Material Flow Optimization as a Tool for Improving Logistics Processes in the Company

Abstract

Advancements in transport engineering and technology play a crucial role in improving multimodal transport systems and optimizing logistics operations. This study focuses on efficient material flow management in an industrial enterprise, directly supporting the goals of sustainable transport and innovative logistics strategies. The manufacturing plant in Veselí nad Lužnicí was selected as a case study because of the identified inefficiencies in its logistics processes and the availability of detailed operational data, allowing for an accurate analysis of material flows. The research identifies weaknesses in the current material flow and proposes the following two optimization solutions: replacing an external operator for semi-finished goods transport with in-house logistics and substituting external transport providers for finished goods transportation with an internally managed fleet. The proposed methodology introduces a novel integration of analytical tools, including checkerboard table analysis, cost modeling, and return-on-investment (ROI) assessment, to evaluate logistics efficiency and minimize material handling costs. This study demonstrates how optimized material flows, particularly using railway logistics, can contribute to cost-effective and sustainable supply chains. The research reflects current trends in transport system planning, emphasizing transport modeling, digital twin simulations, and smart railway technologies to enhance operational efficiency and resilience. The results provide practical recommendations for companies seeking to integrate rail transport into their logistics processes, contributing to broader objectives of environmental sustainability and digital transformation in the transport sector.

1. Introduction

Optimization of material flow is one of the key areas that directly impact the efficiency of production processes and the overall competitiveness of enterprises. In the context of current challenges in railway and transport technology, this issue becomes even more crucial, as effective material flow management is closely related to logistics, transportation, and cost optimization in transport systems [1,2]. Material flow represents the organized movement of materials within the production process or product circulation, characterized by the direction, intensity, frequency, length, performance, structure, and nature of the transported material, as well as the applied transport and handling technology [3,4]. Analysis of material flow involves examining the efficiency of material movement within the various stages of the production process, including the representation of essential production process requirements and their integration into the external logistics chain [5]. Optimizing material flows in production and logistics enhances competitiveness, as the optimal arrangement of elements and relationships significantly influences costs, flexibility, and overall productivity [6].

An inseparable component of material flow is the information flow, which plays a key role in ensuring the efficiency and continuity of the entire logistics chain [7]. Information flow provides essential data for monitoring, planning, and coordinating material movements, thereby supporting decision making and process optimization [8,9]. Within the broader framework of logistics and supply chain management, material flow optimization aligns with lean logistics principles, reducing waste and improving efficiency through just-in-time (JIT) inventory strategies and streamlined production planning [10]. Furthermore, supply chain resilience depends on the adaptability of material flows to external disruptions, highlighting the importance of predictive analytics, real-time tracking, and automation in modern logistics operations [11].

Efficient material flow management is also integral to supply chain agility, enabling businesses to respond more quickly to changes in demand, supply chain disruptions, and market fluctuations [12,13]. Incorporating Industry 4.0 technologies, such as digital twins, IoT-based tracking, and AI-driven demand forecasting, further enhances the ability of enterprises to optimize material flows dynamically [14]. In transport logistics, integrating optimized material flows with multimodal transport strategies, including railway transport—can significantly reduce costs, lower carbon emissions [15,16,17], and improve overall supply chain sustainability [18,19,20,21].

In today’s competitive environment, it is essential for businesses to continuously improve their processes. The terms analysis, planning, and optimization are closely interconnected, as following this sequence ensures a continuous improvement cycle, providing companies with competitive advantages and a higher probability of maintaining profitability [22,23]. Therefore, businesses must place increased emphasis on analyzing and optimizing material flows, enabling them to achieve greater efficiency, reduce costs, and strengthen their market position.

This paper focuses on identifying deficiencies and proposing solutions in material flow management at a manufacturing company in Veselí nad Lužnicí, which produced animal feed. The primary objective is to conduct a thorough analysis of the current operational processes to uncover key issues that negatively impact efficiency, cost-effectiveness, and overall performance of the company’s logistics system. Based on the findings, measures are proposed to eliminate identified shortcomings. The layout of the company is presented detailing the spatial arrangement of the workplaces and various sections of the production facility. 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.

The novelty of this study also lies in the development of a systematic process for evaluating different material flow variants, integrating visual and economic analytical tools into a comprehensive methodological framework. While existing studies often focus on isolated aspects of material flow, such as warehousing or internal logistics, this paper provides an integrated perspective on the entire logistics process—from inventory management to external transportation—while simultaneously considering the environmental and economic impacts of the proposed solutions. Another significant contribution is the methodology’s ability to assess not only material flow optimization under existing conditions but also its broader effects on the logistics chain, including sustainability and cost efficiency considerations. The proposed methodology quantifies the direct financial impact of changes in logistics operations while also offering a framework for analyzing the ecological benefits resulting from reduced transport distances and emissions. This study differs from previous research in that it does not focus solely on theoretical modeling of material flow optimization but instead provides a practical tool that is adaptable across various industrial sectors while accounting for operational, economic, and environmental factors. By combining a pragmatic approach with analytical precision, this study offers an original contribution to the field of material flow optimization and provides practical recommendations for companies facing similar logistics and transportation challenges.

2. Literature Review

Material flow optimization is a fundamental aspect of logistics and supply chain management, focusing on improving efficiency, reducing costs, and enhancing resource utilization in production and distribution systems. While the terms supply chain optimization, warehouse efficiency, and facility layout optimization are frequently mentioned in the literature, it is essential to clarify their relationship to material flow optimization. Supply chain optimization encompasses a broader strategic approach, integrating procurement, production, and distribution processes to ensure seamless operations, whereas material flow optimization specifically addresses the movement and transformation of materials within a defined system, such as a factory or logistics center. Warehouse efficiency and facility layout optimization represent subcategories of material flow optimization, as they influence the internal movement of materials, storage strategies, and handling operations. This review focuses on methodologies and technological advancements that contribute directly to material flow optimization while acknowledging the interconnections with broader logistics and supply chain management concepts. The authors of [24] offer a comprehensive overview of supply chain management, discussing optimization techniques and material flow management models. Another study [25] emphasizes the importance of effective planning and coordination in logistics strategies to enhance material flows. Reference [26] highlights the role of modern modeling tools, such as simulations and linear programming, in designing efficient logistics systems.

Lean manufacturing principles provide effective tools for optimizing material flow. The authors of [27] explore 5S (a methodology for workplace organization), SMED (single-minute exchange of die), and continuous flow, demonstrating their contributions to economic sustainability and the reduction in setup times in production systems. Reference [28], an online blog, outlines five steps for optimizing material flow, providing practical tips and examples from the automotive industry. The authors of [29] illustrate the use of lean tools in the horticultural sector to reduce cycle times, eliminate waste, and optimize workplace layouts.

Facility layout and warehouse management play significant roles in material flow optimization. The authors of [30] present improvements to warehouse layouts at Magneti Marelli, enhancing efficiency through optimized distances between zones. In [31], the author introduces concepts of integrated handling systems to address inefficiency. The authors of [32] evaluate new layout configurations in a digital factory using 3D modeling, while the authors of [33] develop a genetic approach for optimizing single-loop material flow paths.

Mathematical modeling and algorithmic approaches have been widely applied to material flow problems. In the paper [34], authors propose a mixed-integer programming model to optimize material flows and routes, offering managerial recommendations based on sensitivity analysis. In [35] applies the artificial bee colony algorithm to minimize operator travel distances in a manufacturing company, drawing parallels with the Vehicle Routing Problem. The authors of [36] introduced a decision support tool for analyzing by-product synergy networks using mathematical programming, demonstrating significant cost and environmental benefits.

