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Fuzzy Multi-criteria Decision Model to Support Product Quality Improvement

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Języki publikacji
EN
Abstrakty
EN
Improving product quality while making decisions remains a challenge. The objective of this research was to develop a model that supports the precise enhancement of product quality through comprehensive analysis of possibilities, product incompatibilities, root causes, and recommended improvement actions. The model incorporated various tools and methods such as the SMARTER method, expert team selection, brainstorming, Ishikawa diagram, 5M+E rule, FAHP, and FTOPSIS methods. The study demonstrated that integrating quality management tools and decision-making methods into a unified model enables the accurate prioritization of activities for product quality management. This integrated approach represents the novelty of this research. The model was evaluated using a mechanical seal made of 410 alloy. The research findings can be valuable to enterprises seeking to enhance product quality at any stage of production, particularly for modified or new products.
Twórcy
  • Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Poland
  • Cracow University of Economics, Faculty of Economics and International Relations, Poland
  • North-West University, NWU Business School, South Africa
  • Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Poland
Bibliografia
  • Alt R., Ehmke J. F., Haux R., Henke T., Mattfeld D., Overweis A., Peach B. and Winter A. (2019), Towards customer-induced service orchestration – requirements for the next step of customer orientation, Electronic Markets, No. 1, Vol. 29, pp. 79–91. doi: 10.1007/s12525-019-00340-3.
  • Bamford D.R. and Greatbanks R.W. (2005), The use of quality management tools and techniques: a study of application in everyday situations, International Journal of Quality & Reliability Management, No. 4, Vol. 22, pp. 376–392. doi: 10.1108/02656710510591219.
  • Basahel A. and Yaylan, O. (2016), Using Fuzzy AHP and Fuzzy TOPSIS Approaches for Assessing Safety Conditions at Worksites In Construction Industry, Int. J. Saf. Secur. Eng., No. 6, pp. 728–745.
  • Chang D-Y. (1996), Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research, No. 3, Vol. 95, pp. 649–655.
  • Chen C.-T. (2000), Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets Syst., No. 114, pp. 1–9.
  • Chetan S., Onkar SB. and Ucharia V. (2015), A quality approach to control casting defects in alloy wheels, International Journal of Science, Technology & Management, No. 6, Vol. 4, pp. 1–17.
  • Chokkalingam B., Boocendravarman S., Tamilselvan R. and Raja V. (2020), Apllication of Ishikawa diagram to investigate significant factors causing rough surface on sand casting, Proceedings on Engineering Sciences, No. 4, Vol. 2, pp. 353–360. doi: 10.24874/PES 0204.002.
  • Chokkalingam B., Raja V., Anburaj J., Immanual R. and Dhineshkumar M. (2017), Investigation of Shrinkage Defect in Castings by Quantitative Ishikawa Diagram, Archives of Foundry Engineering, No. 17, Vol. 1, pp. 174–178. doi: 10.1515/afe-2017-0032.
  • Gawdzińska K. (2011), Application of the Pareto chart and Ishikawa diagram for the identification of major defects in metal composite castings, Archives of Foundry Engineering, No. 11, Vol. 2, pp. 23–28.
  • Gawlik R. (2019a), Enhancing managerial decisionmaking through multicriteria modelling, Publishing house of the Cracow University of Technology, Kraków, ISBN 978-83-65991-41-6.
  • Gawlik R. (2019b), Making decisions under conditions of uncertainty, Entrepreneurship and Management, XX (1.2), pp. 7–19.
  • Gawlik R. (2019c), Smart and Green Buildings Features in the Decision-Making Hierarchy of Office Space Tenants: An Analytic Hierarchy Process Study, Administrative Sciences, No. 3, Vol. 9. doi: 10.3390/admsci9030052.
  • Hoła A., Sawicki M. and Szóstak M. (2018), Methodology of Classifying the Causes of Occupational Accidents Involving Construction Scaffolding Using Pareto-Lorenz Analysis, Appl. Sci., No. 8, Vol. 48. doi: 10.3390/app8010048.
