A rule-based system for human performance evaluation: A case study

Abstract

The issues of many organizations are related to the proper evaluation of human performance and efficient decision-making. The expert system application within the decision-making process is not a novelty, but the widespread of its implementation regarding performance evaluation has not been recognized yet. To overcome this problem, a case study of rule-based expert system application in the decision-making process regarding human performance in periodical technical inspection stations in Croatia is presented. The rule-based expert system improves the quality of traditional decision-making as designed rules provide a visual, transparent, and accurate comparison of observed values with the expected values. Moreover, it provides easy problem identification. Therefore, rules regarding periodical technical inspection inspectors’ performance are designed and embedded in the expert system architecture. However, more effort should be made into data analysis to define parameters and their relations for the purpose of designing rules. Thus, the binary logistic regression and an ANOVA statistical test were conducted to identify which parameters can be assumed as relevant indicators regarding the performance of periodical technical inspection in-spectors. In this study, the expert system application has resulted in faster response, greater efficiency, and increased objectivity. That is of utmost importance for providing an efficient and transparent periodical technical inspection system.