Supply Chain Segmentation Planner for Stable Dynamic 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 the data, leading to inefficiencies in supply chain inventory allocation and sub-optimal service levels.
Innovation Solution
A strategic and tactical segmentation system that utilizes a segmentation planner to manage input data comprehensively or with a representative sample, analyzes features, and employs machine learning models to generate and update segments efficiently, maintaining segmentation boundaries while reducing unnecessary changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic segmentation systems are used to speed up segmentation, then productivity is improved, but segmentation boundaries change frequently due to noise, worsening reliability
Solution Approach 1:
The system performs preliminary actions by pre-defining segment boundary adjustment rules and thresholds before segmentation occurs. When data changes are detected, the system checks whether they meet pre-established criteria for boundary adjustment, preventing reactive changes to minor fluctuations while maintaining readiness for significant changes.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring data changes and comparing them against established thresholds. Only when changes exceed predefined significance levels does the system trigger boundary adjustments, creating a feedback loop that filters out noise while responding to genuine market shifts.
2Adaptability or versatility
If frequent segmentation updates are made to reflect data changes, then adaptability is improved, but lead time for understanding segmentation boundaries increases, worsening productivity
Solution Approach 1:
Segmentation boundary adjustment rules and decision criteria are pre-established before segmentation updates occur. This preliminary configuration enables the system to automatically determine whether updates are necessary without requiring manual analysis, reducing planner lead time while maintaining adaptability to genuine market changes.
Solution Approach 2:
The segmentation system performs self-service by automatically detecting data changes, evaluating them against predefined criteria, and executing boundary adjustments when warranted. This automation eliminates the need for manual intervention in routine updates, reducing planner involvement to oversight and exception handling only.
3Measurement precision
If change management protocols are triggered frequently, then segmentation accuracy is improved, but operating expenses increase, worsening productivity
Solution Approach 1:
The system uses feedback control by continuously comparing data changes against thresholds and only triggering change management protocols when changes exceed significance levels. This feedback mechanism ensures segmentation accuracy is maintained for meaningful changes while avoiding unnecessary protocol executions for noise, reducing operational expenses.
Solution Approach 2:
The system changes the parameter of protocol trigger frequency by introducing threshold-based filtering. Instead of triggering protocols for all data changes, the system adjusts the trigger parameter to activate only when changes meet predefined significance criteria, maintaining accuracy while reducing operational overhead.
Data Source
AI summary
A system and method for performing strategic segmentation including a supply chain network having a strategic segmentation planner, an inventory system, a transportation network and supply chain entities. The strategic segmentation planner includes a computer having a memory and a processor, that selects a workflow depth including an amount of data to analyze, discovers features by analyzing cleansed data to locate features which are characterized by features data, pre-processes the features data to standardize the features data, performs multi-dimension segmentation by computing feature importance to generate multi-dimensional segments, assigns policy parameters to the supply chain network based on the generated multi-dimensional segments, and trains a machine learning model by applying a cyclic boosting process to the standardized features data wherein the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments.