Universal Model to Predict Expected Direction of Products Quality Improvement

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 Naive 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.

Automated summary

The article proposes a universal model to predict the direction of product quality improvement. The research purpose was determined using the SMART(-ER) method, product criteria and range states were determined using the brainstorming method, and customer expectations were obtained using a Likert scale survey. Quality product levels were calculated using the Weighted Sum Model, and the direction of quality improvement was predicted using the Naive Bayesian Classifier.