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Article

Conditions for Increasing the Level of Automation of Logistics Processes on the Example of Lithuanian Companies

by
Laima Naujokienė
1,
Valentina Peleckienė
2,
Kristina Vaičiūtė
3,* and
Rasa Pocevičienė
1
1
Faculty of Business and Technology, Šiauliai State University of Applied Sciences, Aušros Ave. 40, LT-76241 Šiauliai, Lithuania
2
Department of Management, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania
3
Transport Engineering Faculty, Vilnius Gediminas Technical University, Plytinės Str. 25, LT-10105 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Systems 2025, 13(7), 608; https://doi.org/10.3390/systems13070608
Submission received: 3 July 2025 / Accepted: 14 July 2025 / Published: 19 July 2025

Abstract

Globalization has greatly changed the way logistics firms function, improving speed, accuracy, and efficiency in everything from logistic management to warehousing. Robotics and automation technologies driven by artificial intelligence improve warehouse operations’ efficiency and adaptability, allowing warehouses to easily manage a variety of items, packaging kinds, and order profiles. Nevertheless, more research is still needed to fully comprehend how automation has affected logistics and how it has evolved. In addition, to date, no scholarly work has provided a thorough analysis of particular automated logistic process automation strategies used by Lithuanian businesses. Although many of the assessments that are currently available in this field offer valuable insights, they are frequently overly broad. In order to tackle this problem, we conducted a methodical study that attempts to offer a strong and pertinent basis, focusing on the automation of logistics processes that are used in supply chain management together with artificial intelligence. This study’s objective was to examine conditions for increasing logistics automation processes in Lithuanian logistic companies. The novelty of this article is the consideration of the main factors influencing the automation of logistics processes, which include the key drivers of AI-powered warehouse automation processes to evaluate the real level of automation.

1. Introduction

In this article, a methodical study was conducted that attempted to offer a strong and pertinent basis, focusing on the automation of logistical procedures that are most commonly used in supply chain management in Lithuania. This study highlighted three key supply chain management technologies: automated guided vehicles, robotic arms, and autonomous mobile robots. More research is still needed to fully comprehend how automation has affected logistics and how it has evolved. According to a previous assessment [1], robotics and artificial intelligence (AI) have the potential to increase the operational efficiency of logistics while reducing errors, facilitating quicker and more reliable product delivery to customers. In addition to providing cloud solutions for tracking and monitoring these products, such logistical advancements would help manufacturers by making it simpler, faster, and potentially less expensive to transfer their goods to other markets [2]. The aim of this article was to evaluate the conditions for increasing the level of automation in logistics processes using the example of Lithuanian companies. Over the past century, warehouse automation has experienced a remarkable transformation driven by the goal of operational efficiency, shifting consumer needs, and technology advancements. The invention of mechanical conveyor systems and forklifts in the early 20th century, which transformed material handling and storage procedures in warehouses, is where warehouse automation originated [3].
With the introduction of barcode scanning technology and automated storage and retrieval systems (AS/RSs) in the mid-1900s, the development of computer technology opened the door for additional warehouse automation. These developments laid the groundwork for contemporary warehouse automation systems by enabling warehouses to enhance inventory management, maximize space usage, and boost throughput. More complex warehouse automation solutions, such as automated guided vehicles (AGVs), robotic palletizers, and conveyor systems with built-in sorting capabilities, were developed in the late 20th century as a result of developments in robotics, sensors, and control systems. Warehouses were able to increase automation, save on labor expenses, and boost operational efficiency thanks to these innovations.
This scientific study was conducted in four stages: (1) determination of the criteria; (2) selection of a multi-criteria method; (3) determination of the significance of the selected criteria; and (4) results.
The structure of this article comprises explaining the development of warehouse automation processes and the application of artificial intelligence, the analysis of key AI drivers and obstacles in AI-powered warehouse automation, and review of the Lithuanian logistics sector.

