Dynamic Segmentation Planner for Stable Supply Chain Boundaries
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Solution Overview
Problem
Existing dynamic segmentation systems often result in frequent, unnecessary changes to segment boundaries due to noise in data, leading to inefficient allocation of supply chain inventory and sub-optimal service levels.
Innovation Solution
A strategic and tactical segmentation system that autonomously generates and updates segments using a segmentation planner with machine learning models, distinguishing between significant and insignificant data changes to maintain stable segmentation boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic segmentation is used to speed up segmentation process, then productivity is improved, but segmentation boundaries become unstable due to noise and insignificant changes
Solution Approach 1:
The system performs preliminary actions by pre-calculating segment boundaries using historical data and establishing baseline segments before new data arrives. This allows the system to quickly assess whether new data represents significant changes or just noise, maintaining stability while enabling fast processing of incoming data through pre-established segmentation frameworks
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring changes in segment boundaries and comparing them against thresholds for significance. When insignificant changes are detected (noise), the system provides feedback to maintain existing boundaries. When significant changes are detected, feedback triggers boundary updates. This feedback loop enables fast processing while filtering out noise-induced fluctuations
2Adaptability or versatility
If frequent segment boundary changes are made to reflect data changes, then adaptability is improved, but lead time for planners to understand segmentation increases
Solution Approach 1:
The system extracts and separates significant changes from noise by applying statistical analysis and change detection algorithms. Only extracted significant changes trigger boundary updates, while noise is filtered out. This extraction process enables the system to adapt to real meaningful changes while maintaining stability against insignificant fluctuations, reducing unnecessary updates that would increase planner lead time
Solution Approach 2:
The system implements dynamic thresholds for change detection that adapt based on historical variability. The threshold for what constitutes a 'significant' change is not fixed but dynamically adjusted based on learned patterns of normal variation. This dynamic approach enables timely adaptation to genuine changes while ignoring noise, optimizing the balance between adaptability and planner workload
3Reliability
If change management protocols are triggered frequently, then reliability of segment accuracy is improved, but resource efficiency deteriorates due to unnecessary protocols
Solution Approach 1:
The system uses feedback-based change detection that monitors segment boundary movements and compares them against significance thresholds. Feedback loops detect whether changes represent genuine shifts in market segments or merely noise. Only when feedback confirms significant changes does the system trigger change management protocols, ensuring high reliability while avoiding wasteful resource consumption from unnecessary protocol activations
Data Source
AI summary
A system and method for performing tactical segmentation including a supply chain network having a tactical segmentation planner, an inventory system, a transportation network, supply chain entities and a computer. The computer performs multi-dimension segmentation on input data by computing feature importance to generate multi-dimensional segments, assigns policy parameters to the supply chain network based on the generated multi-dimensional segments, trains a machine learning model by applying a cyclic boosting process to the standardized features data, where the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments, stores the machine learning model in a database, performs multi-dimension segmentation based on the stored machine learning model, determines whether data drift has occurred in the input data and in response to determining that data drift has occurred, repeats the perform, assign, trains steps, and stores an updated machine learning model in the database.