Advancements in Industry 4.0 technologies have transformed material flow optimization. Reference [37] presents the concept of digital twins, which enable real-time simulations through IoT (Internet of Things), big data, and cloud technologies. The authors of [38] integrate material flow simulations with automated guided vehicles (AGVs) and digital twins of manufacturing cells. The authors of [39] propose the implementation of kanban logic in material flow management, supported by simulation modeling. The study in [40] analyzes the use of sensor networks in logistics and demonstrates how the Internet of Things (IoT) contributes to the real-time monitoring of material flows, leading to a reduction in operational costs. The authors of [41] present the application of blockchain in combination with IoT for transparent and efficient real-time monitoring of logistics operations. The study in [42] focuses on the application of machine learning for optimizing logistics route planning and predicting transportation obstacles in supply chains.

The study in [43] explores the use of digital twin simulations to test different supply chain scenarios before their implementation in real-world operations. The authors of the study in [44] provide an overview of digital twin implementation across various manufacturing and logistics sectors. The authors of [45] analyze how Industry 4.0 digital technologies improve supply chain resilience in response to market disruptions or global crises.

Material flow cost accounting (MFCA) provides a framework for evaluating material losses and improving resource efficiency. The authors of the study in [46] introduced the MFCA method, which assesses inefficiencies in monetary terms, while the authors of the study in [47] applied MFCA to a ceramic tile manufacturing company, highlighting its impact on process optimization and data utilization for sustainable production. Numerous case studies demonstrate practical applications of material flow optimization. In [48], the authors analyze material flow in an automotive company, proposing internal transport programs and workspace reorganization. The authors of [49] develop an evolutionary algorithm to synchronize supply chains, achieving optimal solutions for various logistical scenarios.

These studies highlight the transformative impact of modern digital technologies on material flow optimization, not only in terms of efficiency and cost reduction but also in sustainability. Their application in logistics and supply chain management enables better planning, more accurate predictions, and increased resilience to unforeseen market fluctuations.

3. Methodology

Optimization of material flow is a key element of logistics and production management, focusing on improving process efficiency, reducing costs, and increasing competitiveness. The methodology for optimizing material flow in enterprises is designed for systematic analysis, evaluation, and optimization, utilizing visual tools such as the checkerboard table, quantitative cost analysis, and economic indicators like return on investment (ROI).

The methodology of material flow optimization consists of several interconnected steps that ensure a systematic approach to improving logistics efficiency. The main steps, highlighted in bold, represent the core process of optimization, while the supporting steps, shown in regular font, provide additional analytical and decision-making support.

The process begins with data collection and analysis, where material flow costs are calculated to identify inefficiencies in the system. Based on this assessment, deficiencies are identified, forming the foundation for developing optimization measures. Parallel to this, potential optimization measures (variants) are proposed, evaluated, and selected based on feasibility and expected benefits.

Once a suitable variant is chosen, implementation (realization) of the selected optimization approach takes place. The results are then analyzed to determine whether the applied measures align with expectations. If the outcomes meet predefined objectives, the process concludes with layout realization, ensuring the long-term application of the optimized material flow strategy.

In cases in which the results do not meet expectations, the methodology incorporates a feedback loop, allowing for adjustments in the proposed measures or refinements in the data analysis process. This iterative approach ensures continuous improvement and adaptability to changing operational conditions. The updated methodology, enriched with additional supporting steps, provides a more structured and flexible framework for optimizing material flows while maintaining cost efficiency and operational effectiveness.

By applying this methodology, businesses gain a comprehensive overview of their material flows, identify critical inefficiencies, and implement measures that reduce costs and enhance efficiency. The ROI calculation provides a clear view of the economic benefits of optimization efforts. The proposed approach is both replicable and flexible, allowing its application across various industrial sectors. The results of this methodology can serve as a foundation for further research and practical implementations. Figure 1 illustrates the methodology for optimizing material flow.

The methodology consists of the following steps:

Step 1: Data Collection and Analysis

Identification of Workstations and Flows—Each workstation within the company is defined as a node in the logistics system [50]. Data collection includes the following:

• Distances between workstations (d_ij);
• Frequency of transfers (f_ij);ddd
• Transfer costs (c_transfer).

Visualization of Relationships—The Checkerboard Table Method

The checkerboard table is a widely used tool in logistics for analyzing relationships between different entities. It accurately depicts the movement and quantity of materials transferred within a given period between various workstations, facilities, and even external entities. The design of a checkerboard table is flexible and depends solely on its intended purpose.

A fundamental principle of this method is to list the names of individual entities in the first column and first row of the table. This ensures that the intersection of identical entity names appears along the table’s diagonal. The rows and columns represent different workstations, while the table elements express the intensity of relationships between them, such as material flow volumes, transfer frequencies, or logistical costs [51].

The checkerboard table provides extensive insights into interactions between entities. While simple tables usually cover only two parameters—material flow per time unit—it is possible to extend them with additional data, such as transport frequency, loading times, and packaging sizes. This allows for a more precise analysis of material flows. The key advantage of this method lies in its ability to provide a detailed and measurable description of relationships within a company’s logistics infrastructure. Its flexibility makes it highly adaptable to various needs, ensuring universal applicability. By implementing the checkerboard table, analysts gain a simple yet effective tool for collecting data, which can later be utilized for more complex evaluations. Ultimately, the goal of this method is to standardize the description of relationships between workstations, thereby improving material flow analysis and optimization [51].

The checkerboard table was applied as a visual and analytical tool to evaluate material flow in comparison with the selected optimization variants. This approach provided a clear representation of material movements under existing conditions and after the application of optimization, considering the following factors:

• The number of material handling operations before final processing;
• The frequency of transportation and the average distance between logistics points;
• The time required for individual operations throughout the entire process;
• The impact on overall production flow and the elimination of inefficient movements.

The checkerboard table methodology involves dividing the material flow into discrete elements, which are then evaluated based on predefined efficiency parameters. The analysis results enabled the identification of critical points in the logistics chain and the modeling of their optimal solutions within the new internal transport system. The cost model formulas were developed based on actual recorded operational costs within the company, considering the key components.

The costs of external transport, C_ext, are calculated according to Formula (1), as follows:

$$C_{ext}=P_{transp}\times N_{transp}+C_{handl} [€]$$

(1)

where

P_transp—average cost per unit of material transport [EUR/unit];

N_transp—number of transport operations in each period [operations/time];

C_handl—handling costs associated with external transport [EUR].

The cost of internal transport, C_int, is calculated according to Formula (2):

$$C_{int}=P_{fuel}\times S+P_{maint}+P_{staff} [€]$$

(2)

where

P_fuel—average fuel costs per unit of distance [EUR/km];

S—total distance traveled by internal transport vehicles [km];

P_maint—maintenance and repair costs of the internal transport system [EUR];

P_staff—personnel costs for operating internal transport vehicles [EUR].

Step 2: Calculation of Material Flow Costs

The costs associated with material flow between workstations are calculated using Formula (3). This formula enables the precise calculation of the financial impact of material movements within the company’s logistics network. By applying this approach, businesses can identify costly inefficiencies and optimize the allocation of resources in material handling and transport.

$$C_{flow}={\sum }_{i=1}^n{\sum }_{j=1}^m\left(d_{ij}\ast f_{ij}\ast c_{transfer}\right)$$

(3)

where

C_flow—total material flow costs;

d_ij—distance between workstations i and j (meters);

f_ij—frequency of transfers between workstations i and j;

c_transfer—unit transfer cost (EUR/m).

