The design of wave soldering pallets (WSPs) for Printed Circuit Boards (PCBs) used in electronic devices is a laborious process for humans. This study aims to analyze mental workload and human error to recommend interventions that improve the design process. A hierarchical task analysis (HTA) decomposed the tasks, and the NASA Task Load Index (NASA-TLX) evaluated the mental workload. The systematic human error reduction and prediction approach (SHERPA) and the Human Error Calculator (HEC) were also applied. The creation of blueprint drawings has the highest probability of human error. Recommendations, such as modifying and standardizing information for drawings, were provided.
Arana-De las CasasA.-D. l. C.De la Riva-RodríguezJ.Maldonado-MacíasA. A.Sáenz-ZamarrónD. (2023). Cognitive Analyses for Interface Design Using Dual N-Back Tasks for Mental Workload (MWL) Evaluation, 20(2). https://doi.org/10.3390/ijerph20021184
2.
AtıcıU.AdemA.ŞenolM. B.DağdevirenM. (2023). A systematic review of cognitive ergonomics and safety: General trends and application areas. Gazi Universitesi fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 11(4), 1131–1161. https://doi.org/10.29109/gujsc.1378288
3.
Barajas-BustillosM.MaldonadoA.Ortiz-SolisM.Hernandez ArellanoJ.García-AlcarazJ. (2019). SHERPA and TAFEI, comparison of two human error identification techniques: A case study. In Carlos E. G., Elisa C. M. E. de. l. v. B., Francisco O. L. M. (eds.).Ergonomía Ocupacional: Investigaciones y Soluciones (Vol. 12, p. 657). Sociedad de Ergonomistas de México A.C.
4.
BommerS. C.FendleyM. (2018). A theoretical framework for evaluating mental workload resources in human systems design for manufacturing operations. International Journal of Industrial Ergonomics, 63, 7–17. https://doi.org/10.1016/j.ergon.2016.10.007
5.
CaoH.RiveraF. G.SöderlundH.BerlinC.StahreJ.JohanssonB. (2025). Human-centered design of VR interface features to support mental workload and spatial cognition during collaboration tasks in manufacturing. Cognition, Technology & Work, 27(3), 467–485. https://doi.org/10.1007/s10111-025-00809-6
6.
Durán-CoronadoA. A.Maldonado-MacíasA. A.Barajas-BustillosM. A.Hernández-ArellanoJ. L. (2019). Análisis cognitivos de carga mental e identificación del error humano para mejorar la experiencia de usuario. CienciaUAT, 14(1), 71–84. https://doi.org/10.29059/cienciauat.v14i1.1173
7.
Favre-FélixJ.DziadzkoM.DuclosA.LehotJ.-J.RimmeléT.LilotM. (2022). (In press). High-Fidelity Simulation to Assess Task Load Index and Performance: A Prospective Observational Study. Turkish Journal of Anaesthesiology and Reanimation, 50(4), 282–287. https://doi.org/10.5152/TJAR.2022.21234
8.
HancockG. M.LongoL.YoungM. S.HancockP. A., et al. (2021). Mental workload. In Handbook of Human Factors and Ergonomics (pp. 203–226). John Wiley & Sons, Inc. https://doi.org/10.1002/9781119636113.ch7
9.
HermansyahM. S. A.HandayaniN. 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
10.
HollnagelE. (2003). Handbook of cognitive task design. CRC Press.
11.
HungC.-L.DaiM. D.-M. (2024). Using SHERPA to predict human error on the maritime SAR helicopter hoist task. Heliyon, 10(11), Article e32043. https://doi.org/10.1016/j.heliyon.2024.e32043
12.
JafariM.-J.ZaeriF.JafariA. H.Payandeh NajafabadiA. T.Hassanzadeh-RangiN. (2019). Human-based dynamics of mental workload in complicated systems. EXCLI Journal, 18, 501–512. https://doi.org/10.17179/excli2019-1372
13.
LeãoC. P.SilvaV.CostaS. (2024). Exploring the intersection of ergonomics, design thinking, and AI/ML in design innovation. Applied System Innovation, 7(4), 65. https://doi.org/10.3390/asi7040065
14.
MusaviF.HekmatshoarR.FallahiM.MoradiA.Yazdani-AvalM. (2024). Identifying and preventing human error in the sugar production process: A multi-stage approach using HTA, HEC and PHEA techniques. Heliyon, 10(9), Article e29687. https://doi.org/10.1016/j.heliyon.2024.e29687
15.
Ortiz-SolisM.Maldonado-MaciasA. A.Barajas-BustillosM. A.Arredondo-SotoK.Carrillo-GutiérrezT. (2019). Human error identification and mental workload index setting during a computer power source change task: A case study. In KarwowskiW.TrzcielinskiS.MrugalskaB.Di NicolantonioM.RossiE. (Eds.), Advances in manufacturing, production management and process control (pp. 427–438). Springer International Publishing. https://doi.org/10.1007/978-3-319-94196-7_39
16.
RubioS.DiazE.MartinJ.PuenteJ. (2004). Evaluation of Subjective Mental Workload: A Comparison of SWAT, NASA-TLX, and Workload Profile Methods.Applied Psychology, 53(1), 61–86. https://doi.org/10.1111/j.1464-0597.2004.00161.x
17.
ShiraliG. H.JafariB.RaoufianF. (2021). Identification and evaluation of human errors of Epoxy control room operators of a pipe mill company using HEC technique. Occupational Medicine, 13(3), 57–66. https://doi.org/10.18502/tkj.v13i3.8199
18.
StantonN.SalmonP. M.RaffertyL. A. (2013). Human factors methods: A practical guide for engineering and design. Ashgate Publishing, Ltd.
19.
Torres-MedinaY. (2020). The analysis of human error in manufacturing: a key to improve production quality. Revista UIS Ingenierías, 19(4), 53–62. https://doi.org/10.18273/revuin.v19n4-2020005
20.
Van AckerB.ParmentierD.VlerickP.SaldienJ. (2018). Understanding mental workload: from a clarifying concept analysis toward an implementable framework. Cognition, Technology & Work, 20, 351–365. https://doi.org/10.1007/s10111-018-0481-3
21.
VidulichM. A.TsangP. S. (2012). Mental workload and situation awareness. In SalvendyEn G. (Ed.), Handbook of human factors and ergonomics (pp. 243–273). John Wiley & Sons, Inc. https://doi.org/10.1002/9781118131350.ch8
ZhangX.SchmidtL.SchlickC.ReuthR.LuczakH. (2008). A human task-oriented simulation study in autonomous production cells. International Journal of Production Research, 46(18), 5013–5041. https://doi.org/10.1080/00207540701222768