  • Jarosińska M. and Berczyński S. (2021), Changes in Frequency and Mode Shapes Due to Damage in Steel– Concrete Composite Beam, Materials, Vol. 14, 6232. doi: 10.3390/ma14216232.
  • Kabir G. and Hasin M. (2011), Comparative Analysis Of AHP And Fuzzy AHP Models For Multicriteria Inventory Classification, International Journal of Fuzzy Logic Systems, No. 1, Vol. 1, pp. 1–16.
  • Katunin A., Dragan K., Nowak T. and Chalimoniuk M. (2021), Quality Control Approach for the Detection of Internal Lower Density Areas in Composite Disks in Industrial Conditions Based on a Combination of NDT Techniques, Sensors, Vol. 21, 7174. doi: 10.3390/s21217174.
  • Khedmatgozar S.S., Caluk N., Mehrabi A. and Khedmatgozar S.S. (2021), Non-Destructive Testing Applications for Steel Bridges, Appl. Sci., Vol. 11, 9757. doi: 10.3390/app11209757.
  • Khorramrouz F., Pourmahdi N., Rahiminezhad M. and Rafiei F. (2019), Application of fuzzy analytic hierarchy process (FAHP) in failure investigation of knowledge-based business plans, Appl. Sci., Vol. 1, 1386. doi: 10.1007/s42452-019-1394-3.
  • Krajewska-Śpiewak J., Turek J. and Gawlik J. (2021), Maintenance supervision of the dies condition and technological quality of forged products in industrial conditions, Management and Production Engineering Review, No. 2, Vol. 12, pp. 27–32. doi: 10.24425/mper.2021.137675.
  • Kupraszewicz W. and Zółtowski B. (2002), Selection of a team of experts to diagnose the condition of machines. Diagnostics ’26 – Main articles, Vol. 26, pp. 94–100.
  • Kusumawardani R.P. and Agintiara M. (2015), Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process, Procedia Comput. Sci., Vol. 72, pp. 638–646.
  • Lawlor K. B. and Hornyak M., J. (2012), Smart Goals: How The Application Of Smart Goals Can Contribute To Achievement Of Student Learning Outcomes, Developments in Business Simulation and Experiential Learning, Vol. 39, pp. 259–267.
  • Lin C.-N. (2020), A Fuzzy Analytic Hierarchy ProcessBased Analysis of the Dynamic Sustainable Management Index in Leisure Agriculture, Sustainability, No. 12, 5395. doi: 10.3390/su12135395.
  • Liu Y., Eckert C. and Earl C. (2020), A review of fuzzy AHP methods for decision-making with subjective judgements, Expert Systems with Applications, Vol. 161. doi: 10.1016/j.eswa.2020.113738.
  • Luca L. and Luca T.O. (2019), Ishikawa diagram applied to identify causes which determines bearings defects from car wheels, IOP Conference SeriesMaterials Science and Engineering, No. 564, 012093. doi: 10.1088/1757-899X/564/1/012093.
  • Minutolo V., Ronza S., Eramo C. and Zona R. (2019), The use of destructive and non-destructive testing in concrete strength assessment for a school building, International Journal of Advanced Research in Engineering and Technology, No. 6, Vol. 10, pp. 252–267.
  • Naik G. (2017), Process Improvement in Casting through Defect Minimization: A case study, International Journal of Scientific and Engineering Research, No. 4, Vol. 8
  • Naro R. L. (1999), Porosity Defects in Iron Castings From Mold-Metal Interface Reactions, AFS Transactions, No. 206, Vol. 99, pp. 839–850.
  • Nugroho E., Marwanto A. and Hasibuan S. (2017), Reduce Product Defect in Stainless Steel Production, Using Yield Management Method and PDCA. International Journal of New Technology and Research (IJNTR), No. 11, Vol. 3, pp. 39–46.
  • Olejarz T., Siwiec D. and Pacana A. (2022), Method of Qualitative–Environmental Choice of Devices Converting Green Energy, Energies, Vol. 15, 8845. doi: 10.3390/en15238845.