Step 3: Identification of Deficiencies

Workstations with the highest cost values in the checkerboard table are identified as critical nodes within the material flow. The deficiencies typically include the following:

• Inefficient layout—poor spatial arrangement of workstations leading to excessive transport distances;
• Excessive transfers—unnecessary or frequent movements of materials increasing overall costs;
• High handling costs—elevated expenses due to inefficient use of handling equipment or labor-intensive processes.

By identifying these critical points, companies can prioritize optimization efforts and implement targeted improvements to reduce costs, minimize inefficiencies, and streamline material flow within the production environment.

Step 4: Proposal of Measures

Based on the identified deficiencies, targeted measures are proposed to optimize material flow and reduce costs. These proposals aim to improve logistics efficiency, reduce handling costs, and enhance overall productivity within the company’s material flow system. These measures may include the following:

• Layout optimization—redesigning the spatial arrangement of workstations to minimize unnecessary movement of materials;
• Reduction of internal transport—implementing direct supply routes or conveyor systems to limit excessive handling;
• Automation of handling processes—introducing automated guided vehicles (AGVs) or conveyor belts to improve efficiency;
• Optimization of transport fleet—evaluating the feasibility of replacing external transport services with an in-house fleet;
• Implementation of FIFO and FEFO methods—ensuring efficient stock rotation and minimizing material obsolescence.

The cost per kilometer is calculated using Formula (4), as follows:

$$C_{km}={\displaystyle \frac{C_{total}}{d_{km}}} [€]$$

(4)

where

C_total—total annual costs (EUR);

d_km—total annual distance traveled (km).

The savings during transportation are calculated using Formula (5), as follows:

$$S_{transp}=C_{original}-C_{new}$$

(5)

where

C_original—current transport cost (EUR);

C_new—new transport cost (EUR).

Step 5: Evaluation of Measures

In the recalculation of the material flow costs after implementing the changes, C_flow is recalculated to assess the achieved savings.

The calculation of the return on investment (ROI) measures the profitability of the total invested capital and expresses the overall efficiency of the company, earning capacity, or production strength [52]. An ROI is calculated according to Formula (6), as follows:

$$\mathrm{R}\mathrm{O}\mathrm{I}={\displaystyle \frac{\mathrm{S}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}-\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}}{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}}}\ast 100\%$$

(6)

$$\mathrm{S}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}={\mathrm{C}}_{\mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{B}}-{\mathrm{C}}_{\mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{A}}$$

(7)

where

C_flowB—total material flow costs before (EUR);

C_flowA—total material flow costs after (EUR);

Investments—total cost of implementing measures (EUR).

The payback period of an investment is the time required for the initial investment expenses to be offset by the cash inflow generated from the investment. It is calculated using Formula (8), as follows:

$$\mathrm{P}\mathrm{P}={\displaystyle \frac{\mathrm{I}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l} \mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}}{\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l} \mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h} \mathrm{f}\mathrm{l}\mathrm{o}\mathrm{w}}}$$

(8)

where

PP—payback period (years);

Initial investments—initial investments (EUR);

Annual cash flow—cash flow generated from them (EUR).

The proposed methodology offers a practical advantage over complex simulation models in terms of real-world application. The use of a checkerboard table combined with quantitative cost analysis enables a fast and efficient identification of savings without the need for extensive data collection and the creation of simulation scenarios, which can be time-consuming and costly. Simulation modeling often requires comprehensive data on process dynamics and interactions, which may not always be available or sufficiently accurate in industrial practice. Therefore, the proposed methodology provides a practical solution that is easily applicable in a business environment and allows for rapid material flow optimization without the need for complex software tools.

The proposed solution is based on an analysis of the current state and the identification of optimization opportunities within existing logistics constraints. While advanced technological solutions, such as automated warehouses, may offer long-term benefits, their implementation requires high investment costs and an extended adaptation period. In contrast, the proposed solution, which focuses on optimizing internal transport processes and improving material flow organization, offers quick and practically feasible improvements without requiring significant capital expenditures. Although alternative analytical and technological approaches may provide additional optimization possibilities, the proposed methodology represents a balanced solution between accuracy, applicability, and financial efficiency. Its key advantage lies in its ease of implementation, direct usability for business decision making, and the ability to quickly respond to identified inefficiencies in material flow.

4. Research Background

Material flow begins with the supply of raw materials and components into the production process and ends with the delivery of the finished product to the end customer. Material flow can be divided into three main stages. The first stage is input, which consists of raw materials and components that are procured and subsequently integrated into the production process. Figure 2 illustrates the basic scheme of material flow.

4.1. Material Flows in the Company

Material Flow

Based on the order, the supplier delivers the required quantity of raw materials (RM) and packaging materials (PM). These materials are subsequently stored according to their specific requirements. The RM is stored in refrigerated and frozen pre-production warehouses, while bulk materials are kept in silos. The PM is stored in two separate warehouses, as follows:

• The primary packaging warehouse, which contains the direct packaging of pouches (bags);
• The secondary packaging warehouse, which stores materials for the packing facility, including cartons, dividers, and protective corners.

Figure 3 illustrates the actual material flow within the manufacturing company in Veselí nad Lužnicí.

Based on the production plan and recipes, raw materials (RM) are released to section 1, where they undergo grinding and mixing with various components (additives, vitamins, flour, vegetables, pasta, etc.), forming the mixture. The mixture is then transported through a steam tunnel, where it is pre-cooked, before being transferred to the production lines, where it is filled into pouches, resulting in semi-finished goods (SF). The SF are placed in vegetable crates, with 160 pieces per crate and palletized in logistics at 28 crates per pallet. They are then transported to a warehouse located 2 km away, where they are stored for three days. After this period, the SF are loaded for return transport to the factory premises, where they are stored for one additional day before being packed at the packaging center on the fourth day according to customer requirements. These four days represent the maturation period, during which potential production issues become evident. Defective pouches begin to emit an odor or inflate starting from the second day after cooking. This process allows for the sorting of defective SF from the properly produced batches.

The packaging center operates with the following five packaging lines: two dedicated to the 12-pack format, one for the 24/48-pack format, and two for the flow-pack format (6/4× pouches in a bag). Based on customer requirements, the pouches are packed into cartons and palletized. Once wrapped in stretch film, the finished pallet is labeled with a pallet tag and recorded in the finished goods system, creating finished goods (FG). The FG warehouse is equipped with gravity flow racks with a maximum capacity of 420 pallet positions, which covers approximately 38 h of production. This flow-through FG warehouse sorts goods according to the final distribution warehouse, after which they are loaded onto trucks and transported to Distribution Warehouse 1 in Tábor or Distribution Warehouse 2 in České Budějovice. On average, nine truckloads of FG are dispatched daily. The distribution warehouses are in two locations. The first warehouse in Tábor is an internal warehouse, serving retail customers and brands managed by the manufacturing company. Here, goods are consolidated and dispatched according to customer orders. The Tábor warehouse has a capacity of 15,000 pallet positions and includes a co-packing center for the entire manufacturing group, where products are custom-mixed based on customer specifications. The second warehouse in České Budějovice is an external warehouse operated by a third-party logistics provider. This warehouse handles FG designated for sister companies in Poland, Slovakia, Hungary, and the Netherlands, categorized as Intercompany FG. According to intercompany agreements, these goods must be shipped within 14 days of the production date, either to the sister company warehouses or directly to customers. Both distribution warehouses can cooperate and transfer goods between each other as needed. Like any production facility, the company generates defective products, waste, and residual materials. Given that these products are of animal origin, all waste is sent to rendering companies or incineration facilities. This disposal process is financially demanding, with waste management companies charging EUR 239.30 per ton for disposal services.