2. Development of Warehouse Automation Processes and Application of Artificial Intelligence

Warehouse automation has been significantly impacted by the emergence of Industry 4.0 technologies, which are defined by the application of digital technologies into manufacturing and logistical activities. Big data analytics, cloud computing, and the Internet of Things (IoT) are examples of Industry 4.0 technologies that have made warehouses more intelligent, networked, and adaptable to shifting market conditions [4,5]. The next stage of warehouse automation process development is the use of artificial intelligence (AI), which allows warehouses to become more autonomous, efficient, and flexible. Warehouses can automate complicated activities, make data-driven choices, and optimize operations in real time thanks to AI technologies including machine learning, computer vision, and natural language processing. Warehouses can estimate demand, improve inventory levels, and automate replenishment procedures by using machine learning algorithms to examine past data and find patterns and trends. Over time, these algorithms’ accuracy and efficacy increase as they continuously learn from fresh data. Warehouses can identify products, automate visual inspection chores, and precisely track inventory movements with the help of computer vision systems [6]. Robotics and automation technologies driven by AI improve warehouse operations’ efficiency and adaptability, allowing warehouses to easily manage a variety of items, packaging types, and order profiles. Through chatbots, virtual assistants, and voice-enabled interfaces, natural language processing technologies allow warehouses to communicate with employees, clients, and suppliers. In warehouse operations, these technologies promote teamwork, expedite communication, and improve the user experience in general.
With its advanced tools and algorithms to optimize inventory levels, streamline operations, and boost overall productivity, AI is playing a crucial role in transforming inventory management methods in warehouses [7]. Demand forecasting is one of the main uses of AI in inventory management. AI systems can precisely forecast future product demand by examining past sales data, industry trends, and outside variables like weather and economic indicators. With the help of these projections, warehouses may proactively modify inventory levels, eliminate stockouts, and streamline replenishment cycles, all of which enhance customer satisfaction and lower carrying costs.

3. Key AI Drivers in AI-Powered Warehouse Automation Processes

As the importance of e-commerce grows, supply chain responses are also changing in the form of solutions used in modern warehouses. With the tremendous growth of smartphones and smart gadgets, the retail business has undergone a disruptive change. Here, automation helps satisfy the needs of “e-retailers”, or internet retailers. In order to lower shipping costs and maintain their competitiveness in the online market, online retailers are currently implementing various types of automation in their warehouses to enhance product mobility for effective order fulfillment and storage and to lower the number of defective return pickups. Warehouse automation is now based on the application of robotics and other IT-supported technologies. Process automation and physical automation are the two main categories of warehouse automation. Process automation, sometimes referred to as system automation, is the digitization of manual procedures such as gathering inventory data and integrating that data with the company’s database or Enterprise Resourse Planning (ERP) system. The use of wireless barcode scanners and the barcoding ecosystem, which are utilized for tracking and entering item data, are examples of process automation. This information is subsequently disseminated and stored in the organization’s central database, where it can be utilized in all functional domains, including marketing, logistics, and manufacturing. Any type of mechanized technology or automation-related machinery is considered physical automation. It includes the employment of robotic devices and robots in the warehouse. When compared to process automation, the technologies employed in physical automation are far more costly to implement. Physical automation technologies include automated guided vehicles (AGVs), goods-to-person (G2P) technology, and autonomous mobile robots (AMRs). One aspect of process automation is barcode labeling. The use of printed paper, specialized scanners, and IT-based applications constitute the most fundamental level of automation. Barcode labeling is the least expensive and most straightforward of the several warehouse automation systems. After being barcode-labeled, the products are traced at several supply chain locations using scanners. Use of these labels facilitates accurate data and product entry into the system, which is used for warehouse management in particular [8]. According to research, “59% of IT and operations personnel in manufacturing, retail, transportation, and wholesale market segments planned to expand process automation between 2017 and 2022” [9]. Warehouse automation is the use of different IT-based technologies that make warehouse labor much more efficient and rapid so that more can be achieved with considerably less work. Every day, stores have to deal with more and more package deliveries and internet orders. E-retailers need automation to help them handle a lot of orders at cheap prices. Without automation, it is hard to make sure orders are delivered on time.
Thus, e-commerce companies can meet high logistics demands without incurring large maintenance costs by implementing automation in warehouses and distribution [8]. E-retailers have a variety of technological choices to select from when it comes to warehouse management. Globally, e-commerce is expanding quickly, which is drastically altering the retail environment. Due to the evolving demands of their customers, manufacturers, distributors, retailers, and logistics service providers must adapt their backend fulfillment and warehousing operations in order to fulfill consumer orders. In addition to being a backend function, the warehouse is essential to supply chain management. Any problems with a warehouse’s operations might cause delays and have an effect on the company’s cash flow and customer satisfaction. Effective warehouse management can boost productivity, boost performance, and aid in the expansion of the business. Automation is therefore useful for enhancing warehouse management.
“There was not enough technology to perform the tasks of carrying and handling a wide array of various shapes when logistic and e-commerce companies first considered using robots to carry out their business practices” [10]. E-commerce companies can profit from warehouse automation in a number of ways, including meeting faster fulfillment expectations and delivering orders quickly and nimbly to cut down on avoidable mistakes and operating expenses; cut down on overhead costs; lower the expenses for personnel, equipment, maintenance, and safety; and lower the expenses for energy use and storage space.
Process automation and physical automation are the two main categories of warehouse automation. Process automation, sometimes referred to as system automation, is the digitization of manual procedures such as gathering inventory data and integrating that data with the company’s database or ERP system. Process automation includes the use of wireless barcode scanners and the barcoding ecosystem, which are used to track and input item data [11]. This information is subsequently disseminated and stored in the organization’s central database, where it can be utilized in all functional domains, including marketing, logistics, and manufacturing.
Any type of mechanized technology or automation-related machinery is considered physical automation. It includes the employment of robotic devices and robots in the warehouse. When compared to process automation, the technologies employed in physical automation are far more costly to implement. Physical automation technologies include automated guided vehicles (AGVs), goods-to-person (G2P) technology, and autonomous mobile robots (AMRs). A component of process automation is barcode labeling [11]. It is the simplest degree of automation and includes the use of IT-based programs, certain scanners, and printed paper. Barcode labeling is the least expensive and most straightforward of the several warehouse automation systems. After being barcode-labeled, the products are traced at several supply chain locations using scanners.
The most recent trends use state-of-the-art technologies to enhance decision-making, streamline procedures, and adjust to changing warehouse conditions.
Edge computing reduces the requirement for data transfer to centralized servers by bringing processing power closer to the location of data production. Without requiring a continuous connection to the cloud, edge computing devices mounted on machinery like robots and sensors can process data locally in warehouse systems. This lowers data processing latency, allowing for quicker reaction times and increasing warehouse operations’ overall effectiveness [12]. Warehouse systems can make choices in real time using the most recent data from sensors, cameras, and other IoT devices by processing data locally at the edge [13].

