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Job Evaluation for Production Roles with Fuzzy Cognitive Map: An Empirical Study in the Manufacturing Industry

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DOI: 10.23977/ieim.2023.060703 | Downloads: 20 | Views: 515


Safiye Turgay 1, Metehan Han 2, Abdülkadir Aydın 1


1 Department of Industrial Engineering, Sakarya University, Sakarya, Turkey
2 Insurance Information and Surveillance Center, Merdivenköy Mahallesi Bora Sok. No:1 Kat:18, 34732, Kadıköy-Istanbul, Turkey

Corresponding Author

Safiye Turgay


Job evaluation is a critical process in organizations, particularly in the manufacturing industry, where production roles play a vital role in overall operational success. Traditional job evaluation methods often rely on subjective judgments and can be prone to bias. This study considered the application of Fuzzy Cognitive Maps (FCMs) as a novel approach to job evaluation in the manufacturing industry. FCMs provide a framework to capture and represent the complex relationships and interdependencies between various job attributes and their impact on overall job performance. The objective of this empirical study is to demonstrate the effectiveness of FCMs in job evaluation for production roles in the manufacturing industry. The collected data will be used to construct FCMs, where nodes represent job attributes (e.g., technical skills, communication, problem-solving) and edges capture the strength and direction of relationships between attributes. The FCMs will be validated and calibrated using statistical techniques, ensuring their reliability and accuracy. The final evaluation framework will provide a quantitative method for assessing the relative importance of job attributes and determining the overall value of production roles within the organization.


Job evaluation, fuzzy Cognitive Maps, job attributes, evaluation framework, performance management


Safiye Turgay, Metehan Han, Abdülkadir Aydın, Job Evaluation for Production Roles with Fuzzy Cognitive Map: An Empirical Study in the Manufacturing Industry. Industrial Engineering and Innovation Management (2023) Vol. 6: 16-25. DOI:


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