This paper addresses the design of production expansion by using simulation to optimize production and storage capacities. The primary objective is to apply the Siemens Tecnomatix Plant Simulation software version 2404 to analyze the current production process, identify potential bottlenecks, and propose improvements. The methodology involves the use of simulation to create digital models of the production process, allowing for the detection of issues and evaluation of alternative solutions without interfering with ongoing production. The novelty of this approach lies in its ability to test and optimize the production flow digitally, ensuring a smoother transition to expanded capacities. Several alternative scenarios for production expansion were developed using Siemens Tecnomatix Plant Simulation, with a focus on verifying production volumes, identifying bottlenecks, and proposing the necessary machines and technological equipment. The results of the simulations are used to assess the proposed improvements, while the layout of the process is optimized for efficient material flow. The paper also presents 3D models as part of the solution proposal. The findings demonstrate the effectiveness of simulation-based design for expanding production systems, offering a methodology that can be applied to similar manufacturing processes.

The presented article deals with the application of tools to calculate the Master Production Schedule (MPS) in the production of beverages (consumer packed goods) and its impact on the inventory profile in finite or infinite capacity. The calculation of MPS can be performed at the level of an ERP solution or with dedicated standalone planning software. The first part of the manuscript defines MPS calculations, its position in the planning hierarchy and defines reason of importance having MPS calculations in the consumer product industry. The second part of the article analyses sample data of beverage production, calculates MPS for each item and compares the results of finite/infinite capacity utilization. In the last part of the manuscript, authors discuss if the challenges described in introduction part of the article can be addressed with specific planning solution to calculate MPS.

This study explores the integration of educational robotics into the development of digital competencies essential for Industry 4.0 and 5.0. These industrial paradigms are defined by automation, interconnected cyber-physical systems, value chain integration, and digitalisation. In this environment, digital skills become strategically vital. Didactic robotic platforms, such as the Wlkata Mirobot, offer students hands-on opportunities to develop these abilities in a practical and interdisciplinary context. When combined with technologies like digital twins, the Internet of Things, and simulation tools, educational robotics fosters both technical proficiency and adaptability to evolving industrial demands. The presented case study demonstrates the design, construction, and experimental setup of a functional laboratory mini-line using the Wlkata Mirobot. The focus is placed on layout design, robot programming, and simulation-based process optimization to reflect real industrial processes. This study also presents student feedback and performance indicators from repeated trials to illustrate the educational and operational potential of the solution.

The article deals with the application of cluster analysis in modeling in-house processes, specifically supply processes. An algorithm is designed on the theoretical basis of cluster analysis and according to the analysis of the supply processes in selected industrial companies. Specifically, the algorithm is based on the hierarchical methods of cluster analysis, and the selected hierarchical clustering method is applicable in modeling storage systems under various production conditions in industrial companies. The methodology of clustering regarding the supply processes is subsequently experimentally verified. Based on the results of the cluster analysis, a system of organization was proposed for the analyzed warehouse in the form of 2D and 3D layout models of the warehouse.

This study presents a comprehensive simulation-driven approach to identify and mitigate bottlenecks in the supply flows of an automotive manufacturing system. The supply process relies on a Kanban-based assembly line replenishment, supported by multiple material transports per shift using tugger trains. To analyze flow constraints, the research combines time–motion studies with data from a Real-Time Locating System (RTLS), enabling spatial-temporal mapping of material movements. Based on this data, digital-twin simulation models of the supply process were developed and validated. These models allowed testing of improvements aimed at increasing throughput and efficiency. The proposed methodology demonstrates effective bottleneck resolution and provides a structured framework for data collection and simulation integration. Unlike traditional observation-based approaches, the use of RTLS enables precise detection of routing deviations and operator behaviors. The simulation model was tested under real operating conditions and validated against ground-truth data. Results showed a reduction in collisions and delivery delays, as well as measurable productivity and financial gains. This approach offers a practical and transferable method for optimizing intralogistics in modern production environments.