Measuring Mental Workload Among Employees In The Furniture Industry Using The Nasa-Tlx Method

Authors

  • Dian Eko Hari Purnomo Politeknik Industri Furnitur dan Pengolahan Kayu Author
  • Yessi Nasia Ulfia Politeknik Industri Furnitur dan Pengolahan Kayu Author
  • Yogi Akbar Sunardiansyah Politeknik Industri Furnitur dan Pengolahan Kayu Author
  • YunHaoChuang National Chi Nan University Author
  • Kongkon Bordoloi Majuli College Author

Keywords:

mental workload, NASA-TLX, furniture industry, ergonomics, occupational workload

Abstract

Mental workload represents an important ergonomic factor in furniture manufacturing because employees perform tasks involving cognitive concentration, physical effort, time pressure, quality control, and production targets. This study aimed to measure mental workload among employees in the furniture industry using the National Aeronautics and Space Administration Task Load Index (NASA-TLX). A quantitative descriptive approach was applied, and workload was evaluated through six dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration. The NASA-TLX procedure involved pairwise weighting and rating of each dimension to determine the relative contribution of individual workload components and the overall workload score. The analytical framework emphasized the importance of interpreting aggregate workload together with dominant dimensions because similar total scores may arise from different occupational demands. The findings provide a basis for identifying work-system conditions requiring ergonomic improvement, including task complexity, physical exertion, production pacing, work organization, and psychological strain. The study demonstrates that NASA-TLX can support systematic evaluation of employee workload and provide practical information for improving ergonomic conditions, work allocation, operational efficiency, and occupational safety in furniture manufacturing environments.

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References

Azizah, F. N., Rinaldi, D. N., & Al-Muqaffa, F. W. (2025). Assessing mental workload of automotive factory employees using NASA-TLX and RSME methods. Jurnal Ilmiah Teknik Industri, 24(1), 107–116. https://doi.org/10.23917/jiti.v24i01.8363

Babaei, E., Dingler, T., Tag, B., & Velloso, E. (2025). Should we use the NASA-TLX in HCI? A review of theoretical and methodological issues around mental workload measurement. International Journal of Human-Computer Studies, 201, 103515. https://doi.org/10.1016/j.ijhcs.2025.103515

Badola, S., Mishra, V. N., Parkash, S., & Pandey, M. (2023). Rule-based fuzzy inference system for landslide susceptibility mapping along National Highway 7 in Garhwal Himalayas, India. Quaternary Science Advances, 11, 100093. https://doi.org/10.1016/j.qsa.2023.100093

Bai, Y., Kamarudin, K. M., & Alli, H. (2024). A systematic review of research on sitting and working furniture ergonomic from 2012 to 2022: Analysis of assessment approaches. Heliyon, 10(7), e28384. https://doi.org/10.1016/j.heliyon.2024.e28384

Berling, P., Johansson, L., & Marklund, J. (2023). Controlling inventories in omni/multi-channel distribution systems with variable customer order-sizes. Omega, 114, 102745. https://doi.org/10.1016/j.omega.2022.102745

Bernardino, L. F., & Skogestad, S. (2023). Bidirectional inventory control with optimal use of intermediate storage and minimum flow constraints. IFAC-PapersOnLine, 56(2), 2665–2670. https://doi.org/10.1016/j.ifacol.2023.10.1357

Berthon, L., Bernard, F., Fleury, S., Paquin, R., & Richir, S. (2025). Multi-dimensional measurement of mental workload in industrial context: An experiment in the field of helicopter maintenance. Applied Ergonomics, 129, 104599. https://doi.org/10.1016/j.apergo.2025.104599

Chattopadhyay, S. (2017). A neuro-fuzzy approach for the diagnosis of depression. Applied Computing and Informatics, 13(1), 10–18. https://doi.org/10.1016/j.aci.2014.01.001

Chenarboo, F. J., Hekmatshoar, R., & Fallahi, M. (2022). The influence of physical and mental workload on the safe behavior of employees in the automobile industry. Heliyon, 8(10), e11034. https://doi.org/10.1016/j.heliyon.2022.e11034

Dadashi, N., Lawson, G., Marshall, M., & Stokes, G. (2022). Cognitive and metabolic workload assessment techniques: A review in automotive manufacturing context. Human Factors and Ergonomics in Manufacturing & Service Industries, 32(1), 20–34. https://doi.org/10.1002/hfm.20928

Dombi, J., & Hussain, A. (2020). A new approach to fuzzy control using the distending function. Journal of Process Control, 86, 16–29. https://doi.org/10.1016/j.jprocont.2019.12.005

Fogelberg, E., Cao, H., & Thorvald, P. (2025). Cognitive ergonomics: Triangulation of physiological, subjective, and performance-based mental workload assessments. Frontiers in Industrial Engineering, 3, 1605975. https://doi.org/10.3389/fieng.2025.1605975

Han, H. (2023). Fuzzy clustering algorithm for university students’ psychological fitness and performance detection. Heliyon, 9(8), e18550. https://doi.org/10.1016/j.heliyon.2023.e18550

Hermansyah, M. S. A., & Handayani, N. U. (2022). NASA-TLX assessment of mental workload in manufacturing industry. Spektrum Industri, 20(2), 1–14. https://doi.org/10.12928/si.v20i2.43

