Application of Set-Valued Statistical Methods for Excellent Performance Evaluation

Abstract

The performance of an organization depends on the model and methodology adopted by the organization in carrying out casual activities. The high-performance model, which is widely recognized internationally, is an effective method and tool for the comprehensive performance management of organizations. It is used to measure how much value each employee brings to a firm in terms of improved revenue, as well as overall employee return on investment when compared to industry norms. The overall goal of the performance review process is to improve a team’s or organization’s performance in order to boost customer satisfaction. However, there are many problems with the current performance evaluation methods. For instance, the currently adopted approaches are fully reliant on the assessment team’s human scoring and lack the processing and detection of scoring results, which significantly reduce the objectivity of the evaluation outcomes. In order to sort out this issue, this paper constructs an excellent performance evaluation assessment method based on set-valued statistics, which obtains the final comprehensive score value by performing set-valued statistical processing on the scores of each evaluator and detects the evaluation results by calculating the confidence level. Hereby, the case analysis is conducted and Excel modeling to assist quantitative evaluation work is adopted. The analysis results reveal that the proposed technique is better than the earlier approaches.