The article discusses the application of computer simulation in the optimization of production processes, particularly in the context of analyzing scenarios related to the addition of new production lines. The conducted research and simulations have shown that computer simulation is a key tool for precise modeling and analysis of various options, allowing for better understanding and optimization of production activities. The article presents the theoretical foundations of simulation along with practical examples of its application, focusing on assessing the impact of different production line configurations on the overall system’s efficiency. The analysis of benefits includes shortening the production cycle time, increasing flexibility, and improving operational efficiency. The challenges associated with implementing computer simulation, such as the need for specialized knowledge and the necessity for continuous updates of simulation models, are also discussed. Based on the research and analyses conducted, the article demonstrates that computer simulation is an effective tool supporting strategic and operational decision-making in production management, particularly in the context of expanding production infrastructure.

Manufacturing and logistics companies increasingly rely on warehouse automation and data-driven planning; however practical tools for evaluating how structural decisions influence outbound performance remain limited. This paper addresses this gap by developing a discrete-event simulation model of a warehouse system in which forklifts execute pallet transport tasks according to predefined shipment schedules. The model integrates real inventory data, structured storage addressing (alley-bay-level-slot), forklift routing logic and dock-capacity constraints within a unified ProcessFlow architecture. The experimental study focuses on assessing the impact of dock capacity on outbound operational performance. Three scenarios with varying numbers of docks were analysed while maintaining constant order structure, inventory distribution and transport logic. The evaluation is based on two key performance indicators: forklift utilization and average dock occupancy level. Statistical analysis was conducted using multiple replications and 95% confidence intervals to verify the significance of observed differences. The findings confirm that discrete-event simulation provides a robust decision-support tool for analysing structural modifications in warehouse systems and supports data-driven configuration of outbound logistics processes within Industry 4.0 environments.

Optimization of production logistics processes through the use of simulation tools brings a lot of benefits to a production company, and thus significantly reduces production costs. Increasing competition resulting from the use of production automation to increase productivity has increased the complexity of production systems that can only be analysed by simulation. Production logistics performs management functions such as planning, motivating, organising and controlling. It does not deal with technological processes, but the organisation of physical delivery and displacement of components and information in the system. An important feature of production logistics is combining supply logistics with distribution logistics. If the production logistics is properly organised, it provides access to all materials and components of a given product during the implementation of a specific order. Simulation tools ensure continuity and rhythm of production after it is started, as well as convenient conditions for using modern internal transport. The article presents the Flexsim simulation tool, which was used to improve production logistics in the studied enterprise. The programme also contributed as an opportunity to improve and shorten production cycles, maintain optimal inventories, select packaging and logistics units, as well as the ability to use automatic identification of raw materials, materials or semi-finished products.

The article explores the analysis of material flow and resource utilization in production systems using rotary tables. Through the use of simulation models in FlexSim software, the study evaluates different configurations of production lines and their impact on system performance. Three scenarios are examined, involving variations in the number of production lines and rotary tables, to identify the balance between system stability and flexibility. The findings emphasize the role of rotary tables in managing material flow effectively and ensuring smooth operations. The study also discusses the advantages of simulation in predicting operational outcomes and supporting decision-making processes, such as optimizing material flow paths and managing production resources. Challenges, including variability in system performance and the complexity of balancing multiple lines, are also addressed. The results demonstrate the value of simulation tools like FlexSim in understanding and improving production systems, highlighting their importance in strategic planning and enhancing the adaptability of modern manufacturing processes.

Purpose: The main goal of managing a simulation project in a manufacturing company regarding the optimization of warehouse preparation and placement of galvanized steel profiles using FlexSim software is to improve logistics processes in the warehouse and increase production efficiency. The aim of the research is to develop an effective package addressing scheme and to introduce a special code that will enable precise management of profile storage and will improve the process of completing customer orders. Design/methodology/approach: The simulation project management methodology is based on several key steps: defining goals and requirements, collecting data and modeling, designing scenarios, performing simulations, and designing an individual addressing scheme for parcels in the warehouse. Findings: Simulations allow you to find the optimal warehouse layout that ensures effective use of the available space. This, in turn, allows you to increase the storage capacity and better organize the storage of profiles, which translates into minimizing losses and costs. Simulations allow you to test different strategies for placing profiles in a warehouse to minimize the time and effort needed to find them. Optimizing the placement process allows parcels to be located faster and more precisely, which speeds up order picking. Research limitations/implications: Simulation-based process optimization requires model accuracy and reliability. Imprecise or inaccurate data can affect simulation results and lead to inappropriate decisions. This requires careful data collection and model validation before implementation. Further research may focus on optimizing routes inside the warehouse and optimizing internal logistics. Investigating the best routes and methods of transport will minimize the time your products pass through your warehouse. Research can introduce an element of random events, such as machine breakdowns or fluctuating demand, into the simulation model to better reflect real conditions and increase the reliability of the results. Practical implications: Improvement of warehouse processes and the introduction of a special code addressing the shipment can significantly reduce the risk of errors and material losses, which contributes to greater accuracy and efficiency of operation and improvement of customer relations. Social implications: Effective optimization of warehouse and production processes contributes to increased work efficiency. Reduction of redundant activities, more precise management of resources, and more efficient order picking can affect employee satisfaction as well as positively affect working conditions and security. Originality/value: The key element of originality is using advanced FlexSim software for modeling and simulation of storage processes and producing galvanized steel profiles. Simulations of this type integrated with real data and the implementation of a special package addressing code constitute a comprehensive approach to optimizing the entire process. The results of such a study can have a significant impact on the practice in manufacturing companies, enabling more effective warehouse management, shortening the time of picking orders, reducing costs, optimal use of resources, and improving the quality of customer service. It is an interdisciplinary approach that combines aspects of production management, logistics, process optimization, and the use of advanced IT tools. Keywords: Simulation project management, process simulation, FlexSim software. Category of the paper: research paper.

Purpose: Paper aims to review available knowledge base about MOQ models and review their application to 7 wastes. Design/methodology/approach: Research areas were identified through author-assigned keywords linked to individual scientific publications. The multi-stage research process incorporated two bibliometric methods: a systematic literature review and a traditional literature review. Scope of the paper gathers published optimization models of MOQ. Available database was reviewed with the purpose of Collected models are reviewed under the objective of 7 wastes. Findings: The research identified five models focused on optimizing Minimum Order Quantity (MOQ). Each model addresses inventory-related waste, recognizing inventory costs as a significant factor in overall production expenses. Notably, one model also incorporates transportation and defect costs, providing a broader approach to waste reduction. The data shows a clear trend: as manufacturing costs rise, driven by factors such as inflation and supply chain disruptions, there is an increasing demand for advanced optimization models to mitigate these pressures. These models aim not only to reduce inventory costs but also to enhance production efficiency and quality, supporting manufacturers' ability to remain competitive in a challenging market environment. Originality/value: Compared to other bibliometric analyses, this study distinguishes itself through the precise syntax of its constructed query. Furthermore, the paper investigates the application of models concerning waste categories recognized in lean management techniques. By identifying forms of waste that have been overlooked in prior research, this study offers valuable insights for researchers and practitioners in determining future research directions.