Original scientific paper

https://doi.org/10.17559/TV-20211102151808

Appliance to Predict the Quality of Hypothetically Modified Products

Dominika Siwiec ; Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology Al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
Andrzej Pacana orcid.org/0000-0003-1121-6352 ; Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology Al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland

Abstract

Customizing the quality of the product to change customer expectations is a necessary action in good, prospering organizations. In enterprises, the most beneficial solutions consider the future satisfaction of customer with the product. This issue is not easy and is not resolved; therefore, integration of different techniques was proposed as part of a single, coherent appliance. Therefore, the aim is to propose the appliance to predict the quality of hypothetically modified products. The appliance was developed by adequately selected and combined techniques, i.e., survey research with the Likert scale, AHP method (Analytic Hierarchy Process), Pareto rule (20/80), WSM method (Weighted Sum Model) and Naive Classifier Bayes. The concept of the proposed appliance concerns the possibility of determining important product attributes and possible combinations of feature states. Based on this, the quality levels were estimated, and then satisfaction with the hypothetical modifications of the product was predicted. The test was carried out on the vacuum cleaner. As a result, four combinations of product modifications were determined, which have been created based on hypothetical and actual attributes. Each modification was satisfying for the customer. Therefore, the proposed apparatus turned out to be effective in predicting customer satisfaction for the modified quality levels. Originality is to propose a new integration of different techniques to predict levels of quality product modification based on current product quality.

Keywords

Analytic Hierarchy Process; decision support; customer requirements; Naive Bayes Classifier; predict; quality level; Weighted Sum Model

Hrčak ID:

279477

URI

https://hrcak.srce.hr/279477

Publication date:

17.6.2022.

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