Worker Evaluation by Object Classification and Process Similarity
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Solution Overview
Problem
Existing worker evaluation technologies lack accuracy in assessing individual performance and do not effectively utilize information for flexible human resource deployment and development based on worker strengths and weaknesses.
Innovation Solution
A system that calculates individual worker performance, compares it to overall performance, identifies similar processes, and outputs evaluations to improve accuracy, enabling better human resource planning and development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing worker evaluation technologies are used, then evaluation can be performed, but evaluation accuracy is insufficient
Solution Approach 1:
The evaluation is segmented into multiple dimensions: individual performance evaluation, team performance evaluation, and relative evaluation among workers. Each dimension uses different calculation methods and comparison bases, allowing comprehensive assessment that improves both accuracy and reliability of worker evaluation.
Solution Approach 2:
The system incorporates feedback mechanisms by comparing individual worker performance with team performance and by providing relative rankings. This feedback loop allows continuous improvement of evaluation accuracy and enables workers to understand their performance relative to standards and peers.
2Measurement precision
If comprehensive worker evaluation is implemented, then evaluation accuracy improves, but system complexity increases
Solution Approach 1:
The evaluation system is designed with multi-functionality, serving multiple purposes: individual performance assessment, team performance measurement, relative worker comparison, and basis for human resource deployment. This universal approach consolidates multiple evaluation functions into a single system, improving accuracy while managing complexity through integrated design.
3Measurement precision
If detailed performance data is collected, then evaluation accuracy improves, but information processing requirements increase
Solution Approach 1:
The system extracts and processes only the necessary performance data required for evaluation, separating relevant information from unnecessary data collection. By focusing on key performance indicators and essential worker attributes, the system achieves high evaluation accuracy while minimizing information processing burden and data management complexity.
Data Source
AI summary
A system accepts input of worker specification information that specifies a worker to be analyzed, specifies specific object information of the worker, calculates individual performance of a work of the work for a specific object, and compares the individual performance and overall performance to calculate evaluation of the worker for the specific object. Further, the system specifies a similar specific object belonging to a classification similar to a classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, and outputs individual evaluation, the similar specific object or the similar process.