# Material Flow Optimization as a Tool for Improving Logistics Processes in the Company
## 1 Introduction
An important part of the analysis also includes a description of storage and material handling processes, incorporating inventory management methods such as FIFO (First In, First Out) and FEFO (First Expired, First Out), which ensure optimal material flow in accordance with its characteristics and requirements. The analysis of material flow within the company focuses on quantifying the costs associated with handling, including personnel costs, transportation expenses, material handling equipment costs, and warehouse rental fees. Based on the identified shortcomings, two solution variants are proposed to optimize the material flow. These proposals include replacing the external operator for semifinished goods (SF) with in-house transport and substituting external operators for the transport of finished goods (FG) with an internal logistics system. This paper presents a novel, systematic, and practically applicable approach to material flow optimization in enterprises, integrating traditional analytical tools with modern visualization and efficiency assessment techniques. The proposed methodology combines a checkerboard table as a visual tool for analyzing workplace relationships with formulas for calculating material flow costs. This approach enhances the understanding of interactions within the logistics system while allowing for the quantification of the impact of proposed changes. The inclusion of an economic analysis of the return on investment (ROI) directly within the methodology provides clear and measurable indicators of the success of the implemented measures. This integration enhances the practical value of the methodology for both managers and academics. The methodology is designed to be easily applicable across various types of enterprises, from manufacturing companies to warehouse systems. One of its key advantages is its adaptability, allowing individual steps to be tailored based on the specific needs of a company, distinguishing it from rigid theoretical approaches. While many methodologies rely solely on tabular and numerical data, this approach incorporates a checkerboard table for an intuitive representation of relationships among workstations. This tool facilitates the rapid identification of critical areas within the material flow. Another significant aspect of this solution is its ability to enhance operational flexibility, enabling companies to respond quickly to fluctuations in demand or customer specifications. As a result, the article not only presents concrete solutions for improving logistics processes but also offers a methodological framework applicable to other manufacturing enterprises facing similar challenges. Overall, it provides a valuable approach to improving material flow efficiency, emphasizing practical feasibility and economic benefits. This approach is applicable not only in logistics enterprises but also within integrated railway logistics. It directly supports the goals of sustainable transport and innovative transport technologies. Although the issue of material flow optimization has been the subject of numerous research studies, this paper contributes to the existing literature by focusing on practical constraints within the industrial environment and offering a methodology that is applicable in real-world conditions without requiring extensive investments in technological modernization. This study identifies and challenges common industrial constraints that often hinder effective material flow optimization. Traditional optimization models primarily emphasize mathematical and simulation-based approaches, which, while providing precise results, do not always reflect real operational conditions, such as capacity limits, infrastructure constraints, or a company's investment capabilities. In contrast, the proposed methodology relies on a combination of a checkerboard table and quantitative cost analysis, allowing for a flexible and rapidly applicable solution without the need for sophisticated simulation models.