Development of a Competency Assessment Model for Measurement of the Human Inspection Skill

• Autorzy: Ramzan Muhammad Babar , Kang Yuncheol , Iqbal Muhammad Waqas

Identyfikatory

DOI

10.5604/01.3001.0014.6074

Warianty tytułu

PL

Opracowanie modelu oceny kompetencji do pomiaru umiejętności inspekcji wizualnej wykonywanej przez człowieka

• Języki publikacji: EN

Abstrakty

EN

Extensive research has already been done on visual inspection as well as on the effect of different factors on human inspection performance. However, a method should be developed to measure their inspection skill based on influencing factors. This study contributes to the literature by proposing a competency assessment model based on the influencing factors that can classify human labour into its respective skill levels. From the literature review, the influencing factors of visual inspection are listed and divided into five observed variables. A team of experts selected the significant factors with respect to the textile and clothing industry. The analytical hierarchy process is used to measure their respective weights so as to calculate the inspection performance in terms of a competency score. A numerical example is presented and the model proposed successfully determined the competency score, and inspectors are classified into their respective skill levels according to the well-defined cut-off values. This study enables organisations to classify available human labour into its skill levels and utilise them according to their capacity.

PL

Dotychczas przeprowadzono szeroko zakrojone badania dotyczące inspekcji wizualnej, a także wpływu różnych czynników na wyniki inspekcji wykonywanej przez człowieka. W pracy skupiono się na opracowaniu metody pomiaru umiejętności inspekcji w oparciu o czynniki na nie wpływające. Praca wnosi wkład do literatury, proponując model oceny kompetencji oparty na czynnikach wpływających, które mogą sklasyfikować ludzką pracę według odpowiednich poziomów umiejętności. Na podstawie przeglądu literatury wyszczególniono czynniki wpływające na kontrolę wzrokową i podzielono je na pięć obserwowanych zmiennych. Zespół ekspertów wybrał istotne czynniki w branży tekstylno-odzieżowej. Proces hierarchii analitycznej posłużył do pomiaru ich odpowiednich wag, w celu obliczenia wyników inspekcji w kategoriach oceny kompetencji. Przedstawiono przykład liczbowy, a zaproponowany model z powodzeniem określił punktację kompetencji, tak aby inspektorzy mogli być klasyfikowani według ich odpowiednich poziomów umiejętności zgodnie z dobrze określonymi wartościami granicznymi. Badanie umożliwiło organizacjom klasyfikowanie dostępnej siły roboczej według poziomów umiejętności i wykorzystywanie jej zgodnie z ich możliwościami.

Słowa kluczowe

EN

quality control; visual inspection; decision making; inspection skill; competency assessment

PL

kontrola jakości; inspekcja wizualna; podejmowanie decyzji; umiejętność inspekcji; ocena kompetencji

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2021
• Tom: Vol. 29, no. 2 (146)
• Strony: 10--19
• Opis fizyczny: Bibliogr. 45 poz., rys., tab.

Twórcy

autor

Ramzan Muhammad Babar

• National Textile University, Department of Garment Manufacturing, Pakistan

• https://orcid.org/0000-0002-0401-4253

autor

Kang Yuncheol

• Ewha Womans University, School of Business, Shinsegae Bldg. #419 52, Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, South Korea

