Talent management in manufacturing system using fuzzy logic approach. (August 2015) - Karatop, Buket; Kubat, Cemalettin; Uygun, Özer
Highlights: Fuzzy logic approach is implemented for talent management. A methodology is proposed for talent management in human resources domain. The approach can also be applied for other MCDM problems. The aim is assigning the right people with proper talents to the right positions. Abstract: Nowadays, manufacturing companies have been working based on people-oriented approach. Customer satisfaction is currently not only for external customers but also required for internal customers. In this context, human resource is one of the most important resources to manage and vital for manufacturing companies in terms of customers, employees and managers. With this perception, The Human Resource Management (HRM) issue is emerged. HRM enables effective and efficient management of human resources. Manufacturing companies considers that the existing abilities and qualification of its employees is insufficient for achieving their targets. The gap between desired level of capabilities and the existing capabilities of the human recourses should be narrowed. Therefore talent management (TM) concept is introduced in order to deal with the gap and reveal the required personnel profile. TM raises institutional awareness and is a supportive tool for HRM. The TM contributes to remove wastes of the companies and supports self-improvement of its employees. In other words the TM is a key of success about exploring abilities. The aim of this study is to propose a model for investigating competency level of employees and utilizing this information in obtaining a yield in an optimal level from employees' emotional and intellectual capabilities, and experiences. The level of perception and job-ability match of each employee is different. The tests implemented in companies for TM purposes are usually evaluated in crisp logic like black and white. In this study, a fuzzy logic approach is proposed in order to deal with uncertainty and vagueness in assessment of TM.