Hernandez, R., Roll, S. C., Jin, H., Schneider, S., & Pyatak, E. A. (2022). Validation of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) adapted for the whole day repeated measures context. Ergonomics, 65(7), 960–975. https://doi.org/10.1080/00140139.2021.2006317

Kontesya, K., Sari, A. P., Ridhana, I., & Adelino, M. I. (2024). Evaluasi beban kerja mental pekerja yang terpapar kebisingan pada perusahaan mebel. Go-Integratif: Jurnal Teknik Sistem dan Industri, 5(1), 1–11. https://doi.org/10.35261/gijtsi.v5i01.11238

Kumar, D., Singh, J., & Pal, O. (2013). A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices. Mathematical and Computer Modelling, 57(11–12), 2945–2960. https://doi.org/10.1016/j.mcm.2013.03.002

Kumar, S., Maithani, R., & Kumar, A. (2021). Optimal design parameter selection for performance of alumina nano-material particles and turbulence promotors in heat exchanger: An AHP-TOPSIS technique. Materials Today: Proceedings, 43, 3152–3155. https://doi.org/10.1016/j.matpr.2021.01.654

Leva, M. C., Demichela, M., Comberti, L., & Caimo, A. (2022). Human performance in manufacturing tasks: Optimization and assessment of required workload and capabilities. Safety Science, 154, 105838. https://doi.org/10.1016/j.ssci.2022.105838

Luukka, P., & Stoklasa, J. (2024). Possibilistic mean based defuzzification for fuzzy expert systems and fuzzy control—LSD for general fuzzy sets. Journal of Computational and Applied Mathematics, 441, 115663. https://doi.org/10.1016/j.cam.2023.115663

Marchionni, M., Usman, M., Chai, L., & Tassou, S. A. (2023). Inventory control assessment for small-scale sCO₂ heat-to-power conversion systems. Energy, 267, 126537. https://doi.org/10.1016/j.energy.2022.126537

Memmedova, K. (2017). Fuzzy logic modelling of the impact of using technology on anxiety and aggression levels of students. Procedia Computer Science, 120, 495–501. https://doi.org/10.1016/j.procs.2017.11.270

Morton, J., Zheleva, A., Van Acker, B. B., Durnez, W., Vanneste, P., Larmuseau, C., De Bruyne, J., Raes, A., Cornillie, F., Saldien, J., De Marez, L., & Bombeke, K. (2022). Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context. Applied Ergonomics, 102, 103763. https://doi.org/10.1016/j.apergo.2022.103763

Nino, V., Claudio, D., & Monfort, S. M. (2023). Evaluating the effect of perceived mental workload on work body postures. International Journal of Industrial Ergonomics, 93, 103399. https://doi.org/10.1016/j.ergon.2022.103399

Nyakam Nya, D., Hachour, S., & Abouaïssa, H. (2022). Inventory control in supply chain: A model-free approach. IFAC-PapersOnLine, 55(10), 2755–2760. https://doi.org/10.1016/j.ifacol.2022.10.141

Prasetyo, R. A. B., & Iridiastadi, H. (2025). Mental workload: Definition and measurement review. International Journal of Technology, 16(5), 1854–1876. https://doi.org/10.14716/ijtech.v16i5.7072

Saravanan, S., Manoharan, J. S., Kumar, N. V., & Surenderanath, S. (2022). A dynamic fuzzy engine for adaptive control towards improvement of network performance in big data environment. Procedia Computer Science, 215, 24–32. https://doi.org/10.1016/j.procs.2022.12.003

Satriyono, R. D. A. P., Putri, A. S., & Bakhtiar, M. R. (2023). Analysis of mental workload on workers in service department using the NASA-TLX method. Metris: Jurnal Sains dan Teknologi, 24(2). https://doi.org/10.25170/metris.v24i02.4342

Teunter, R. H., & Kuipers, S. (2022). Inventory control with demand substitution: New insights from a two-product economic order quantity analysis. Omega, 113, 102712. https://doi.org/10.1016/j.omega.2022.102712

Theodorou, E., Spiliotis, E., & Assimakopoulos, V. (2023). Optimizing inventory control through a data-driven and model-independent framework. EURO Journal on Transportation and Logistics, 12, 100103. https://doi.org/10.1016/j.ejtl.2022.100103

Van Foreest, N. D., & Kilic, O. A. (2023). An intuitive approach to inventory control with optimal stopping. European Journal of Operational Research, 311(3), 921–924. https://doi.org/10.1016/j.ejor.2023.05.035

Yaqot, M., Menezes, B. C., & Kelly, J. D. (2023). Real-time coordination of multiple shuttle-conveyor-belts for inventory control of multi-quality stockpiles. Computers & Chemical Engineering, 178, 108388. https://doi.org/10.1016/j.compchemeng.2023.108388

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Published

2026-09-28

How to Cite

Measuring Mental Workload Among Employees In The Furniture Industry Using The Nasa-Tlx Method. (2026). Technema: Journal of Intelligent Engineering and Computing, 1(3), 417-427. https://sovereignresearch.org/technema/article/view/368