Approach to predict product quality considering current customers’ expectations

• Autorzy: Siwiec Dominika , Pacana Andrzej , Bednárová Lucia

Identyfikatory

DOI

10.29119/1641-3466.2022.155.29

• Języki publikacji: EN

Abstrakty

EN

Purpose: The purpose was to develop an approach to predict product quality considering current customers' expectations. Design/methodology/approach: The approach includes integrated techniques, i.e.: SMART(-ER) method, a questionnaire with the Likert scale, brainstorming (B&M), WSM method, and Naïve Bayes Classifier. This approach refers to obtaining customers' expectations for satisfaction from the current quality of products and the importance of these criteria. Based on the satisfaction of customers, the quality of the product was estimated and classified. Then, the quality of the product was predicted for current customers. Findings: It was shown that it is possible to predict product quality based on current customer expectations, and so based on the current existing product. Research limitations/implications: The proposed approach does not include the possibilities of determining the expected quality of the product. The approach focuses on predicting customers' satisfaction with the current quality of the product. Therefore, if there is a need for improvement actions, further analyzes should be carried out to determine which criteria should be modified and how. Practical implications: The presented approach can be used for any product. Therefore, it is a useful tool for any kind of organization, which strives to meet customer satisfaction. Despite the possibility to predict the quality of the product, the proposed approach can indicate at an early stage to the organization that it is necessary to make improvement actions. Social implications: It is possible to reduce the waste of resources by predicting that improvement actions are necessary. Moreover, the approach supports an entity (e.g., expert, enterprise, interested parties) in predicting current customers' satisfaction. Originality/value: Originality is predicting product quality based on current customers' expectations. A new combination of quality management techniques, decision support, and machine learning was implemented.

Słowa kluczowe

EN

predict; product quality; decision support; naïve Bayesian classifier; weighted sum model; customer expectations

PL

przewidywanie; jakość produktu; wspomaganie decyzji; naiwny klasyfikator Bayesa; metoda sumy ważonej; oczekiwania klientów

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2022
• Tom: z. 155
• Strony: 461--472
• Opis fizyczny: Bibliogr. 34 poz.

Twórcy

autor

Siwiec Dominika

• Rzeszow University of Technology; Faculty of Mechanical Engineering and Aeronautics, Rzeszow, Poland

• https://orcid.org/0000-0002-6663-6621

autor

Pacana Andrzej

• Rzeszow University of Technology; Faculty of Mechanical Engineering and Aeronautics, Rzeszow, Poland

• https://orcid.org/0000-0003-1121-6352

autor

Bednárová Lucia

• Technical University of Technology; Faculty of Mining, Ecology, Process Control and Geotechnologies, Košice, Slovak Republic

