Expert Peer Identification via Asset Interaction Centrality

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Solution Overview

Problem

In enterprise asset management, identifying expert peers for maintenance or repair is challenging due to the loss of tribal knowledge and outdated databases, as technicians often struggle to find experienced peers for guidance, especially with poorly documented tasks and high employee turnover.

Innovation Solution

A method and system that utilize asset interaction data to calculate an overall centrality measure for workers, selecting the most experienced peer based on their interaction with specific assets, by multiplying category values with corresponding weights and processing these values to identify the expert peer.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a database of key experts for each asset is maintained, then expert peer identification is improved, but the complexity of generating and maintaining the database increases significantly

Engineering Contradiction:

Improveexpert peer identification accuracyVSAvoiddatabase maintenance complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system automatically calculates centrality measures using existing asset interaction data without requiring manual database maintenance. The algorithm self-updates as new interaction data becomes available, eliminating the need for manual expert database curation while maintaining high identification accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system transitions from static expert database entries to dynamic centrality measures calculated from multiple interaction parameters. By continuously analyzing interaction frequency, task completion, and collaboration patterns, the system adapts expert identification to current organizational realities without manual intervention.

Inventive Principle:

Principle #35Parameter changes

2Ease of operation

If traditional expert databases are used, then expert identification is simplified, but the data becomes outdated quickly due to employee turnover

Engineering Contradiction:

Improveexpert identification simplicityVSAvoidexpert knowledge obsolescence

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system continuously monitors asset interaction data and updates centrality measures in real-time. As employees interact with assets and collaborate on tasks, the system automatically detects changes in expertise patterns and updates expert recommendations, ensuring current accuracy without manual updates.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The expert identification system transitions from static database entries to dynamic centrality calculations that adapt continuously. The system processes ongoing interaction data to reflect current organizational structure and expertise distribution, automatically adjusting to employee turnover and role changes.

Inventive Principle:

Principle #15Dynamics

3Device complexity

If technicians research to find expert peers independently, then no additional system complexity is added, but significant time is lost in the research process

Engineering Contradiction:

Improvesystem complexityVSAvoidtechnician research time

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The system introduces an intermediary expert peer recommendation service between technicians and actual experts. When technicians encounter asset issues, the system automatically queries interaction data, calculates relevant centrality measures, and returns targeted expert recommendations, eliminating manual research while adding minimal system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Measurement precision

If manual expert database maintenance is performed, then data accuracy is improved, but the process becomes unsustainable with thousands or millions of assets

Engineering Contradiction:

Improveexpert database accuracyVSAvoiddatabase maintenance efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system replaces manual mechanical database maintenance with automated computational processing. Algorithms automatically analyze asset interaction data, calculate centrality measures, and update expert recommendations at scale, making the process sustainable for organizations with thousands or millions of assets while maintaining high accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11250358B2Asset management expert peer identification

Publication Date: 2022.02.15 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method of expert peer identification includes receiving, from a user, a request for an expert peer for an asset type, and obtaining asset interaction data relevant to the request including a list of assets, and, for each asset on the list, values for each of a set of pre-defined interaction categories for each worker that has interacted with the asset. The method further includes, for each asset on the list, respectively multiplying the interaction category values by a corresponding set of category weights to obtain a set of weighted interaction values, and, for each worker, processing the sum of their weighted interaction values to obtain an overall centrality measure. The method also includes selecting one of the workers as the expert peer based, at least in part, on their overall centrality measure, and identifying the expert peer to the user.