Universal Model to Predict Expected Direction of Products Quality Improvement

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.

A New Model Supporting Stability Quality of Materials and Industrial Products

“…It results from the need to identify the incompatibility of the product, and then its effective verification to determine the main cause of its occurrence [5,7,14,21]. Later, it is possible to identify adequate improvement actions [46][47][48][49]. In addition, for the lack of stable quality of product, the problem consists of a large number of potential causes [50].…”

Method of Fuzzy Analysis of Qualitative-Environmental Threat in Improving Products and Processes (Fuzzy QE-FMEA)

“…The assumptions were assumed based on the FMEA methodology, assumptions for decision making based on multiple criteria in a fuzzy decision environment, and also based on the literature review. The assumptions for the modified FMEA method, i.e., for fuzzy QE-FMEA method were following: products or processes to be analysed are arbitrary with the use of adequate priority number selection tables [ 1 , 3 , 37 ]; quality is expressed by the Z index (i.e., the importance of the defect for the customer) and refers to the effect of the defect on the use of the product/or the functioning of the process, the impact on customer satisfaction and possible repair costs) [ 7 , 29 , 38 ]; the impact on the natural environment (indicator E) is the negative impact of a defect (product or process) on the natural environment [ 4 , 12 , 38 ]; the threat priority value (RQE) is calculated in a fuzzy decision-making environment and is a quotient of the ratings assigned to the indicators, i.e.,: P—probability of a defect, W—possibility of detecting a defect, Z—effect (significance) of the defect (so-called quality), E—impact on the natural environment, where Z and E are combined and created qualitative environmental indicator (QE) and are evaluated simultaneously in the pairwise comparison matrix [ 2 , 30 , 39 , 40 ]; when determining the number Z (significance of the defect), only the effect of the defect should be considered; determining the number P (the probability of a defect) may refer to the defect but also to the cause of the defect, it is necessary to consistently comply with the adopted rule; determining the number W (the possibility of detecting a defect) refers to the cause of the defect; criteria for assessing indicators P (probability of a defect), W (possibility of detecting defects), and QE (qualitative-environmental indicator) result from the subject of the analysis and are selected individually by a team of experts, considering, for example, the frequency of incompatibilities, their effects, and causes; the QE indicator results from the relationships occurring in a fuzzy decision-making environment and the nine-point Saaty scale [ 3 , 12 , 33 ]. …”

“…products or processes to be analysed are arbitrary with the use of adequate priority number selection tables [ 1 , 3 , 37 ];…”

“…The products and processes of their occurrence require continuous improvement. It is a necessary action for all prosperous enterprises [ 1 , 2 ]. In order to stabilise actions made by enterprises, it is important to effectively analyse hazards in the area of projects, processes, and products [ 3 , 4 ].…”

Untitled

“…The results of the surveys showed that the relationship between social media, attachment to the place attachment, and pro-ecological behavior was analyzed, and the impact of social interactions on pro-ecological public behavior and its mechanisms was studied (Janmaimool, Khajohnmanee, 2019). In addition, an analysis of the impact of employee behavior in managerial positions in the context of pro-ecological behavior was carried out (Fatoki, 2019), and the relationship between knowledge of the environment and environmental attitudes, as well as knowledge about the environment and pro-ecological behavior (Ostasz et al, 2022). After the literature review, it was concluded that the topics of pro-environmental awareness and pro-environmental activities are being analyzed, as well as being an important area of research.…”