Current trends in manufacturing, which are based on customisation and gradually customised production, are becoming the main initiator for the development of new manufacturing approaches. New manufacturing approaches are counted as the application of new behavioural management patterns that calculate the retained competencies of decision-making by the individual members of the system agent; the production becomes decentralised. The interaction of the members of such a system creates emergent behaviour, where the result cannot be accurately determined by ordinary methods and simulation must be applied. Modelling and simulation will, therefore, be an integral part of the planning and control of the processes of factories of the future. The purpose of the article is to describe the use of modelling and simulation processes in factories of the future. The first part of the article describes new manufacturing concepts that will be used in factories of the future, with a description of modelling and simulation routing in the frame of Industry 4.0. The next section describes how simulation is used for the control of manufacturing processes in factories of the future. The included subsection describes the implementation of this suggested pattern in the laboratory of ZIMS (Zilina Intelligent Manufacturing System), with an example of a metamodeling application and the results obtained.

In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the duration of shipment processing times and numbers of workers. The commercially available software Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing time durations, and thus selecting the most suitable solution for defined inputs. This method has produced better results in comparison to the classical heuristic methods and, furthermore, is not as time consuming. This article, at its core, describes the algorithm used to determine the optimal number of workers in sorting warehouses with the results of its application. The final part of this article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of workers needed to speed up the departure of shipments and optimise the workload of workers.

Continuous cost reduction is a subject of interest for almost every production company. The cost reflects the competitiveness and sustainability of the business. Many company costs are linked to the effectiveness of production. One such cost is the work-in-progress (WIP) inventory cost. The present article deals with the design of a system for calculating the optimal WIP inventory stored in a manufacturing buffer, which, in the long term, provides the lowest costs. The main goal of the article is to design a new system that allows for the calculation of the optimal capacity of interoperation manufacturing buffers and thus the calculation of the optimal WIP inventory, which influences the lead time and cost. The newly designed system consists of algorithms that describe various steps, many of which use mathematical models. The individual blocks of algorithms are described, and the proposed system is verified and validated by simulation of the production line in the automotive production company.

Nowadays, many factories face changes on the global market and manufacturing is unpredictable. This fact creates a demand for developing new concepts of the factory which can represent a solution to these changes. This study presents a way for designing these new factory concepts, particularly a concept of the reconfigurable manufacturing lines. The methodology in this study uses characteristics of reconfigurable manufacturing systems for developing an algorithm for designing the basic factory layout. The methodology also combines classical math operations for designing the production layout with such approaches as simulation, cluster analysis, and LCS algorithm. This combination method with LCS algorithm and an entirely different approach to the design of the manufacturing line, has not yet been used. The accuracy of this methodology is then verified through the results of the complete algorithm containing these features. The main purpose of this study was to find new approaches to designing the reconfigurable factory layout. This article is presenting new ways that differ from the classical design method. The article suggests the new way is possible and the new systems also need new ways for designing and planning.

This article delves into sensitivity analysis within simulation models of real systems, focusing on the impact of variability in independent input factors (x) on dependent system outputs (y). It discusses linear and nonlinear regression to analyse and represent relationships between input factors and system responses. This study encompasses three sensitivity analysis areas: factor screening, local sensitivity analysis, and global sensitivity analysis, highlighting their roles in understanding the significance of factors in simulation models. The practical application of sensitivity analysis becomes clear through a case study in a manufacturing system. The case study utilises the Simio simulation system to investigate the impact of input factors on production lead time and work in process (WIP). The analysis uses regression to quantify the impact of seven factors, showcasing the most significant ones with tornado charts and emphasising the application of sensitivity analysis to optimise system responses.

Flexible custom manufacturing is becoming increasingly important, and, in the near future, it will serve as a key method to counter growing competition and meet market demands across most industrial sectors. This situation necessitates the substantial reorganization of companies’ material and information flows, as traditional planning approaches focused on serial production and longer time horizons are gradually losing their effectiveness. An integrated digital twin system that unifies production and logistics planning is emerging as a promising solution. The proposed approach entails implementing a digital twin directly within custom manufacturing, enabling the continuous monitoring and real-time adjustment of production plans based on instant data from sensors and information systems. The system architecture is designed around multiple modules responsible for data collection and processing, scheduling, simulation, statistical analysis, and effective communication between the system and its users. By leveraging these components, the solution can flexibly adapt to any deviations or changes as they occur. Within the scope of this research, attention is devoted not only to the handling of dynamic and random data but also to the prioritization of individual orders. Equally emphasized is the role of intelligent communication tools, which promptly inform us about shifts in the production process and allow for rapid plan modifications to ensure the highest possible levels of efficiency and reliability.