Dynamic Supply Chain Segmentation with Data-Drift Boundary Control

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Solution Overview

Problem

Existing dynamic segmentation systems face inefficiencies due to frequent, insignificant changes in data, leading to longer lead times, confusion in segmentation boundaries, and wasteful change management, resulting in sub-optimal supply chain inventory allocation and service levels.

Innovation Solution

A strategic and tactical segmentation system that utilizes a segmentation planner to manage input data comprehensively or agilely, discover relevant features, and train machine learning models to generate and update segments efficiently, maintaining segmentation boundaries while adapting to significant data changes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If dynamic segmentation systems are used to speed up segmentation, then segmentation speed is improved, but frequent changes in segment boundaries occur due to noise in data

Engineering Contradiction:

Improvesegmentation speedVSAvoidsegment boundary stability

Core Design Contradiction:

ProductivityVSStability of the object's composition

Solution Approach 1:

The system performs preliminary actions by pre-defining segment boundaries and change management protocols before data changes occur. When data changes are detected, the system checks against pre-established protocols to determine whether changes are significant enough to warrant boundary adjustments, thereby preventing reactive over-segmentation due to noise

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms that monitor data changes and compare them against thresholds and protocols. This feedback loop allows the system to distinguish between significant data changes requiring segmentation updates and insignificant fluctuations that should be ignored, maintaining segment boundary stability while still enabling necessary adaptations

Inventive Principle:

Principle #23Feedback

2Extent of automation

If dynamic segmentation systems are used, then segmentation automation is improved, but confusion in segmentation results occurs

Engineering Contradiction:

Improvesegmentation automationVSAvoidsegmentation clarity

Core Design Contradiction:

Extent of automationVSLoss of information

Solution Approach 1:

The system introduces an intermediary layer of change management protocols and decision rules that mediate between automated data processing and final segmentation decisions. This intermediary filters and interprets data changes, providing clear, unambiguous segmentation results by translating complex data variations into straightforward boundary decisions

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes parameters by establishing threshold values and significance criteria that control when segmentation boundaries should be modified. By adjusting these parameters, the system maintains clarity in segmentation results while still allowing automation to respond to meaningful data changes

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If frequent changes to segment boundaries occur, then adaptability to data changes is improved, but lead times for planners to understand segmentation boundaries increase

Engineering Contradiction:

Improveadaptability to data changesVSAvoidplanner lead time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-establishing change management protocols and significance thresholds before data changes occur. This allows the system to quickly evaluate data changes against pre-defined criteria, enabling rapid adaptation to significant changes while maintaining stable boundaries for insignificant variations, thereby reducing planner lead times

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If frequent changes to segment boundaries occur, then responsiveness to data changes is improved, but wasteful change management protocols are triggered

Engineering Contradiction:

Improveresponsiveness to data changesVSAvoidchange management efficiency

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The system implements feedback mechanisms that monitor data changes and compare them against pre-established thresholds and protocols. This feedback loop ensures that change management protocols are only triggered when data changes are significant and warrant segmentation updates, preventing wasteful activation of protocols due to noise while maintaining responsiveness to genuine changes

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250265610A1Strategic and Tactical Intelligence in Dynamic Segmentation

Publication Date: 2025.08.21 BLUE YONDER GROUP INC

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.