Prediction of quality level of product considering current customers’ expectations
DOI:
https://doi.org/10.24425/mper.2022.142061Abstract
The activities of the organisation concentrate mainly on meeting customers’ requirements. For this purpose, various activities are being conducted for customer satisfaction surveys. In this context, it is important to predict the quality of the product and the changes in the cost of the purchase product. The purpose of this study is to propose a method for predicting the quality level of a product and change the cost of the product considering current customers’ requirements for a combination of product feature states and pro-quality changes. The method includes the calculation of the quality level of the product using the punctationformalised method, where the level depends on a combination of values of states (parameters) attributes of the product, that is, current and modified. The method was tested as an example of a household vacuum cleaner for which 20 attributes were determined. According to the Pareto rule (20/80), the four product attributes important for customers were selected. Thereafter, for important attributes, possible combinations of the values of these attributes were determined. In addition, an algorithm for determining the possible combinations of product attribute states in the MATLAB program was developed. Additionally, the change in the current cost of the product considering the change in the quality level was estimated. The product cost changes were determined based on the actual cost of the product and the current product quality level. The method allows the determination of all combinations of values of state attributes of the product, such that it is possible to take appropriate improvement actions both in terms of quality and cost. The results from the method allow the prediction of product satisfaction for customers and they are favourable in terms of production cost. Therefore, it is possible to design the product in advance and support the producer in preparatory activities.References
Ali A., Hafeez Y., Hussain S., and Yang S. (2020), Role of Requirement Prioritization Technique to Improve the Quality of Highly-Configurable Systems, IEEE Access, No. 8, pp. 27549–27573. DOI: 10.1109/ACCESS.2020.2971382
Aliyu R., Arifin A., Haq R., Hassan M., Rahman M., Ismail A., Rahim M., Ibrahim M., Azlan M., Ahmad M. et al. (2019), An integration of kano model and quality function deployment technique – A case study using sport earphone, Journal of Physics, Vol. 1150, No. 1, pp. 1–9. DOI: 10.1088/1742-6596/1150/1/012025
Amineha H. and Kosach N. (2016), Assessment of Consumers’ Satisfaction with the Automotive Product Quality, International Journal Of Environmental & Science Education, No. 16, Vol. 11, pp. 8726–8739.
Aoyama K., Matshuda N., and Koga T. (2010), A Design Method of Product Family for Unpredictable Customer Requirements Using Fuzzy Sets. In NEW World Situation: New Directions In Concurrent Engineering, ed. J. Pokojski et al., pp. 193–201. London: Springer-Verlag. DOI: 10.1007/978-0-85729-024-3_19
Bils M. and Chang Y. (2000), Understanding how price responds to cost and production, Carnegie-Rochester Conference Series on Public Policy, No. 52, pp. 33–77. DOI: 10.1016/S0167-2231(00)00016-6
Chen C. and Wang L. (2008), Integrating rough set clustering and grey model to analyse dynamic customer requirements, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 222, No. 2, pp. 319–332. DOI: 10.1243/09544054JEM826
Chen C.H., Khoo L.P., and Yan W. (2003), Evaluation of multicultural factors from elicited customer requirements for new product development, Research In Engineering Design Theory Applications And Concurrent Engineering, Vol. 14, No. 30, pp. 119–130. DOI: 10.1007/s00163-003-0032-6
Hansen E. and Bush R.J. (1999), Understanding customer quality requirements – Model and application, Industrial Marketing Management, Vol. 28, No. 2, pp. 119–130. DOI: 10.1016/S0019-8501(98)00007-8