  • Ostasz G., Siwiec D. and Pacana A. (2022a), Model to Determine the Best Modifications of Products with Consideration Customers’ Expectations, Energies, Vol. 15, 8102. doi: 10.3390/en15218102.
  • Ostasz G., Siwiec D. and Pacana A. (2022b), Universal Model to Predict Expected Direction of Products Quality Improvement, Energies, Vol. 15, 1751. doi: 10.3390/en15051751.
  • Pacana A, Siwiec D. and Bednarova L. (2020), Method of Choice: A Fluorescent Penetrant Taking into Account Sustainability Criteria, Sustainability, No. 14, Vol. 12, 5854. doi: 10.3390/su12145854.
  • Pacana A. and Siwiec D. (2021a), Analysis of the Possibility of Used of the Quality Management Techniques with not-destructive testing, Tehnicki vjestnik, No. 1, Vol. 28, pp. 45–51. doi: 10.17559/TV20190714075651.
  • Pacana A. and Siwiec D. (2022a), Method of Determining Sequence Actions of Products Improvement, Materials, Vol. 15, 6321. doi: 10.3390/ma15186321.
  • Pacana A. and Siwiec D. (2022b), Model to Predict Quality of Photovoltaic Panels Considering Customers’ Expectations, Energies, Vol. 15, 1101. doi: 10.3390/en15031101.
  • Pacana A. and Siwiec D. (2021b), Universal Model to Support the Quality Improvement of Industrial Products, Materials, Vol. 14, 7872. doi: 10.3390/ma14247872.
  • Pacana A. and Siwiec D. (2022c), Method of Determining Sequence Actions of Products Improvement, Materials, Vol. 15, 6321. doi: 10.3390/ma15186321.
  • Pacana J., Siwiec D. and Pacana A. (2023), Numerical Analysis of the Kinematic Accuracy of the Hermetic Harmonic Drive in Space Vehicles, Appl. Sci., Vol. 13, 1694. doi: 10.3390/app13031694.
  • Parkan C. and Wu M.L. (2000), Comparison of three modern multicriteria decision-making tools, International Journal of Systems Science, No. 4, Vol. 31, pp. 497–517. doi: 10.1080/002077200291082.
  • Peças P., Encarnação J., Gambôa M., Sampayo M. and Jorge D. (2021), PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm, Appl. Sci., Vol. 11, 7671. doi: 10.3390/app11167671.
  • Pinho T., Zhiltsova T., Oliveira M. and Costa A. (2021), Computer-Aided Reengineering towards Plastic Part Failure Minimization, Materials, Vol. 14, 6303. doi: 10.3390/ma14216303.
  • Poucher Z., Tamminen K., Caron J. and Sweet S. (2020), Thinking through and designing qualitative research studies: a focused mapping review of 30 years of qualitative research in sport psychology, International Review of Sport and Exercise Psychology, No. 1, Vol. 13, pp. 163–186. doi: 10.1080/1750984X.2019.1656276.
  • Putman V. and Paulus P. (2008), Brainstorming, Brainstorming, Rules and Decision Making. Journal of Creative Behavior, pp. 1–17.
  • Radej B., Drnovsek J. and Beges G. (2017), An overview and evaluation of quality-improvement methods from the manufacturing and supply-chain perspective, Advances in Production Engineering & Management, No. 4, Vol. 12, pp. 388–400. doi: 10.14743/apem2017.4.266.
  • Raji R., Shaheed A., Pradeep P. and Pramod V. (2018), An over view of casting defects in automatic highpressure line, International Journal of Latest Technology in Engineering, Management & Applied Science, No. 4, Vol. 7, pp. 263–268.
  • Ruszaj A., Gawlik J. and Skoczypiec S. (2016), Electrochemical Machining – Special Equipment and Applications in Aircraft Industry, Management and Production Engineering Review, No. 2, Vol. 7, pp. 34–41. doi: 10.1515/mper-2016-0015.
  • Saaty T. (1997), A scaling method for priorities in hierarchical structures, J. Math. Psychol., Vol. 15, pp. 234– 281. DOI:10.1016/0022-2496(77)90033-5
  • Sheth H., Shah K., Sathwara D. and Trivedi R. (2015), Investigation, Analysis of Casting Defect by Using Statistical Quality Control Tools, Introduction concept of lean six sigma and feedback system, No. 4, Vol. 3, pp. 247–254.
