Education, Science, Technology, Innovation and Life
Open Access
Sign In

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

Download as PDF

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

Author(s)

Safiye Turgay 1, Metehan Han 2, Abdülkadir Aydın 1

Affiliation(s)

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

ABSTRACT

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.

KEYWORDS

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

CITE THIS PAPER

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: http://dx.doi.org/10.23977/ieim.2023.060703.

REFERENCES

[1] Suwarsono, L. W., Aisha, A. N.,  Nugraha, F. N. (2019), Comparison of Job Evaluation Methods: Implications for the Salaries Design in Publishing Company, Advances in Intelligent Systems Research, volume 173, 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019), pages={305-312}, doi= {10. 2991/icoemis-19. 2019. 42}, publisher={Atlantis Press)
[2] Kahya, Emin. (2018). A wage model consisted of job evaluation, employee characteristics and job performance. Pamukkale University Journal of Engineering Sciences. 24. 720-729. 10. 5505/pajes. 2017. 92609. 
[3] Morgeson, Frederick & Campion, Michael & Maertz, Carl. (2001). Understanding Pay Satisfaction: The Limits of a Compensation System Implementation. Journal of Business and Psychology. 16. 10. 1023/A:1007848007459. 
[4] Chen Y, Zhang Z, Zhou J, Liu C, Zhang X, Yu T. ( 2023). A cognitive evaluation and equity-based perspective of pay for performance on job performance: A meta-analysis and path model. Front Psychol., Jan 20;13:1039375. doi: 10. 3389/fpsyg. 2022. 1039375. PMID: 36743591; PMCID: PMC9897207. 
[5] Aliku, H. H., Morka, T. O., Igemohia, F. (2020) Compensation Management and Employee Performance: Manufacturing Industry in Focus-- Palarch’s Journal of Archaeology of Egypt/Egyptology 17(7), 8792-8810. ISSN 1567-214x. 
[6] Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the Effectiveness of Work Groups and Teams. Psychological Science in the Public Interest, 7(3), 77–124. https://doi. org/10. 1111/j. 1529-1006. 2006. 00030. x
[7] Zhenjing G, Chupradit S, Ku KY, Nassani AA, Haffar M. (2022). Impact of Employees' Workplace Environment on Employees' Performance: A Multi-Mediation Model. Front Public Health. May 13;10:890400. doi: 10. 3389/ fpubh. 2022. 890400. PMID: 35646787; PMCID: PMC9136218. 
[8] Ergu, D., Kou, G., Shi, Y., & Shi, Y. (2014). Analytic network process in fuzzy cognitive maps to model causality in decision-making. European Journal of Operational Research, 232(3), 658-668. 
[9] Piera Miquel, M., Nebot, À., & Aler, R. (2019). Fuzzy cognitive maps as a decision support tool for environmental management: A systematic literature review. Science of the Total Environment, 650, 2405-2419. 
[10] Karyotis, C., Pappis, C., & Stylios, C. D. (2021). Fuzzy cognitive maps in forecasting and decision-making: A systematic literature review. Information Sciences, 541, 165-195. 
[11] Díaz-Madroñero, M., & Sánchez-Medina, J. J. (2017). Application of fuzzy cognitive maps for decision-making in the energy sector: A systematic review. Energies, 10(2), 166. 
[12] Wu, J., & Lin, C. (2016). A literature review on the applications of fuzzy cognitive maps for representing complex systems. Expert Systems with Applications, 55, 228-241. 
[13] Liu, Y., Chen, G., Li, S., & Wang, Q. (2020). Fuzzy cognitive map-based decision support systems: A review. Information Sciences, 510, 297-314. 
[14] Erkan, E. F., Uygun, O. (2020), Scenario based examination of institutional leaning using fuzzy cognitive maps, Computers & Industrial Engineering, Volume 147, 106642, https://doi. org/10. 1016/j. cie. 2020. 106642. 
[15] Gkitzia, V., Apostolou, D., & Mentzas, G. (2018). Fuzzy cognitive map-based expert systems: A review. Expert Systems with Applications, 107, 111-129
[16] Delen, D., & Walker, G. (2019). Fuzzy cognitive maps in healthcare: A systematic literature review and bibliometric analysis. Artificial Intelligence in Medicine, 95, 23-38
[17] Kosko, B. (1986), Fuzzy cognitive maps, International Journal of Man-Machine Studies, Volume 24, Issue 1, 
pp. 65-75, https://doi. org/10. 1016/S0020-7373(86)80040-2. 
[18] Özesmi, U., Özesmi, S. L. (2004). Ecological models based on people's knowledge: a multi-step fuzzy cognitive mapping approach, Ecological Modelling, Vol. 176, Is. 1–2, pp. 43-64
[19] Falcone, P. M., et al. (2018). Dynamics of an olive oil bio-refinery in Andalusia: A multi-objective multi-agent approach. Sustainability, 10(3), 612. 
[20] Eraslan, E., Arıkan, A (2004). Point method in remuneration, seniority and success evaluation: Application in the internal production department of a manufacturing company. Journal of Gazi University Faculty of Engineering and Architecture, 19(2), 139 - 150. (ın Turkish)

Downloads: 14619
Visits: 321580

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.