Hardesty S. and Leff P. (2010), Determining marketing costs and returns in alternative marketing channels, Renewable Agriculture and Food Systems, Vol. 25 No. 01. DOI: 10.1017/S1742170509990196
He L., Ming X., Li M., Zheng M., and Xu Z. (2017), Understanding customer requirements through quantitative analysis of an improvement fuzzy Kano’s model, Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, Vol. 231, No. 4, pp. 699–712. DOI: 10.1177/0954405415598894
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.,Vol. 8, No. 48. DOI: 10.3390/app8010048
Huang C.-L., Chen Y.-H., Tseng C.-H., Wan T.-L., and Shen M. (2017), The hybrid algorithm for product design in multimedia, International Journal of Applied Systemic Studies, No. 1-3, Vol. 7, pp. 78–91. DOI: 10.1504/IJASS.2017.088902
Jaravel X. and O’Connell M. (2020), Real-time price indices: Inflation spike and falling product variety during the Great Lockdown, J Public Econ. Vol. 191. DOI: 10.1016/j.jpubeco.2020.104270
Jiao J.X. and Chen C.H. (2006), Customer requirement management in product development: A review of research issues, Concurrent Engineering-Research And Applications, Vol. 14, No. 3, pp. 173–185. DOI: 10.1177/1063293X06068357
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, Vol. 261, pp. 1–9. DOI: 10.1088/1757-899X/261/1/012022
Kijewska J. and Mierzwiak R. (2014), The Empirical Verification of the Kolman’s Universal Interpretative Scale, Przedsiębiorczość i Zarządzanie, Vol. 15, No. 10, pp. 335–350.
Kolman, R.R. (1992), Quality Engineering, Warsaw, Poland: PWE.
Kwong C.K. and Bai H. (2002), A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment, Journal Of Intelligent Manufacturing, Vol. 13, No. 5, pp. 367–377. DOI: 10.1023/A:1019984626631
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.
Lee C.H., Chen C.H., Lin CY., Li F., and Zhao X.J. (2019), Developing a Quick Response Product Configuration System under Industry 4.0 Based on Customer Requirement Modelling and Optimization Method, Applied Sciences-Basel, Vol. 23, No. 9. DOI: 10.3390/app9235004
Li W., Pomegbe W., and Dogbe C. (2019), Employees’ customer orientation and customer satisfaction in the public utility sector: The mediating role of service quality, African Journal of Economic and Management Studies, Vol. 10, No. 4, pp. 408–423. DOI: 10.1108/AJEMS-10-2018-0314
Li Z. and Tian H. (2019), Research on Fuzzy Hierarchy Optimization Model of Product Family Parameters Based on Flexible Design of Clothing, IOP Conference Series: Materials Science and Engineering, Vol. 573, No. 1. DOI: 10.1088/1757-899X/573/1/012002
Mishra P. Pandey C. Keshri A., and Sabaretnam M. (2019), Selection of Appropriate Statistical Methods for Data Analysis, Ann Card Anaesth, Vol. 22, No. 3, pp. 297–301. DOI: 10.4103/aca.ACA_248_18
Mu E., and Pereyra-Rojas M. (2017), Practical Decision Making. In Springer Briefs in Operations Research, Appendix A: Practical Questions Related to AHP Modeling, ed. E. Mu and M. Pereyra-Rojas, 105–106. Switzerland: Springer Nature: Basel.
Pacana A., Siwiec D., and Bednárová L. (2020), Method of Choice: A Fluorescent Penetrant Taking into Account Sustainability Criteria, Sustainability, Vol. 12, No. 14, p. 5854. DOI: 10.3390/su12145854
Pacana A. and Ulewicz R. (2020), Analysis of causes and effects of implementation of the quality management system compliant with ISO 9001, Polish Journal Of Management Studies, Vol. 21, No. 1, pp. 283–296. DOI: 10.17512/pjms.2020.21.1.21
Pugna A., Potra S., Negrea R., Miclea Ş., and Mocan M. (2016), A Refined Quality Attribute Classification Model for New Product and Service Strategic Design, Procedia Computer Science, Vol. 91, pp. 296–305. DOI: 10.1016/j.procs.2016.07.080
Realyvásquez-Vargas A., Arredondo-Soto K.C., CarrilloGutiérrez T., and Ravelo G. (2018), Applying the Plan-Do-Check-Act (PDCA) Cycle to Reduce the Defects in the Manufacturing Industry. A Case Study, Appl. Sci. Vol. 8, No. 11, p. 2181. DOI: 10.3390/app8112181
Roder B., Heidl M.J., and Birkhofer H. (2013), PreAcquisition Clustering of Requirements – Helping Customers to Realize What They Want, Design for Harmonies, Vol. 7: Human Behaviour in Design, pp. 407–416.