  • Sidhant A.K. and Bhushan S.K., (2018), Diagnostic approach towards analyzing casting defects – An Industrial case Study, International journal of Advanced Research in Science, Engineering and Technology, No. 4, Vol. 5, pp. 5616–5625.
  • Siekański K. and Borkowski S. (2003), Analysis of the foundry defects and preventive activities for quality improvement of casings, Metalurgija, No. 1, Vol. 42, pp. 57–59.
  • Siwiec D. and Pacana A. (2021a), Method of improve the level of product quality, Production Engineering Archives, No. 1, Vol. 27, pp. 1–7. doi: 10.30657/pea.2021.27.1.
  • Siwiec D. and Pacana A. (2020b), Identifying the source of the problem by using implemented the FAHP method in the selected quality management techniques, Production Engineering Archives, No. 1, Vol. 26, pp. 5–10. doi: 10.30657/pea.2020.26.02.
  • Siwiec D. and Pacana A. (2021b), A Pro-Environmental Method of Sample Size Determination to Predict the Quality Level of Products Considering Current Customers’ Expectations, Sustainability, No. 13, Vol. 5542. doi: 10.3390/su13105542.
  • Siwiec D. and Pacana A. (2021c), Model of Choice Photovoltaic Panels Considering Customers’ Expectations, Energies, No. 14, Vol. 5977. doi: 10.3390/en14185977.
  • Siwiec D. and Pacana A. (2021d), Model Supporting Development Decisions by Considering QualitativeEnvironmental Aspects, Sustainability, No. 13, Vol. 9067. doi: 10.3390/su13169067.
  • Siwiec D. and Pacana A. (2022), A new model supporting stability quality of materials and industrial products, Materials, No. 3, Vol. 15, 4440. doi: 10.3390/ma15134440.
  • Sygut P. (2017), Analysis of factors lowering the level of quality during welding pipe with seam, METAL 2017: 26th International Conference on metallurgy and materials, Brno Czech Republic, May 24-26, pp. 2321–2326.
  • Tegegne A. and Shing A. (2013), Experimental analysis and Ishikawa diagram for burn on effects on managanse silicon alloy medium cabron steel shaft, International Journal for Quality Research, No. 4, Vol. 7, pp. 545–558.
  • Tsai H.-C., Lee A.-S., Lee H.-N., Chen C.-N. and Liu Y.-C. (2020), An Application of the Fuzzy Delphi Method and Fuzzy AHP on the Discussion of Training Indicators for the Regional Competition. Taiwan National Skills Competition, in the Trade of Joinery, Sustainability, No. 10, Vol. 12, 4290. doi: 10.3390/su12104290.
  • Ulewicz R. (2003), Quality control system in production of the castings from spheroid cast iron, Metalurgija, No. 1, Vol. 42, pp. 61–63.
  • Ulewicz R., Siwiec D., Pacana A., Tutak M. and Brodny J. (2021), Multi-Criteria Method for the Selection of Renewable Energy Sources in the Polish Industrial Sector, Energies, Vol. 14, 2386. doi: 10.3390/en14092386.
  • Wolniak R. (2019), The Level of Maturity of Quality Management Systems in Poland—Results of Empirical Research, Sustainability, No. 11, Vol. 4239. doi: 10.3390/su11154239.
  • Wotzka D., Kozioł M., Boczar T., Kunicki M. and Nagi Ł. (2021), Latest Trends in the Improvement of Measuring Methods and Equipment in the Area of NDT, Sensors, No. 21, Vol. 7293. doi: 10.3390/s21217293.
  • Yunus R., Samadi Z., Yusop N. and Omar D. (2013), Expert choice of ranking heritage streets, Procedia - Social and Behavioral Sciences, Vol. 101, pp. 465-475. doi: 10.1016/j.sbspro.2013.07.220.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cf13a686-e163-4cc6-885f-50714c404146
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