Information Flow

Material flow within the company cannot function without a corresponding information flow [53]. The company utilizes Microsoft Navision as its enterprise resource planning (ERP) system, where the entire material flow is recorded and tracked, from raw material intake to finished goods (FG) dispatch. This ensures the traceability of all movements, which is a requirement of audit standards for ISO (International Organization for Standardization), IFS certifications (International Featured Standards), and private-label customers who strictly audit their production processes. The Navision system consists of key modules that facilitate information flow both within the company and externally. These modules include finance management, sales and marketing, procurement, warehousing, production, resource planning, human resources, transport management, and quality management.

Company Layout

Figure 4 illustrates the current spatial arrangement (layout) of the company in Veselí nad Lužnicí. This figure represents the material flow within the production facility, covering key areas, such as intake, storage, production, and dispatch. Each numbered and labeled section corresponds to specific processes or locations within the facility, ensuring a clear overview of the operational workflow.

Storage and Handling

The storage process includes activities such as receipt (RM, PM, SF, and FG), stocking, handling, picking, dispatch, maintenance, and cleaning. Throughout the PPF facility, the following two inventory management methods are strictly followed: FIFO and FEFO. FIFO ensures that the material received first is also the first to be dispatched, while FEFO prioritizes the earliest expiration date. Since this is a food production company, the FEFO method always takes precedence over FIFO. These methods are easily implementable, traceable, and fully supported by the enterprise system, including the Warehouse Management System (WMS). The WMS system guides warehouse operators by indicating the correct material location and expiration sequence for picking, ensuring compliance with inventory control standards.

In the company, there are multiple separate storage areas (Figure 4), not all of which are located directly within the factory premises. All warehouses are managed by the WMS, ensuring data accuracy and inventory control. For ease of operation, warehouses are divided into sections. If storage areas are equipped with racks, each pallet location has its own identification label, called a bin (compartment). The WMS system directs material release preparation by guiding warehouse operators to the correct bin and ensuring that materials are picked according to FEFO and FIFO principles. Material handling is performed using electric forklifts (both high-lift and low-lift), conveyors connecting production lines, trucks, and, in some areas, manual handling.

4.2. Material Flow Cost Calculation

To optimize material flow with the primary goal of increasing its efficiency, it is essential to gather sufficient information and data, map out the processes, and conduct an analysis based on these findings. For the calculation of material flow, it is crucial to determine the volume of handled material in measurable units, in this case, the tons per year, handling distance, and, most importantly, handling costs.

Handling Costs

The total handling costs, in this case, include the operating expenses of forklifts and pallet trucks, transportation costs between FG and SF warehouses, and personnel costs. Additionally, these costs also cover the rental expenses of the Fruta warehouse, where the material maturation process takes place.

Handling Equipment Costs

The costs associated with pallet trucks and forklifts are easily identifiable, as the company acquires these resources through long-term leases. For its operations, the company utilizes handling equipment from Linde, specifically eight E20 forklifts and three T16 pallet trucks.

Table 1. Handling equipment costs.

Item	Units	Cost per Unit (EUR)	Total per Month (EUR)	Total per Year (EUR)
Forklift	8	739.89	5919.16	70,029.87
Pallet truck	3	267.96	803.88	9646.55
Total	11		6723.04	80,676.43

presents the handling equipment costs in the company.

The monthly cost for one forklift is EUR 739.89, while the cost for one pallet truck is EUR 267.96. The total annual cost amounts to EUR 80,676.43. The handling equipment is leased under a full-service agreement.

Personnel Costs

The total payroll costs cover all expenses related to employees, including contributions, training, protective equipment, and medical check-ups. The factory operates on a continuous 24/7 schedule, and all operations involved in material flow follow this regime. Fruta Warehouse operates from Monday to Friday in a two-shift system (6:00 AM–10:00 PM) with morning and afternoon shifts. On Saturdays, it operates from 6:00 AM to 6:00 PM, while on Sundays, its schedule aligns with truck traffic restrictions. FG Warehouse operates in a single-shift system from 6:00 AM to 6:00 PM.

Table 2. Personnel costs.

Personnel Costs for Handling	Quantity	Costs for the Section per Month (EUR)	Costs for the Section per Year (EUR)	Shift Work
section 1	4	6378.67	76,544.09	24/7 Non-stop
End of the line	4	6378.67	76,544.09	24/7 Non-stop
Unloading of semi-finished goods (SF)	4	6378.67	76,544.09	24/7 Non-stop
Finished goods (FG) from the line	4	6378.67	76,544.09	24/7 Non-stop
Finished goods (FG) for transport	2	3189.34	38,272.05	Day shift 12
Fruta	4	6378.67	76,544.09	Morning/Afternoon shift 6–22
Total	22	35,082.71	420,992.50	-

shows the personnel costs in the company.

Total personnel costs amount to EUR 420,992.50 per year.

Costs for SF and FG Transfers

A significant portion of expenses consists of the cost of transferring SF to the Fruta Warehouse and transporting FG to distribution warehouses in České Budějovice. The cost of a single SF transfer is EUR 39.67, with an average total of 19 truck movements (OUT + IN) per day. This results in daily costs of EUR 753.70, with a cost per full truck cycle of EUR 79.34. The transport provider operates two trucks, which are exchanged at ramps and shuttle between the factory location and the Fruta Warehouse.

Table 3. Costs for SF transport from factory to Fruta.

Costs for the Transport of SF	Number of Transport	Total Costs (EUR)
Transport IN–OUT day	19	753.70
Transport IN–OUT year	6593	261,533.58

presents the costs associated with SF transport from the factory to Fruta.

The cost for a single FG transport (

Table 4. Costs for FG transport.

Costs for the Transport of FG	Number of Transports	Total Cost (EUR)
Transports/day	8	634.69
Transports to České Budějovice/year	694	55,059.70
Transports to Tábor/year	2082	165,179.10
Total per year	2776	220,238.80

) averages EUR 79.34. There are two trucks in operation, continuously moving between the FG Warehouse and distribution centers from 6:00 AM to 6:00 PM. In exceptional cases, transport times can be extended beyond these hours.

On average, one truck completes four transports per day, with a total of 2776 transports annually to both warehouses. The total annual costs amount to EUR 220,238.80.

Costs of Renting the Fruta Warehouse

The Fruta Warehouse covers an area of 2000 m² and is used for storing SF during the ripening period. The monthly rental cost is EUR 30,742.99 (excluding VAT), resulting in an annual cost of EUR 35,701.54 (excluding VAT). The warehouse is leased from Italian owners.

4.3. Material Flow Calculations

To calculate the material flow, it is essential to have plans that indicate the actual or planned flow. The annual average is approximately 132 tons per day.

Table 5. Annual plan for packed tons of FG.

Month	1	2	3	4	5	6
Packed tons	3383	3200	3100	3350	3466	3790
Production days	30	27	30	28	30	29
Average per day/t	113	119	103	120	116	131
Month	7	8	9	10	11	12
Packed tons	4330	4471	4215	4372	4247	3878
Production days	30	29	30	30	29	25
Average per day/t	144	154	141	146	146	155

presents the annual plan for packed tons of FG.

Total annual packed tons amount to 45,802 t, with 347 productive days and a daily average of 132 t. The quantity entering the production process and the total annual volume in tons is shown in

Table 6. Material flow volume.

From	To	Tons/Year
Receipt of primary packaging materials (PM)	Primary packaging materials warehouse (PM)	504
Receipt of secondary packaging materials (PM)	Secondary packaging materials warehouse (PM)	4719
Receipt of raw materials (RM)	Raw materials warehouse (RM)	47,634
Raw materials warehouse (RM)	section 1	47,634
section 1	Production lines	47,634
Primary packaging materials warehouse (PM)	Production lines	476
Production lines	Output from lines	48,110
Output from lines	Semi-finished goods warehouse (SF)	48,110
Semi-finished goods warehouse (SF)	Fruta	48,110
Fruta	Packaging	48,110
Secondary packaging materials warehouse (PM)	Packaging	4461
Packaging	Finished goods warehouse (FG)	45,802
Finished goods warehouse (FG)	České Budějovice	11,451
Finished goods warehouse (FG)	Tábor	34,351
Packaging	Waste	6769
Total		443,875

. Table 6 also illustrates the origin and destination of the material transfers. In total, 443,875 tons of material flow through the system annually.