autor

Iqbal Muhammad Waqas

• National Textile University, Department of Yarn Manufacturing, Pakistan

• https://orcid.org/0000-0001-6390-585X

Bibliografia

• 1. Park KS. Human reliability: Analysis, Prediction, and Prevention of Human Errors: Elsevier 2014.
• 2. Jiang X, Gramopadhye AK, Melloy BJ, Grimes LW. Evaluation of Best System Performance: Human, Automated, and Hybrid Inspection Systems. Hum Factors Ergon Manuf Serv Ind. 2003; 13(2): 137-52.
• 3. Lindblad M. Human Inspection Work: A Case Study of Why Faults are Missed? 2006.
• 4. Khan M, Jaber MY, Ahmad AR. An Integrated Supply Chain Model with Errors in Quality Inspection and Learning In Production. Omega-Int J Manage S. 2014; 42(1): 16-24.
• 5. Anily S, Grosfeld-Nir A. An Optimal Lot -Sizing and Offline Inspection Policy in the Case of Nonrigid Demand. Oper Res. 2006; 54(2): 311-23.
• 6. Sun C, Hou J. An Improved Principal Component Regression for Quality-Related Process Monitoring of Industrial Control Systems. IEEE Access. 2017; 5: 21723-30.
• 7. Purushothama B. A Practical Guide to Quality Management in Spinning: Woodhead Publishing India; 2011.
• 8. Ramzan MB, Kang CW. Minimization of Inspection Cost by Determining the Optimal Number of Quality Inspectors in the Garment Industry. Indian Journal of Fibre and Textile Research 2016; 41(3): 346-50.
• 9. Park KS. Human reliability. Amsterdam, The Netherlands: Elsevier Science Publishers; 1987. 341 p.
• 10. Pan R, Gao W, Liu J, Wang H. Automatic Inspection of Woven Fabric Density of Solid Colour Fabric Density by the Hough Transform. FIBRES & TEXTILES in Eastern Europe 2010, 18, 4(81): 46-51.
• 11. Çelik HI, Topalbekiroğlu M, Dülger LC. Real-Time Denim Fabric Inspection Using Image Analysis. FIBRES & TEXTILES in Eastern Europe 2015; 23, 3(111): 85-90. DOI: 10.5604/12303666.1152514
• 12. Eldessouki M, Hassan M, Qashqari K, Shady E. Application of Principal Component Analysis to Boost the Performance of an Automated Fabric Fault Detector and Classifier. FIBRES & TEXTILES in Eastern Europe 2014; 22, 4(106): 51-57.
• 13. Harris DH. The Nature of Industrial Inspection1. Human Factors: The Journal of the Human Factors and Ergonomics Society 1969; 11(2): 139-48.
• 14. See JE. Visual Inspection: A Review of the Literature. Sandia National Laboratories, 2012.
• 15. Kaufman J, Gramopadhye A, Kimbler D. Using Training to Improve Inspection Quality. Qual Eng. 2000; 12(4): 503-18.
• 16. Garrett SK, Melloy BJ, Gramopadhye AK. The Effects of Per-Lot and Per-Item Pacing on Inspection Performance. Int J Ind Ergon. 2001; 27(5): 291-302.
• 17. See JE. Visual Inspection Reliability for Precision Manufactured Parts. Human Factors: The Journal of the Human Factors and Ergonomics Society 2015; 57(8): 1427-42.
• 18. Dhillon BS. Human reliability: with Human Factors: Elsevier 2013.
• 19. Heidl W, Thumfart S, Lughofer E, Eitzinger C, Klement EP. Machine Learning Based Analysis of Gender Differences in Visual Inspection Decision Making. Information Sciences 2013; 224: 62-76.
• 20. Wu S-P, Lin Y-H. The Effect Of Defect Complexity On Inspection Performance. Journal of Ergonomic Study 2012; 14(1): 39-47.
• 21. Czaja S, Drury C. Training programs for inspection. Human Factors. The Journal of the Human Factors and Ergonomics Society 1981; 23(4): 473-83.
• 22. Chan AH, Chiu CH. Visual Lobe Shape Characteristics of Experienced Industrial Inspectors and Inexperienced Subjects. Hum Factors Ergon Manuf Serv Ind. 2010; 20(5): 367-77.
• 23. Lin CL, Chen FS, Twu LJ, Wang MJJ. Improving SEM Inspection Performance in Semiconductor Manufacturing Industry. Hum Factors Ergon Manuf Serv Ind. 2014; 24(1): 124-9.
• 24. Gallwey T, Drury CG. Task Complexity in Visual Inspection. Human Factors. The Journal of the Human Factors and Ergonomics Society 1986; 28(5): 595-606.
