but loss of time for data processing increases
Solution Approach 1:
The patent applies preliminary action by performing data preprocessing, feature engineering, and model training in advance using historical data. The system prepares cleaned and labeled historical supply chain data beforehand, enabling rapid real-time analysis when lead time deviations occur. This pre-processing significantly reduces the time required for actual root cause analysis while maintaining high reliability.
Solution Approach 2:
The patent implements skipping by using trained machine learning models to quickly infer root causes without performing comprehensive step-by-step analysis of all historical data points. The models have learned patterns from historical data and can rapidly predict lead time deviations and identify causality factors by skipping detailed manual analysis, thereby reducing processing time while maintaining accuracy.
Data Source
AI summary
A method and system for a machine learning cluster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.