Relationships between individual workplaces are expressed using the checkerboard table method.

Table 7. Checkerboard table.

From–To	Receipt of Primary Packaging Materials (PM)	Receipt of Secondary Packaging Materials (PM)	Receipt of Raw Materials (RM)	Raw Materials Warehouse (RM)	section 1	Primary Packaging Materials Warehouse (PM)	Production Lines	Output from Lines	Semi-Finished Goods Warehouse (SF)	Fruta	Secondary Packaging Materials Warehouse (PM)	Packaging	Finished Goods Warehouse (FG)	Finished Goods Warehouse (FG) ČB	Finished Goods Warehouse (FG) Tábor	Waste	SUM
Receipt of primary packaging materials (PM)						504											504
Receipt of secondary packaging materials (PM)											4719						4719
Receipt of raw materials (RM)				47,634													47,634
Raw materials warehouse (RM)					47,634												47,634
section 1							47,634										47,634
Primary packaging materials warehouse (PM)							476										476
Production lines								48,110									48,110
Output from lines									48,110								48,110
Semi-finished goods warehouse (SF)										48,110							48,110
Fruta												48,110					48,110
Secondary packaging materials warehouse (PM)												4461					4461
Packaging													45,802			6769	52,571
Finished goods warehouse (FG)														11,451	34,351		45,802
Finished goods warehouse (FG) ČB																	0
Finished goods warehouse (FG) Tábor																	0
Waste																	0
SUM	0	0	0	47,634	47,634	504	48,110	48,110	48,110	48,110	4719	52,571	45,802	11,451	34,351	6769	443,875

illustrates the relationships between various workplaces, indicating the direction of material flow and the annual material flow volume in tons. Table 7 presents the same input and output data as processed in Table 6. For verification, the values listed in the row and column labeled “sum” in the checkerboard table are added together, and the total should match. As shown in Table 7, the total material flow amounts to 443,875 tons per year.

The checkerboard table can be expanded to include additional data, such as transport frequency, handling costs, and transport performance. To incorporate these indicators alongside the processed data, we need to calculate them. For further calculations, the costs per meter and per ton are determined. The total costs are calculated as the sum from Table 1, Table 2, Table 3 and Table 4, resulting in an amount of EUR 1,019,142.85. This value is then divided by the total number of tons to determine the handling cost per ton. The same approach is used for calculating the cost per meter, where the total number of tons is replaced by the total number of kilometers traveled annually. The cost per meter is, thus, calculated at EUR 0.01. These calculated costs are presented in

Table 8. Overall summary of material flows.

From	To	Tons/Year	Frequence/ Year	Distance (m)	Performance t × m	m/Year	Handling Costs (EUR)
Receipt of primary packaging materials (PM)	Primary packaging materials warehouse (PM)	504	2016	40	20,160	80,640	863.70
Receipt of secondary packaging materials (PM)	Secondary packaging materials warehouse (PM)	4719	7080	20	94,380	141,600	1516.60
Receipt of raw materials (RM)	Raw materials warehouse (RM)	47,634	127,024	10	476,340	1,270,240	13,604.86
Raw materials warehouse (RM)	section 1	47,634	127,024	20	952,680	2,540,480	27,209.73
section 1	Production lines	47,634	63,512	20	952,680	1,270,240	13,604.86
Primary packaging materials warehouse (PM)	Production lines	476	1904	20	9520	38,080	407.87
Production lines	Output from lines	48,110	347	100	4,811,000	34,700	371.65
Output from lines	Semi-finished goods warehouse (SF)	48,110	96,220	5	240,550	481,100	5152.80
Semi-finished goods warehouse (SF)	Fruta	48,110	3123	2000	96,220,000	6,246,000	66,897.54
Fruta	Packaging	48,110	3470	2000	96,220,000	6,246,000	74,330.60
Secondary packaging materials warehouse (PM)	Packaging	4461	2230	50	223,050	111,500	1194.22
Packaging	Finished goods warehouse (FG)	45,802	22,901	10	458,020	229,010	2452.81
Finished goods warehouse (FG)	České Budějovice	11,451	694	34,000	389,334,000	23,596,000	252,724.03
Finished goods warehouse (FG)	Tábor	34,351	2082	31,000	1,064,881,000	64,542,000	691,274.54
Packaging	Waste	6769	347	50	338,450	17,350	185.85
Total		443,875	459,974	69,345	30,780,511,875	107,538,940	1,151,791.58

, which is expanded to include handling costs, annual frequency of movements, distance between workplaces, transport performance in tons per meter, and total distance traveled per year.

When calculating the transport performance, which is determined by multiplying the distance between workplaces and the volume of material, it becomes evident that items dependent on the transportation of material between the factory, ripening warehouse, and distribution warehouse show extremely high values. This indicates an undesirable phenomenon leading to a significant increase in material flow costs, making it essential to focus on this aspect during optimization.

4.4. Layout of Material Flows in the Company

In Figure 5, the material flows within the company are illustrated. The blue arrows represent the flow of materials outward to the Fruta warehouse, while the red arrows indicate the return flow from Fruta back to the factory and into the packaging area. The black arrow represents the flow of packaging material (PM) for the packaging process. The orange arrow signifies the flow of finished goods (FG) into the FG warehouse, and the purple arrow depicts the transport of FG to distribution warehouses.

4.5. Deficiencies Identified in Material Flow

Based on the analysis of the current state, including recalculations of material flow volumes and related costs, direct observation, interviews with employees, and personal experience with knowledge of the given company, several inefficiencies in material flow processes and their impact on associated costs were identified. The key issues in the material flow include the following:

• Transport of SF to the Fruta warehouse for maturation;
• Return transport of SF from Fruta back to the factory for packaging;
• Transport of FG from the production warehouse to distribution warehouses.

Given that the company is owned by financial funds, there is a strong emphasis on return on investment. If the company decides to invest in any measure or development action, it must ensure a return on investment within two years. In exceptional cases, the company allows an investment payback period of up to three years. From this perspective, the construction of warehouses directly within the factory is an impossible task, even though the company has already acquired land near the manufacturing company for this purpose.

The first two processes are part of SF transport, where I see significant potential for optimization and cost reduction. The current cost of these processes amounts to EUR 262,585.63, and if production continues to expand, this expense will only continue to increase.

5. Results

In this study, two optimization variants were selected to improve material flow, as follows: (1) replacing an external carrier for semifinished goods transport with internal processes, and (2) replacing an external carrier for finished goods transport with an in-house fleet. The criteria for selecting these variants were established based on an analysis of current logistics operation costs, availability of internal resources, and potential savings resulting from shifting logistics activities into the company’s internal environment.

The selection criteria for the optimization variants included the following:

• Cost efficiency, identifying potential savings by eliminating external transport providers;
• Availability of internal capacities, assessing the company’s ability to manage transport with its own resources;
• Operational flexibility, evaluating the potential for faster and more reliable material flow management if handled internally;
• Impact on the supply chain, assessing the effects on production continuity and logistics processes;
• Long-term sustainability, considering return on investment (ROI) and environmental benefits.

