Project-oriented manufacturing companies aim to produce high-quality products according to customer requirements and a minimum rate of complaints. In order to achieve this, performance indicators, especially those related to product quality, must be measured and monitored by managers. This research proposes a fuzzy multi-criteria model for the selection of key performance indicators that are critical to product quality. The uncertainties in the relative importance of decision-makers, performance indicators, and their values are described by sets of natural language words that are modeled by the interval-valued intuitionistic fuzzy numbers. The assessment of the relative importance of the decision-makers and the determination of their weights are based on the inclusion comparison probability between the closeness intuitionistic fuzzy sets. The determination of the weights vector of performance indicators is based on the integration of an interval-value fuzzy weighted geometric operator and the inclusion comparison probability between the closeness intuitionistic fuzzy sets. TOPSIS expanded with interval-valued intuitionistic fuzzy numbers for ranking performance indicators is proposed. The developed model was tested on the real data collected from three manufacturing companies in the Republic of Serbia. Based on the obtained results, the top-ranked performance indicators were marked as critical for product quality and selected as quality key performance indicators.

In recent decades, many researchers and practitioners have believed that reaching a high level of business excellence leads to the continuous realization of a set of business goals. In the literature, a vast number of models for business excellence evaluation that contain different criteria depending on the cultural, technological, organizational, and socio-economic factors can be found. The aims of the proposed fuzzy two-stage model are to address some of the main shortcomings of the EFQM2020 model and to adapt it to the needs of process manufacturing. The relative importance of quality criteria and their values are presented by pre-defined linguistic expressions modeled by the triangular fuzzy numbers. The determination of the weight vector of criteria is stated as a fuzzy group decision-making problem and determined by using the fuzzy best-worst method. The proposed fuzzy multi-objective optimization by ratio analysis is implemented for determining the rank of enterprises. The management initiatives that should lead to the improvement of business excellence should be based on the business practices of enterprises that are highly placed in the rank. Testing and verification of the proposed model are performed on real data originating from enterprises operating in the same economic sector.

As the only constant in business is change, business transformation is essential for adopting new perspectives and business trends. One of the keys to performing successful business transformation is to be fully aware of the current components of the business model. This research aims to allocate the business model components (BMCs) to defined business model components groups (BMCGs) by developing a new approach that integrates fuzzy sets and heuristic algorithms. The allocation results enable a comprehensive analysis of business model frameworks and give a good connection to research in the domain of strategic management and business process modeling. For allocation, the decision-makers (DMs) are employing the linguistic terms modeled by the fuzzy sets theory. The considered problem is stated as an integer programming model where the optimal solution is given by a B&B algorithm. The model is tested on a sample of forty experts from four different economic sectors.

This paper conducts an analysis of the application of FMEA in companies operating in the automotive industry within the territory of the Republic of Serbia, focusing on the current situation and future perspectives. The basic characteristics of implementing this analysis are presented, such as the number of employees involved in the implementation process and the prevalence of different types of FMEA in the considered companies. Additionally, the study presents and analyses the needs for methodological improvements and the adoption of modern tools in conducting FMEA. The aim of this research is to present the current situation and challenges in the application of FMEA in the automotive industry in the Republic of Serbia, as well as to identify possibilities for future improvements.

According to the international industry standards, the Process Failure Mode and Effect Analysis (PFMEA) is an obligatory tool for the risk assessment in the automotive industry. Due to FMEA transition in the automotive industry (2019th), PFMEA has been subjected to a series of transformations and improvements, where the substantial change is the risk estimation. However, although it is an essential tool for the reliability assessment, the formal PFMEA is still not sufficiently compatible with the ongoing Industry 4.0 trends. Consequently, practitioners have increasing needs for guides that could support them to implement PFMEA toward Industry 4.0 in their companies. This study aims to ease this shift by proposing a novel model for the assessment of PFMEA maturity level, which considers four PFMEA maturity levels; where the first level corresponds to the manual handling of reports; while the level-four corresponds to the fully automated PFMEA. The overall maturity level was calculated by accounting the eight significant influential factors. Since each of the influential factors has a different degree of influence, their importance can be calculated by applying some Multi-Criteria Decision-Making method or by direct assessment. Finally, the usage of the proposed maturity model was demonstrated by the case study, and obtained results proved that the proposed model is suitable and applicable in a real production environment in the automotive industry, with the emphasis that it could be extended for other industries.