This study deals with the selection of the cutting process using the grey fuzzy relation approach. The analysis was performed using plasma arc machining, laser beam machining, and abrasive waterjet machining on three different workpiece thicknesses with different cutting speeds. The objective was to select the best cutting process considering several performance characteristics such as machining time, dimensional accuracy, kerf width, and surface roughness. Data normalization, grey relation coefficients, fuzzy inference system, and grey fuzzy relation grade are used to evaluate the machining performances of the machining processes. The developed fuzzy model can be used to study the effects of different cutting processes on technological features. The results show that the grey fuzzy technique can be effectively used for the analysis and selection of cutting processes.

This systematic review and meta-analysis, conducted in accordance with PRISMA 2020 guidelines, synthesises SEM-based evidence on the relationship between Total Quality Management (TQM) and organisational performance. The Web of Science, Scopus, and Semantic Scholar were searched for peer-reviewed studies, published between 2015 and 2026, that reported direct TQM-to-performance structural paths. Of 255 full-text records assessed, 148 were coded, and 92 contributed to the primary random-effects model. The pooled standardised path coefficient indicates a moderate positive effect. Between-study heterogeneity was substantial. Composite TQM operationalisation produced stronger effects than individual dimensions. Aggregate and sustainability performance responded more strongly than other performance types. Meta-regression revealed no statistically significant contextual moderators. Key limitations include geographic concentration in South and Southeast Asia and the MENA region, reliance on cross-sectional survey designs, and a single-coder approach. No risk-of-bias tool was applied, as no validated instrument exists for this study type in management research. TQM consistently improves organisational performance across contexts. Future research should prioritise longitudinal designs, broader sectoral coverage, and non-English literature.

Cost calculation is of huge importance both for determining a rational and competitive price of the product and preparation of offers according to customer's demand, where deadlines for sending offers are limited. In individual and small batch produc...

In individual and small-scale production, which is most often realized in the form of service activities of small and medium-sized enterprises, it is of particular importance to effectively prepare offers for the production of products at the request of the customer. Costing is a basic task that is solved in making the right offer. In addition to traditional methods, product costing is calculated using modern methods and models, based on the basics of artificial intelligence. This paper presents a product costing model based on the principles of group technology and the application of artificial neural networks.

The paper presents the application of modern artificial intelligence algorithms in the optimization of production process planning for machining prismatic parts. It is based on the concept of intelligent manufacturing systems and the variability of process plans, with optimization formulated as the problem of determining the sequence of machining operations and selecting technological resources while adhering to precedence constraints. Special attention is given to a biologically inspired metaheuristic algorithm - the modified crow search algorithm (GCSA), which is adapted to the discrete nature of the machining cost optimization problem. The optimization results are illustrated using an example of a prismatic part.