• 25. Pesante JA, Williges RC, Woldstad JC. The Effects of Multitasking on Quality Inspection in Advanced Manufacturing Systems. Hum Factors Ergon Manuf Serv Ind. 2001; 11(4): 287-98.
• 26. Master R, Jiang X, Khasawneh MT, Bowling SR, Grimes L, Gramopadhye AK, et al. Measurement of Trust over Time in Hybrid Inspection Systems. Hum Factors Ergon Manuf Serv Ind. 2005; 15(2): 177-96.
• 27. Rao P, Bowling SR, Khasawneh MT, Gramopadhye AK, Melloy BJ. Impact of Training Standard Complexity on Inspection Performance. Hum Factors Ergon Manuf Serv Ind. 2006; 16(2): 109-32.
• 28. Tetteh E, Jiang X, Mountjoy D, Seong Y, McBride M. Evaluation of a Job-Aiding Tool in Inspection Systems. Hum Factors Ergon Manuf Serv Ind. 2008; 18(1): 30-48.
• 29. Watanapa A, Kaewkuekool S, Suksakulchai S, editors. Influence of Training with and without Reward on Visual Inspector’s Performance In 3 Dimension Model. Applied Mechanics and Materials 2012; Trans Tech Publ.
• 30. Sadasivan S, Gramopadhye AK. Can We use Technology to Train Inspectors to be More Systematic? Digital Human Modeling: Springer; 2007. p. 959-68.
• 31. Sadasivan S, Gramopadhye AK. Technology to Support Inspection Training in the General Aviation Industry: Specification and Design. Int J Ind Ergon. 2009; 39(4): 608-20.
• 32. Chabukswar S, Gramopadhye AK, Melloy BJ, Grimes LW. Use of Aiding and Feedback in Improving Visual Search Performance for an Inspection Task. Hum Factors Ergon Manuf Serv Ind. 2003; 13(2): 115-36.
• 33. Wang M-JJ, Lin S-C, Drury CG. Training for Strategy in Visual Search. Int J Ind Ergon. 1997; 20(2): 101-8.
• 34. Gramopadhye AK, Wilson K. Noise, Feedback Training, and Visual Inspection Performance. Int J Ind Ergon. 1997; 20(3): 223-30.
• 35. Nalanagula D, Greenstein JS, Gramopadhye AK. Evaluation of the Effect of Feedforward Training Displays of Search Strategy on Visual Search Performance. Int J Ind Ergon. 2006; 36(4): 289-300.
• 36. Charles RL, Johnson TL, Fletcher SR. The use of Job Aids for Visual Inspection in Manufacturing and Maintenance. Procedia CIRP 2015; 38: 90-3.
• 37. Ma J, Drury CG, Bisantz AM, editors. Impact of Feedback Training in CBT in Visual Inspection. Proceedings of the Human Factors and Ergonomics Society Annual Meeting; 2002: SAGE Publications.
• 38. Drury CG, Green BD, Chen J, Henry EL, editors. Sleep, Sleepiness, Fatigue, and Vigilance in a Day and Night Inspection Task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting; 2006: Sage Publications.
• 39. Bhuvanesh A, Khasawneh MT, editors. Performance Assessment of Humans in Leadframe Inspection: A Preliminary Study. Proceedings of the Human Factors and Ergonomics Society Annual Meeting; 2006: Sage Publications.
• 40. Mitzner TL, Touron DR, Rogers WA, Hertzog C, editors. Checking it Twice: Age-Related Differences in Double Checking During Visual Search. Proceedings of the Human Factors and Ergonomics Society Annual Meeting; 2010: SAGE Publications.
• 41. Ramzan MB. Composite Modeling for Evaluation of Human Based Inspection Systems: Graduate School of Hanyang University; 2017.
• 42. Saaty TL. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process: Rws Publications; 2000.
• 43. Albayrak E, Erensal YC. Using Analytic Hierarchy Process (AHP) to Improve Human Performance: an Application of Multiple Criteria Decision Making Problem. Journal of Intelligent Manufacturing 2004; 15(4): 491-503.
• 44. Saaty TL. Decision Making with the Analytic Hierarchy Process. International Journal of Services Sciences 2008; 1(1): 83-98.
• 45. Golden BL, Wasil EA, Harker PT. Analytic Hierarchy Process, Springer, 2003.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).