• https://orcid.org/0000-0002-8582-0643

Bibliografia

• 1. Alexandrov, A. (2010). Characteristics of Single-Item Measures in Likert Scale Format. The Electronic Journal of Business Research Methods, 8(1), pp. 1-12.
• 2. Ding, J.W., Yang. D.T., and Bao, Z.Q. (2012). Research on Capturing of Customer Requirements Based on Innovation Theory. International Conference On Applied Physics And Industrial Engineering, 24, pp. 1868-1880. doi: 10.1016/j.phpro.2012.02.275.
• 3. Ellman, A., Wendrich, R., and Tiainen, T. (2014). Innovative Tool For Specifying Customer Requirements. Proceedings Of The Asme International Design Engineering Technical Conferences And Computers And Information In Engineering Conference, 1B.
• 4. Erkarslan, O., and Yilmaz, H. (2011). Optimization of production design through quality function deployment and analytical hierarchy process: case study of a ceramic washbasin. METU JFA, 28, 1, pp. 1-22. DOI: 10.40305/METU.JFA.2011.1.1.
• 5. Franceschini, F., Maisano, D., and Mastrogiacomo, L. (2015). Customer requirement prioritization on QFD: a new proposal based on the generalized Yager's algorithm. Research In Engineering Design, 26(2), pp. 171-197. doi: 10.1007/s00163-015-0191-2.
• 6. Geng, L.S., and Geng, L.X. (2018). Analyzing and Dealing with the Distortions in Customer Requirements Transmission Process of QFD. Mathematical Problems In Engineering. doi: 10.1155/2018/4615320.
• 7. Hauser, J. (1993). How Puritan-Bennet Used the House of Quality. Sloan Management Review, 34, 3, pp. 61-70.
• 8. Jiao, Y., Yang, Y., and Zhang, H.S. (2017). Mapping High Dimensional Sparse Customer Requirements into Product Configurations. International Conference On Artificial Intelligence Applications And Technologies (AIAAT 2017), 261. DOI: 10.1088/1757-899X/261/1/012022.
• 9. Jiao, Y., Yang, Y., Zhong, J., and Zhang, H.S. (2015). A Comparative Analysis of Intelligent Classifiers for Mapping Customer Requirements to Product Configurations. Proceedings Of 2017 International Conference On Big Data Research, pp. 72-77, DOI: 10.1145/3152723.3152726.
• 10. Keshavarz-Ghorabaee, M. (2021). Assessment of distribution center locations using a multi-expert subjective–objective decision-making approach. Sci. Rep., 11, 19461. https://doi.org/10.1038/s41598-021-98698-y .
• 11. Kumar, R. et al. (2021). Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component. Materials, vol. 14, 2084. https://doi.org/10.3390/ma14082084.
• 12. Lawlor, K.B., and Hornyak, M.J. (2012). Smart Goals: How the Application of Smart Goals Can Contribute to Achievement of Student Learning Outcomes. Dev. Bus. Simul. Exp. Learn., 39, pp. 259-267.
• 13. Li, Y.L., Chin, K.S., and Luo, X.G. (2012). Determining the final priority ratings of customer requirements in product planning by MDBM and BSC. Expert Systems With Applications, 39(1), pp. 1243-1255. doi: 10.1016/j.eswa.2011.07.133.
• 14. Melemez, K., Di, G., Esposito, G., and Lanzotti, A. (2014). Concept design in virtual reality of a forestry trailer using a QFD-TRIZ based approach. Turkish Journal of Agriculture and Forestry, 37(6), pp. 789-801, DOI: 10.3906/tar-1302-29.
• 15. Muttaqi’in, N., and Katias, P. (2021). Strategies to Improve Service Quality With House of Quality at Hotel X Surabaya. Business and Finance Journal, 6, 1, pp. 65-70. DOI: https://doi.org/10.33086/bfj.v6i1.1979.
• 16. Pacana, A., and Siwiec, D. (2021). Universal Model to Support the Quality Improvement of Industrial Products. Materials, 14, 7872. https://doi.org/10.3390/ma14247872.
• 17. Piątkowski, J.P. (2014). Modele inteligencji obliczeniowej dla zadań klasyfikacji danych: metody Bayesowskie. Toruń: Uniwersytet Mikołaja Kopernika, Wydział Fizyki, Astronomii i Informatyki Stosowanej.
• 18. Putman, V., Paulus, P. (2011). Brainstorming, Brainstorming Rules and Decision Making. Journal of Creative Bahavior, 43, 1, pp. 29-40. DOI: https://doi.org/10.1002/j.2162-6057.2009.tb01304.x.
• 19. Samarraie, H., and Hurmuzan, S. (2018). A review of brainstorming techniques in higher education. Thinking Skills and Creativity, 27, pp. 78-91.
• 20. Shi, Y.L., and Peng, Q.J. (2020). A spectral clustering method to improve importance rating accuracy of customer requirements in QFD. International Journal Of Advanced Manufacturing Technology, 107(5-6), pp. 2579-2596. DOI: 10.1007/s00170-020-05204-1.
• 21. Siwiec, D., and Pacana, A. (2021). A Pro-Environmental Method of Sample Size Determination to Predict the Quality Level of Products Considering Current Customers’ Expectations. Sustainability, 13, 5542. https://doi.org/10.3390/su13105542.
• 22. Siwiec, D., and Pacana, A. (2021). Model of Choice Photovoltaic Panels Considering Customers’ Expectations. Energies, 14, 5977. https://doi.org/10.3390/en14185977.
• 23. Siwiec, D., and Pacana, A. (2021). Model Supporting Development Decisions by Considering Qualitative–Environmental Aspects. Sustainability, 13, 9067. DOI: https://doi.org/10.3390/su13169067.
• 24. Song, W.Y., Ming, X.G., and Xu, Z.T. (2013). Integrating Kano model and grey-Markov chain to predict customer requirement states. Proceedings Of The Institution Of Mechanical Engineers Part B-Journal Of Engineering Manufacture, 227(8), pp. 1232-1244. DOI: 10.1177/0954405413485365.
• 25. Sun, N., Mei, X., and Zhang, Y. (2009). A simplified systematic method of acquiring design specifications from customer requirements. Journal of Computing and Information Science in Engineering, 9(3), 44105. doi: 10.1115/1.3184600.
• 26. Supriyono, H., and Sari, C. (1977). Developing Decision Support Systems Using the Weighted Product Method for House Selection. AIP Conference Proceedings, 020049. DOI: https://doi.org/10.1063/1.5042905.
• 27. Ulewicz, R., Siwiec, D., Pacana, A., Tutak, M., Brodny, J. (2021). Multi-Criteria Method for the Selection of Renewable Energy Sources in the Polish Industrial Sector. Energies, 14, 2386. DOI: https://doi.org/10.3390/en14092386.
• 28. Vilutiene, T., Zavadskas, E. (2003). The Application of multi-criteria analysis to decision support for the facility management of a residential district. Journal of Civil Engineering and Management, 9(4), pp. 241-252.
• 29. Wang, C. et al. (2014). Innovative design strategy based on customer requirements. Open Mechanical Engineering Journal, 8, pp. 930-935. doi: 10.2174/1874155X01408010930.
• 30. Wang, Y., and Tseng, M.M. (2014). Identifying Emerging Customer Requirements in an Early Design Stage by Applying Bayes Factor-Based Sequential Analysis. IEEE Transactions On Engineering Management, 61(1), pp. 129-137, DOI: 10.1109/ TEM.2013.2248729.
• 31. Wolniak, R. (2017). Application methods for analysis car accident in industry on the example of power. Support Systems in Production Engineering, 6(4), pp. 34-40.
• 32. Wu, H.H., and Shieh, J.I. (2006). Using a Markov chain model in quality function deployment to analyse customer requirements. International Journal Of Advanced Manufacturing Technology, 30(1-2), pp. 141-146. DOI: 10.1007/s00170-005-0023-z.
• 33. Yamagishi, K., Seki, K., and Nishimura H. (2018). Requirement analysis considering uncertain customer preference for Kansei quality of product. Journal Of Advanced Mechanical Design Systems And Manufacturing, 12(1), doi: 10.1299/jamdsm.2018 jamdsm0034.
• 34. Yang, Q., Bian, X.J., Stark, R., Fresemann, C., and Song, F. (2019). Configuration Equilibrium Model of Product Variant Design Driven by Customer Requirements. Symmetry-Basel, 11(4). DOI: 10.3390/sym11040508.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).