Applying fuzzy logic theory to performance management

Abstract

Organizations usually apply classical methods of employee performance evaluation. In this classical system, employee performance depends on work results, and it is evaluated only as success or failure in job behaviors. The non-classical performance evaluation methods such as fuzzy logic may mainly be used to many forms of decision-making including artificial intelligence systems. This research proposes a new employee performance evaluation method based on fuzzy logic systems. The process of performance measurement for evaluating the effectiveness, efficiency, and productivity of employees encompasses data collection, data design, and data analysis stages and it involves a level of uncertainty associated with performance measures. In evaluating employee performance, it usually involves granting numerical values or linguistic labels to employee performance in the organization. The scores accorded by the appraisers are only approximations, which are then, used to represent each employee’s achievement by reasoning incorporated in the computational methods. In this paper, the fuzzy logic theory approach is used to represent the uncertainty caused by performance measures during its design, use and analysis stages. This research seeks to describe and execute the fuzzy logic theory approach for decision making in the employee performance appraisal process. Finally, reasoning based on fuzzy logic theory provides an alternative way in dealing with imprecise data, which is often reflected in the way humans think and make judgments in real life.