5.1. Variant 1—Replacement of External Operator for SF Transport with In-House Company Transport

One of the ways to reduce material flow costs is to lower transportation expenses for transfers. Since negotiating lower prices with external operators has not been successful, we consider the possibility of acquiring two transport units through an operational lease and hiring two drivers with a C + E driving license.

To assess the cost-effectiveness of this change, we calculated the current transport costs per kilometer. At present, the transport cost for SF transfers amounts to EUR 19.91 per kilometer.

Table 9. Costs of in-house transport.

Lease for 48 Months	Costs (EUR)
2x Road freight vehicle	40,146.57
Operating costs	4420.90
Personnel costs for 2x driver	51,234.67
Total	95,802.13

presents the projected costs of this investment. The acquisition of two transport units would result in an annual expense of EUR 40,250.77, with a monthly lease cost of EUR 1672.77 per unit. The operating costs are calculated based on the estimated annual mileage of 13,186 km and the current fuel price of EUR 1.28 per liter, as reported by the Czech Statistical Office. Additionally, personnel costs for two drivers are estimated at EUR 51,234.67.

The cost per kilometer is calculated using Formula (4), as follows:

$$c_{km}={\displaystyle \frac{\mathrm{95,802.13}}{\mathrm{13,186}}}=7.27 €$$

Based on the cost per kilometer data, we can now determine the price of a single transport to Fruta. The savings per transport are calculated using Formula (5), as follows:

$$S_{transp}=39.83-14.54=25.29 €$$

The savings per transport amount to EUR 25.29, resulting in daily savings of EUR 480.52 for 19 transports. Annually, this leads to a total potential savings of EUR 166,741.88 on transport costs.

Table 10. Overview of savings on SF transport.

SF Transport Costs	Number of Transfers	Total Costs (EUR)
IN–OUT transfers per day (original state)	19	756.73
IN–OUT transfers per year (original state)	6593	262,585.63
IN–OUT transfers per day (proposed state)	19	276.21
IN–OUT transfers per year (proposed state)	6593	95,843.75
Daily savings	19	480.52
Annual savings	6593	166,741.88

presents an overview of the savings on SF transport.

Subsequently, we deduct these savings from SF transport and recalculate the total material handling costs. The costs after optimization are shown in

Table 11. Costs after optimization.

Costs	Pcs.	Cost per Unit (EUR)	Total Monthly Costs (EUR)	Annual Costs (EUR)
Forklift	8	742.87	5942.97	71,315.60
Pallet truck	3	269.04	807.11	9685.36
Handling staff	22	1601.08	35,223.83	422,686.00
SF transport	6593	14.54	7986.98	95,843.75
FG transport	2776	79.66	18,427.06	221,124.74
Fruta lease	1	2987.10	2987.10	35,845.15
Total costs				856,500.60
Total costs per meter				0.01

.

After recalculating the costs, the handling expenses were reduced. However, when comparing data from Table 8 and

Table 12. Overall flow overview for Variant 1.

From	To	Tons/Year	Frequency/ Year	Distance (m)	Performance (t × m)	m/Year	Handling Costs
Receipt of primary packaging materials (PM)	Primary packaging materials warehouse (PM)	504	2016	40	20,160	80,640	642.35
Receipt of secondary packaging materials (PM)	Secondary packaging materials warehouse (PM)	4719	7080	20	94,380	141,600	1127.93
Receipt of raw materials (RM)	Raw materials warehouse (RM)	47,634	127,024	10	476,340	1,270,240	10,118.21
Raw materials warehouse (RM)	section 1	47,634	127,024	20	952,680	2,540,480	20,236.42
section 1	Production lines	47,634	63,512	20	952,680	1,270,240	10,118.21
Primary packaging materials warehouse (PM)	Production lines	476	1904	20	9520	38,080	303.33
Production lines	Output from lines	48,110	347	100	4,811,000	34,700	276.41
Output from lines	Semi-finished goods warehouse (SF)	48,110	100,300	5	240,550	501,500	3994.74
Semi-finished goods warehouse (SF)	Fruta	48,110	3123	2000	96,220,000	6,246,000	49,753.07
Fruta	Packaging	48,110	3470	2000	96,220,000	6,940,000	55,281.19
Secondary packaging materials warehouse (PM)	Packaging	4461	2230	50	223,050	111,500	888.16
Packaging	Finished goods warehouse (FG)	45,802	22,901	10	458,020	229,010	1824.20
Finished goods warehouse (FG)	České Budějovice	11,451	694	34,000	389,334,000	23,596,000	187,956.03
Finished goods warehouse (FG)	Tábor	34,351	2082	31,000	1,064,881,000	64,542,000	514,115.02
Packaging	Waste	6769	347	50	338,450	17,350	138.20
Total		443,875	464,054	69,345	30,780,511,875	107,559,340	856,773.46

, the cost reduction did not affect the size, intensity, or distances of the material flow. Thus, despite the lower costs, the overall flow volume remained unchanged.

Return on Investment Calculation for Variant 1

To calculate the ROI, it is necessary to determine the operational profit generated by the investment. In this case, the total savings from the transportation of semifinished goods (SF) serve as the operational profit for the investment.

The ROI is calculated using Formula (6), and after applying the relevant values, the investment return rate is determined to be 74%. The payback period of the investment is calculated using Formula (8), and after inserting the values, the investment payback period is 0.57 years, meaning that the company will recover the investment in 208 days, which meets the required return period of less than two years.

Optimized Material Flow Layout

Figure 6 illustrates the new material flow after optimization. The implemented changes have improved process efficiency and streamlined the material flow, ensuring a more optimal and cost-effective logistics operation.

5.2. Variant 2—Replacing External Operators for FG Transport with In-House Fleet

Like Variant 1, one of the cost-saving strategies for material flow optimization is reducing transportation costs. This proposal considers the operational leasing of two truck-trailer units and hiring a fourth driver with a C + E license. The drivers would rotate on these two units to ensure full utilization of the vehicles. To determine the cost indicator, we calculate the current transportation frequency cost per kilometer, which presently stands at 1.24 EUR/km for FG transport. The acquisition cost for two truck-trailer units amounts to EUR 40,202.61, with a monthly lease of EUR 1675.11 per unit. Operating costs are calculated based on the estimated annual mileage of 176,276 km and the current fuel price of 1.43 EUR/L for diesel [36] (accessed on 11 February 2025). Personnel costs for four drivers are estimated at EUR 102,612.37 (see

Table 13. Costs for two road freight units.

Lease for 48 Months	Costs (EUR)
2x Road freight vehicle	40,202.61
Operating costs	58,895.98
Personnel costs for 4x driver	102,612.37
Total	201,710.96

).

To determine the cost per kilometer, we use Formula (4), as follows:

$$c_{km}={\displaystyle \frac{\mathrm{201,710.96}}{\mathrm{176,276}}}=1.14 €$$

Based on the cost per kilometer calculations, we can now determine the cost of a single transport to the Lašek warehouse and the Tábor warehouse. The round-trip distance between the factory and Lašek is 68 km, while the round-trip distance between the factory and Tábor is 62 km. The cost for a single transport to České Budějovice (Lašek Warehouse) is EUR 78.65. The cost for a single transport to Tábor is EUR 71.71. The savings per transport are calculated using Formula (5), as follows:

$$S_{transp}^{Lašek}=79.77-78.65=1.12 €$$

$$S_{transp}^{Tábor}=79.77-71.71=8.06 €$$

Savings per transport to Tábor: EUR 8.06; savings per transport to Lašek: EUR 1.12. On an annual basis, the total savings on FG transport amount to EUR 17,548.60 (see

Table 14. Costs of FG transport.

