Cognitive Analytics for Group Performance Ranking
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Solution Overview
Problem
Business leaders lack visibility into performance differences across groups within a business unit, hindering informed decision-making regarding resource allocation and investment strategies, as they do not have comprehensive insights into human resource metrics, cost, productivity, quality, and attrition data across various groups.
Innovation Solution
A computer-implemented method and system using machine learning to extract and analyze human resource, cost, productivity, quality, and attrition data from stored business information, producing scores and rankings for groups within a business unit, and providing recommendations for performance improvement, investment, and business expansion or continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If business leaders rely on traditional manual analysis methods, then they can maintain simplicity in the analysis system, but they lack comprehensive visibility and insights into performance differences across groups
Solution Approach 1:
The patent introduces an intermediary analytics system that includes a processing device with machine learning models. This intermediary automatically extracts, analyzes, and synthesizes data from multiple sources (human resource information, cost information, productivity information, quality information, attrition data) to produce performance scores and rankings. This mediator bridges the gap between raw data and business leaders' decision-making needs, providing comprehensive visibility without requiring leaders to manually process complex data themselves.
Solution Approach 2:
The analytics system performs self-service by automatically extracting data from stored business information, applying machine learning models to generate performance scores, and producing rankings without requiring manual intervention from business leaders. The system autonomously identifies performance differences across groups, calculates return on investment metrics, and generates actionable insights, freeing leaders from manual analysis while providing comprehensive visibility.
2Measurement precision
If comprehensive data extraction and analysis is implemented across all groups, then business leaders gain full visibility into performance metrics, but the processing complexity and computational resources increase
Solution Approach 1:
The patent transforms multiple raw data parameters (human resource information, cost information, productivity information, quality information, attrition data) into a standardized performance score parameter. The machine learning model changes the parameters from disparate data formats into a unified, comparable metric that enables precise measurement of performance differences across groups while simplifying the complexity of processing multiple data types.
Solution Approach 2:
The analytics system segments the analysis process into distinct functional modules: data extraction from stored business information, machine learning model processing, performance score calculation, and ranking generation. This segmentation allows each component to handle specific tasks efficiently, improving measurement precision while managing processing complexity through modular architecture.
3Productivity
If manual performance analysis is used for each group, then the system remains simple to operate, but it consumes excessive time and resources
Solution Approach 1:
The system performs preliminary action by pre-processing and storing business information in a structured format that enables rapid extraction and analysis. Data is organized and prepared in advance, with machine learning models pre-trained to quickly generate performance scores. This preliminary preparation eliminates time-consuming manual analysis when leaders need performance comparisons, significantly improving productivity while reducing the time loss associated with ad-hoc analysis.
Solution Approach 2:
The analytics system enables continuous automated analysis of group performances, constantly processing available data to maintain up-to-date performance metrics. Rather than periodic manual analysis, the system continuously extracts data, applies machine learning models, and updates rankings, ensuring leaders always have current information without the time loss of repeated manual analysis cycles.
Data Source
AI summary
A method, a system, and a computer program product are provided for performing cognitive analytics. Human resource information for respective groups of a business unit of an organization may be extracted from stored business information. The human resource information may include, for employees of the respective groups, salary information and employee experience information. For the respective groups, cost information, productivity information, quality information, and attrition data may be extracted from the business information. At least the human resource information, the cost information, the productivity information, the quality information, and the attrition data may be provided, for the respective groups, to a model trained via machine learning. The model may produce respective scores for the groups. The groups may be ranked based at least partly on the respective scores and the ranking of the groups may be output.