Shi Y. and Peng Q. (2020), A spectral clustering method to improve importance rating accuracy of customer requirements in QFD, International Journal of Advanced Manufacturing Technology, Vol. 107, No. 5–6, pp. 2579–2596.
Simpson M., Padmore J., Taylor N., and Fracknall-Hudges J. (2006), Marketing in Small and Medium Sized Enterprises, International Journal of Entrepreneurial Bahaviour & Research, Vol. 12, No. 6, pp. 361–387. DOI: 10.1108/13552550610710153
Siwiec D. and Pacana A. (2021a), A Pro-Environmental Method of Sample Size Determination to Predict the Quality Level of Products Considering Current Customers’ Expectations, Sustainability, Vol. 13, No. 10, p. 5542. DOI: 10.3390/su13105542
Siwiec D. and Pacana A. (2021b), Method of improve the level of product quality, Production Engineering Archives, Vol. 27, No. 1, pp. 1–7. DOI: 10.30657/pea.2021.27.1
Siwiec D., Bednarova L., and Pacana A. (2020), Metoda doboru penetrantów dla przemysłowych badań nieniszczących, Przemysł Chemiczny, Vol. 99, No. 5, pp. 771–773. DOI: 10.15199/62.2020.5.18
Siwiec D., Bednarova L., Pacana A., Zawada M., and Rusko M. (2019), Wspomaganie decyzji w procesie doboru penetrantów fluorescencyjnych do przemysłowych badań nieniszczących, Przemysł Chemiczny, Vol. 98, No. 10, pp. 1594–1596. DOI: 10.15199/62.2019.10.12
Tadeusiewicz R., Izworski A., and Majewski J. (1993), Biometria, Poland: Cracow.
Turisova R. (2015), A Generalization of Traditional Kano Model for Customer Requirements Analysis, Quality Innovation Prosperity-Kvalita Inovacia Prosperita, Vol. 19, No. 1, pp. 59–73. DOI: 10.12776/QIP.V19I1.407
Turisova R., Sinay J., Pacaiova H., Kotianova Z., Glatz J. (2020), Application of the EFQM Model to Assess the Readiness and Sustainability of the Implementation of I4.0 in Slovakian Companies, Sustainability Vol. 12, No. 14, p. 5591. DOI: 10.3390/su12145591
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. 12, No. 14, p. 2386. DOI: 10.3390/en14092386
Wang F., Li H. Liu A.J., and Zhang X. (2015), Hybrid customer requirements rating method for customeroriented product design using QFD, Journal Of Systems Engineering And Electronics, Vol. 26, No. 3, pp. 533–543. DOI: 10.1109/JSEE.2015.00061
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, Vol. 61, No. 1, pp. 129–137. DOI: 10.1109/TEM. 2013.2248729
Wang Y.M. and Chin K.S. (2011), A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment, Information Sciences, Vol. 181, No. 24, pp. 5523–5533. DOI: 10.1016/j.ins.2011.08.016
Winiarski J. (2012), Ryzyko w projektach informatycznych – statystyczne narzędzia oceny, Contemporart Economy, Electronic Scientific Journal, Vol. 3, No. 4, pp. 35–42.
Xie M.Q., Jiang Q.Q., Cheng W.P., and Ma X.X. (2016), Determining the Importance Ratings of Customer Requirements of Automotive Clutch Based on Quality Function Deployment, International Conference on Mechanics Design, Manufacturing and Automation, pp. 379–386.