FG Transport Costs	Distance (km)	Frequency	Total (km)	Price per km	Total Price (EUR)
České Budějovice (ČB)	68	694	47,192.00	1.14	54,583.34
Tábor	62	2082	129,084.00	1.14	149,301.48
Total (km)			176,276.00	1.14	203,884.82
Annual savings (ČB)	775.02
Annual savings (Tábor)	16,773.58
Total annual savings	17,548.60

).

Next, the savings from FG transport are deducted, and the total material handling costs are recalculated, as shown in

Table 15. Costs after FG optimization.

Costs	Pcs.	Cost per Unit (EUR)	Total Monthly Costs (EUR)	Annual Costs (EUR)
Forklift	8	743.91	5951.26	71,415.15
Pallet ruck	3	269.41	808.24	9698.88
Handling staff	22	1603.32	35,273	423,276.03
SF transport	6593	14.56	7998.13	262,952.18
FG transport	2776	1.24	16,990.40	203,884.82
Fruta lease	1	2991.27	2991.27	35,895.19
Total costs				1,007,122.24
Total costs per tons				2.27
Total costs per meter				0.01

.

After recalculating the costs, material handling costs were reduced. The optimization resulted in cost savings, but the size, intensity, and distances remained unchanged. Despite the reduction in handling costs, the material flow volume did not change, even though a total savings of EUR 150,916.84 was achieved.

Table 16. Material flow volume after optimization—Variant 2.

From	To	Tons/Year	Frequency/ Year	Distance (m)	Performance (t × m)	m/Year	Handling Costs
Receipt of primary packaging materials (PM)	Primary packaging materials warehouse (PM)	504	2016	40	20,160	80,640	755.19
Receipt of secondary packaging materials (PM)	Secondary packaging materials warehouse (PM)	4719	7080	20	94,380	141,600	1326.13
Receipt of raw materials (RM)	Raw materials warehouse (RM)	47,634	127,024	10	476,340	1,270,240	11,896.02
Raw materials warehouse (RM)	section 1	47,634	127,024	20	952,680	2,540,480	23,792.09
section 1	Production lines	47,634	63,512	20	952,680	1,270,240	11,896.02
Primary packaging materials warehouse (PM)	Production lines	476	1904	20	9520	38,080	356.64
Production lines	Output from lines	48,110	347	100	4,811,000	24,700	324.97
Output from lines	Semi-finished goods warehouse (SF)	48,110	962,220	5	240,550	481,100	4505.60
Semi-finished goods warehouse (SF)	Fruta	48,110	3123	2000	96,220,000	6,246,000	58,494.95
Fruta	Packaging	48,110	3470	2000	96,220,000	6,940,000	64,994.38
Secondary packaging materials warehouse (PM)	Packaging	4461	2230	50	223,050	111,500	1044.23
Packaging	Finished goods warehouse (FG)	45,802	22,901	10	458,020	229,010	2144.74
Finished goods warehouse (FG)	České Budějovice	11,451	694	34,000	389,334,000	23,596,000	220,980.94
Finished goods warehouse (FG)	Tábor	34,351	2082	31,000	1,064,881,000	64,542,000	604,447.89
Packaging	Waste	6769	347	50	338,450	17,350	162.49
Total		443,875	459,974	69,345	30,780,511,875	107,538,940	1,007,122.24

presents the material flow volume after optimization for Variant 2.

Return on Investment Calculation for Variant 2

To determine the ROI, it is necessary to calculate the operational profit generated by the investment. The input data used for this calculation include the total cost savings from FG transport, which serves as the operational profit, amounting to EUR 17,548.60, while the investment costs total EUR 201,710.96.

The ROI is calculated using Formula (6). After substituting the values into the formula, the resulting return on investment is −91%. The payback period is determined using Formula (8). After applying the values, the payback period is calculated to be 11.5 years, which does not meet the required investment return condition of two years.

6. Discussion

Based on the analysis of the current state of material flow at the company in Veselí nad Lužnicí production facility, measures have been proposed to optimize its efficiency. The objective of this discussion is to compare two proposed solutions and evaluate their benefits in terms of effectiveness, cost reduction, and implementation complexity.

Variant 1 focuses on analyzing the transportation costs associated with transferring SF to the Fruta Warehouse, located two kilometers from the manufacturing company. A significant portion of expenses arises from SF transport to Fruta, where the maturation process occurs before the goods are returned to the factory for final packaging into FG.

An alternative solution to reduce transportation costs involves acquiring an in-house fleet of transport units, thereby reducing dependence on an external operator. This approach does not directly optimize the material flow, but rather reduces transportation costs, which constitute a significant portion of the overall expenses when allocated across individual transport operations. Implementing this strategy would result in cost savings of EUR 166,974.63 on SF transport. Figure 7 illustrates the transportation costs associated with SF.

This transport optimization is reflected in the overall material flow costs, reducing the cost per transported meter and achieving savings of EUR 0.03 per meter. As a result, total variable costs decreased from the original EUR 1,158,039.09 to EUR 857,806.72, generating savings of EUR 300,232.36. From an investment return perspective, this investment proves to be cost-effective, yielding a 74% return. In terms of payback period, the investment will recover within 208 days of implementation, thereby meeting the company’s investment condition of a return within two years.

Variant 2 focuses on replacing external operators for FG transportation with in-house transport and analyzing costs related to FG transport to distribution warehouses, which are located 31 and 34 km from the manufacturing company. A significant share of the total material flow costs is attributed to FG transport from the factory to distribution warehouses, from which products are shipped to customers.

One of the possible ways to reduce transport costs is to establish an in-house fleet, thereby eliminating the need for external operators. Like Variant 1, this approach does not directly optimize material flow but focuses on reducing costs related to material movement, which, when allocated to individual transport operations, account for a significant portion of total expenses. The costs associated with acquiring the fleet through long-term leasing, its operation, and staffing four drivers amount to EUR 201,710.96.

The savings per transport to Tábor amount to 8.06 EUR, while for Lašek, they amount to EUR 1.12. This results in potential annual savings of EUR 17,547.40. Despite the relatively low annual savings, variable material flow costs were reduced by EUR 150,916.84. However, due to the high investment costs and low operational profit, ROI was calculated at −91%, with a payback period of 11.5 years. From this perspective, this variant is deemed unfeasible, as it does not meet the required investment return conditions.

While both optimization variants present potential cost savings, their implementation faces several practical challenges. One of the main obstacles in Variant 2 is the high initial investment required to acquire an internal fleet, along with its ongoing operational and maintenance costs. Additionally, the risk of investment return remains a key concern, as the calculations indicate that while Variant 1 meets the expected payback period, Variant 2 significantly exceeds it, making it financially unviable.

A critical challenge is the organizational transition from an externally managed transport system to an in-house model. This shift would require substantial logistical adjustments, including personnel training, maintenance planning, and fleet management. Moreover, internal transport dependence introduces new operational risks, such as vehicle downtime or scheduling conflicts, which must be mitigated through efficient fleet utilization strategies. To address these issues, a phased implementation approach could distribute investment costs over time while allowing performance evaluation before full-scale adoption. Alternatively, a hybrid transport model, combining both internal and external logistics services, may offer a balanced solution between cost control and operational flexibility. Implementing real-time monitoring technologies, such as IoT-based fleet tracking and route optimization algorithms, could further enhance transport efficiency and reduce unnecessary costs.

From an environmental sustainability perspective, optimizing material flows contributes to lower emissions and reduced energy consumption. By minimizing transport distances and adopting fuel-efficient transport methods, Variant 1 aligns better with sustainable logistics principles. If internal transport units were powered by alternative energy sources, such as electric or hybrid vehicles, the overall carbon footprint of logistics operations could be further minimized. These improvements support broader sustainability objectives in supply chain management and contribute to the green transition in industrial logistics.

