Obtaining and processing customers’ expectations for products is a problem in view of the dynamic changes of these expectations. It refers to precisely determining the most important requirements of customers to improve the product in a standardized way. The purpose of the article was to develop a method to process customers' expectations to improve the quality of products. The motivation for developing this method was to reduce uncertainty in expressing customer expectations. Using the SMART(-ER) method, the goal of the analysis was determined. Then, during brainstorming (BM), the product and the analysis criteria were selected. Next, by using a survey with the Likert scale, the customer expectations were obtained. Later, these expectations were processed by the fuzzy analytic hierarchy process (FAHP). Based on processed weights, the choice of important criteria was compiled. The choice was made according to the Pareto-Lorenz Rule. The originality of the article is based on a method, which simultaneously combines quality management tools with the method in
a fuzzy decision environment. This method can be used to verify customers' expectations for any product.
Alexandrov, A. (2010). Characteristics of Single-Item Measures in Likert Scale Format. “The Electronic Journal of Business Research Methods”, 8(1).
Hansen, E., Bush, R.J. (1999). Understanding customer quality requirements – Model and application. Ind. Mark. Manag., 28.
Hoła, A., Sawicki, M., Szóstak, M. (2018). Methodology of Classifying the Causes of Occupational Accidents Involving Construction Scaffolding Using Pareto-Lorenz Analysis. “Appl. Sci.”, 8, 48. DOI: 10.3390/app8010048.
Huang, Y.M. (1999). On the general evaluation of customer requirements during conceptual design. “J. Mech. Des.”, 121.
Laarhoven, P.J.M., Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. “Fuzzy Sets and Systems”, 11(1–3). DOI: 10.1016/S0165-0114(83)80082-7.
Lawlor, K.B., 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.
Lima-Junior, F., Carpinetti, L. (2019). Dealing with the problem of null weights and scores in Fuzzy Analytic Hierarchy Process. Soft Computing. DOI: 10.1007/s00500-019-04464-8(0123456789.
Oguztimur, S. (2011). Why Fuzzy Analytic Hierarchy Process Approach for Transport Problems? CORE – Research Papiers in Economics.
Ostasz, G., Siwiec, D., Pacana, A. (2022). Universal Model to Predict Expected Direction of Products Quality Improvement. “Energies”, 15. DOI: 10.3390/en15051751.
Ozdemir, U., Altinpinar, I., Demirel, F.B. (2018). A MCDM Approach with Fuzzy AHP Method for Occupational Accidents on Board. “The International Journal on Marine Navigation and Safety of Sea Transportation”, 12(1). DOI: 10.12716/1001.12.01.10.
Pacana, A., Siwiec, D. (2021). Universal Model to Support the Quality Improvement of Industrial Products. “Materials”, 14. DOI: 10.3390/ma14247872.
Putra, M., Andryana, S., Gunaryati, A. (2018). Fuzzy Analytical Hierarchy Process Method to Determine the Quality of Gemstones. “Advances in Fuzzy Systems”, Vol. 2018, Article ID 9094380, 6 pages. DOI: 10.1155/2018/9094380
Roder, B., Heidl, M.J., Birkhofer, H. (2013). Pre-Acquisition Clustering of Requirements – Helping Customers to Realize What They Want. “Des. Harmon.”, 7.
Saad, S.K., Mohamed, A. (2015). A fuzzy-AHP multi-criteria decision-making model for procurement process. “International Journal of Logistics Systems and Management (IJLSM)”, 23(1).
Shukla, R., Garg, D., Agarwal, A. (2014). An integrated approach of Fuzzy AHP and Fuzzy TOPSIS in modelling supply chain coordination. “Production & Manufacturing Research: An Open Access Journal”, 2(1). DOI: 10.1080/21693277.2014.919886.
Siwiec, D., Pacana, A. (2021). A Pro-Environmental Method of Sample Size Determination to Predict the Quality Level of Products Considering Current Customers’ Expectations. “Sustainability”, 13. DOI: 10.3390/su13105542.
Siwiec, D., Pacana, A. (2021). Model Supporting Development Decisions by Considering Qualitative–Environmental Aspects. “Sustainability”, 13. DOI: 10.3390/su13169067.
Torfi, F., Farahani, R., Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. “Applied Soft Computing”, 10(2). DOI: 10.1016/j.asoc.2009.08.021.
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. DOI: 10.3390/en14092386.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2022 Modern Management Review