The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry.

The objective of this work was to investigate the influence of the addition of a small amount of SiC nanoparticles on the mechanical characteristics and wear resistance of ZA-27 alloy. The ZA-27 alloy-based nanocomposites were produced by a relatively cheap compocasting process preceded by mechanical alloying. Reinforcing elements were the silicon carbide (SiC) nanoparticles with an average size lower than 50 nm and in very small amounts of 0.2, 0.3 and 0.5 wt. %. Wear tests were realized on a block-on-disc tribometer under lubricated sliding conditions, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. Optimisation of the SiC amount was performed by applying the Taguchi method, showing that the SiC amount of 0.5 wt. % is optimal for the given testing conditions. Prediction of the results and wear maps were also conducted. The analysis of variance showed that the SiC amount has the greatest influence on wear rate (70.8 %), followed by the normal load (19.8 %), and the sliding speed (3.9 %), while the influences of all interactions between these factors did not have any significant influence.

The problem of classification of risk factors in an uncertain environment is part of the risk management problem, which has a critical effect on the competitive advantage of production supply chain. The severities of consequences, their relative importance, and the frequency of occurrence of risk factors are defined by risk management team, depending on their experience and the results of good practice. Fuzzy rating of the severities of consequences and the frequency of occurrence of risk factors are described by linguistic expressions, which are modeled by triangular fuzzy numbers. The risk values, obtained by the materialization of the identified risk factors, are given precisely with the usage of fuzzy algebra rules. The classification criterion is defined as the distance between current risk value and extreme risk values. The proposed model enables determination of the priorities of risk factors. It is illustrated by an example with real-life data from a production supply chain in auto industry.

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.

Small and medium enterprises are by their very nature almost compelled to seek their place on the market through the implementation of innovative processes and innovations. Innovation is one of the processes that contribute to sustaining the competitiveness of the company, showing the operational flexibility and the ability to acquire new knowledge, that is, a favorable exchange of information with its environment in which it operates. This paper presents an overview of the innovation impact in SMEs in Bosnia and Herzegovina, observing the impact factors in which innovation influences growth and development, and thus the overall success of the company.