The proposed methodology introduces a unique combination of theory and practice, enriching the academic discourse on material flow optimization. By reducing material, energy, and time waste, material flow optimization indirectly contributes to environmental sustainability. Unlike methodologies that focus solely on logistics or financial analysis, this approach integrates a multidimensional perspective on material flow, cost efficiency, and economic return. The methodology provides both professionals and academics with a new tool for effective material flow analysis and optimization. Its added value lies in the distinctive combination of visual and quantitative tools that are easy to implement while being supported by robust economic analysis. This combination makes it both practically relevant and scientifically significant, offering a competitive advantage over existing approaches.

In addition to direct economic benefits, optimized material flows also lead to reduced transportation distances and improved fuel efficiency, which in turn contribute to lower CO₂ emissions and reduced environmental impact. Future research should incorporate environmental impact assessments, such as measuring emissions savings due to reduced transport distances and fuel consumption, to further quantify the sustainability benefits of material flow optimization. Material flow optimization is a continuously evolving field that can benefit from technological advancements, changes in the business environment, and increasing demands for efficiency and sustainability. This comprehensive approach ensures that material flow optimization is not only economically viable but also environmentally responsible, supporting broader sustainability goals in logistics and supply chain management.

While this study provides valuable insights into material flow optimization and cost reduction strategies in the manufacturing industry, several limitations must be acknowledged. The analysis is based on data from a single production facility in Veselí nad Lužnicí, which may limit the generalizability of the results. Although the methodologies can be applied in similar industrial settings, variations in production processes, facility layouts, and supply chain structures could influence the effectiveness of the proposed solutions. Additionally, some calculations rely on estimated values, particularly in cost modeling and return-on-investment (ROI) assessments, introducing a degree of uncertainty. Assumptions related to fuel prices, transport efficiency, and labor costs may change under different economic conditions, potentially impacting the long-term feasibility of the optimization strategies.

Another limitation is the study’s scope concerning external market fluctuations. Factors such as volatile fuel prices, shifts in transportation regulations, and unexpected supply chain disruptions (e.g., raw material shortages, labor strikes, or geopolitical instability) could significantly impact the feasibility of the proposed solutions. Future research should explore how adaptive logistics strategies and risk management frameworks can help mitigate these uncertainties. Additionally, while the study highlights environmental benefits related to reduced transport distances and emissions, a more detailed analysis of the ecological impacts such as life cycle assessment (LCA)—could provide deeper insights into sustainability improvements.

Finally, the study focuses primarily on financial and operational efficiency without fully considering the organizational and human factors involved in implementing these changes. Transitioning to an internal transport model may require workforce restructuring, training programs, and new management strategies to ensure smooth integration. Future research should investigate the managerial and behavioral challenges associated with material flow optimization, particularly in employee adaptation and process automation. Addressing these factors will help refine the methodology and support its broader applicability in different industrial contexts.

Future research should prioritize the most promising directions that build upon the findings of this study. One key focus should be the integration of advanced digital technologies, such as artificial intelligence (AI) and automation, into material flow optimization. AI-driven predictive analytics could enhance decision-making by identifying inefficiencies, forecasting demand fluctuations, and adapting logistics strategies in real time. Automation technologies, including autonomous transport systems and robotic handling, could significantly reduce operational costs and increase efficiency. Additionally, further studies could explore the impact of real-time monitoring systems and digital twins in simulating and optimizing material flows before implementation. Digital twins can provide valuable insights by replicating logistical operations in a virtual environment, allowing companies to test various scenarios and identify optimal configurations. These advancements have the potential to significantly improve material flow management, making logistics processes more adaptive, cost-effective, and environmentally friendly. Another important area of research is the role of Industry 4.0 technologies, such as IoT-based tracking and blockchain applications, in improving supply chain transparency and traceability. The implementation of sensors and IoT devices for real-time monitoring of material flow would enable more precise analysis and faster decision-making. Blockchain technology could further enhance security and efficiency in supply chain transactions by ensuring accurate and tamper-proof data sharing among stakeholders. Future research could also examine the environmental impact of material flow optimization in greater detail. Incorporating life cycle assessment (LCA) methodologies to measure emissions reduction and energy consumption savings would provide a more comprehensive view of sustainability improvements. Comparative studies assessing the environmental performance of different transportation modes, including rail versus road logistics, could help refine sustainability strategies and promote greener supply chain practices. The studies should investigate the broader implications of material flow optimization on overall business performance, such as its effects on customer service, delivery speed, and supply chain resilience. By expanding research in these directions, future studies will not only enhance the practical value of material flow optimization methodologies but also ensure their adaptability to evolving technological, economic, and environmental challenges.

Although IoT technologies and machine learning are not yet utilized in the current operations of the analyzed plant in Veselí nad Lužnicí, their implementation could further enhance the material flow optimization process. As part of future modernization efforts, IoT sensors could be introduced to monitor material movement in real time, providing more accurate data on the efficiency of logistics flows. Machine learning could then analyze this data, automatically identifying patterns and potential bottlenecks in the process, enabling the development of better strategies to reduce costs and improve time efficiency. This approach could serve as a valuable complement to the already proposed measures and represents a promising direction for the future development of the plant in the context of Industry 4.0.

7. Conclusions

Based on the analysis of the current state of material flow in the selected company, the objective was to propose measures aimed at optimizing the material flow and to evaluate their effectiveness. The analysis was conducted at the manufacturing company in Veselí nad Lužnicí in the Czech Republic. An integral part of material flow is the information flow, whose characteristics and functions were described. The layout provided an overview of the spatial arrangement and the functions of various sections and workplaces within the facility. The final part of the current state analysis covered storage and material handling, including inventory management methods such as FIFO and FEFO. The material flow analysis was divided into the quantification of handling costs, including personnel expenses, transportation costs, costs of handling equipment, and warehouse rental for the aging process.

After summing these costs, the total expenses amounted to EUR 1,024,670.84. The second part of the material flow analysis focused on calculating the actual material flows. Based on these calculations, it was determined that the planned annual volume of finished goods production is 45,802 tons, while the cumulative material flow through various workplaces reached 4,433,875 tons per year. The relationships between individual workplaces were transferred into a checkerboard table, and key material flow indicators were calculated, including total material volume (tons per year), annual frequency of material movements, and yearly transport performance. The analysis revealed that products must travel a long distance before being packed into the final product, and this distance is further extended by the transport of FG from the manufacturing company to distribution warehouses. Identified deficiencies included the long transport distances and high costs associated with FG transport, which amounted to EUR 262,952.18 per year. Additionally, significant costs were associated with transporting finished goods to distribution centers in České Budějovice and Tábor. Based on the identified deficiencies, two optimization scenarios for material flow were proposed Variant 1—Replacement of external transport for SF with in-house transport and Variant 2—Replacement of external transport for FG with in-house transport. Variant 1 focused on replacing external transport for SF with in-house transport. If the company adopts this variant, the investment costs amount to EUR 95,935.87, while the annual savings reach EUR 166,974.63. The ROI is 74%, and the investment is expected to pay off within 208 days. Variant 2 focuses on replacing external transport with FG with in-house transport. If the company chooses this option, the investment costs amount to EUR 201,710.96, with annual savings of EUR 17,548.60. The investment would take 11.5 years to pay off, making this variant unfeasible and leading to its rejection.

Based on the conducted analysis and proposed solutions, it is evident that material flow optimization is a crucial tool for increasing efficiency, reducing costs, and enhancing business competitiveness. Furthermore, systematic monitoring and evaluation of material flow contribute not only to operational efficiency but also to economic sustainability. This study provides a framework and practical recommendations that can be valuable not only for academia but also for industry professionals.