Universal Model to Predict Expected Direction of Products Quality Improvement

Grzegorz Ostasz, Dominika Siwiec and Andrzej Pacana
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Grzegorz Ostasz: Department of Humanities and Social Sciences, Faculty of Management, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland
Dominika Siwiec: Department of Manufacturing Processes and Production Engineering, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland
Andrzej Pacana: Department of Manufacturing Processes and Production Engineering, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland

Energies, 2022, vol. 15, issue 5, 1-18

Abstract: Improving the quality of products remains a challenge. This is due to the turbulent environment and the dynamics of changing customer requirements. Hence, the key action is to predict beneficial changes in products, which will allow one to achieve customer satisfaction and reduce the waste of resources. Therefore, the purpose of this article was to develop a universal model to predict the expected direction of quality improvement. Initially, the purpose of the research was determined by using the SMART(-ER) method. Then, during the brainstorming method (BM), the product criteria and range states of these criteria were determined. Next, a survey with the Likert scale was used to obtain customers’ expectations, i.e., assessing the importance of criteria and customers’ satisfaction with ranges of product criteria states. Based on customer assessments, quality product levels were calculated using the Weighted Sum Model (WSM). Then, the initial customer satisfaction from the product quality level was identified according to the relative state’s scale. Based on this, the direction of product quality improvement was anticipated using the Naïve Bayesian Classifier (NBC). A test of the model was carried out for photovoltaic panels (PV) of a key EU producer. However, the proposed model is universal, because it can be used by any entity to predict the direction of improvement of any kind of product. The originality of this model allows the prediction of the destination of product improvement according to customers’ assessments for weights of criteria and satisfaction from ranges of quality-criterion states.

Keywords: predicting; decision support; machine learning; improvement of products; quality; customers’ expectations; naïve Bayesian classifier; weighted sum model; photovoltaic panels; mechanical engineering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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