4. Review of the Logistics Industry of Lithuania

Lithuania’s economy expanded quickly after joining the EU, which benefited the country’s logistics industry. Connecting the markets of Western Europe and Scandinavia with Russia and other CIS nations, Lithuania is a part of Europe’s transportation corridors. The Schengen area borders Poland and Latvia, facilitating the free movement of goods and people. Although the Port of Klaipėda is one of the few significant ice-free ports in the area and has grown to be a well-liked cruise port in the Baltic Sea, transportation is primarily handled by train and road. Lithuania also has four international airports. A sizeable portion of the nation’s GDP is generated by the steady and expanding transportation sector [14]. The Lithuanian market has grown by 2.9% annually since 2000. Lithuania’s transportation revenue in 2023 ranked 13th in the world, although Slovakia’s revenue was higher. In this area, the top three nations were France, Italy, and Spain, who came in second, third, and fourth, respectively [15]. According to the State Data Agency, the Lithuanian logistics industry saw a 10.3% rise in freight traffic in 2024 over the year before. When oil pipeline transportation is excluded, the amount of freight transported rose by 11% to 181.6 million tons. Road transport accounted for 75.9% of the total volume, providing the majority of transportation. Tractor-based transportation rose 14.5%, and 137.9 million tons of freight were handled. This suggests that road transportation is becoming more and more important to the nation’s logistics network. At the same time, there was a drop in rail transportation, with 25.7 million tons of freight handled, a 5.8% fall. Changes in logistics processes and the redirection of some freight to alternative modes of transportation could be the cause of this. The biggest percentage of services imported and exported (44.9% and 44.0%, respectively) were transportation services [16]. The balance of road transport services had the biggest surplus (EUR 1.2 billion) during the reviewed period, while the balance of sea freight transport services had the greatest deficit (EUR 129.4 million).
Service exports to EU Member States increased by 8.2% year over year in the fourth quarter of 2024 and made up 73.3% of all service exports, a decrease of 2.1 percentage points across the entire service export structure. The largest portion of all service exports to Germany was made up of transportation services (62.2%) and other business services (10.7%) [16]. A number of significant occurrences and developments occurred in Lithuania’s logistics industry during the first half of 2024 [17]:
  • Growth in Road Freight Transport: Lithuanian road freight carriers saw an increase in cargo volumes in spite of economic difficulties. The first half of the year saw the transportation of almost 35 million tons of goods, demonstrating steady sector development.
  • Infrastructure Development: In order to enhance logistics flows and shorten travel times, road and rail infrastructure development projects persisted. The “Rail Baltica” project, which intends to link the Baltic States with Western Europe, received particular interest.
  • Technology Integration: In an effort to boost productivity and cut expenses, numerous logistics firms started putting new technologies into place, such as automated warehouses and intelligent transport management systems.
  • Effect of EU Sanctions: The logistics industry was significantly impacted by the EU sanctions imposed on Russia and Belarus, which complicated international transportation and decreased the flow of commodities. As a result, businesses had to look for new markets and routes.
  • Labor Market Issues: There was a labor scarcity in the transportation and logistics industry, particularly for skilled drivers and logistics experts. This prompted businesses to enhance working conditions and make training investments for their staff.
  • Sustainability Initiatives: To cut carbon emissions and switch to greener transportation options, several businesses began putting sustainability initiatives into place. This includes using alternate fuels and electric trucks.
These elements suggest that Lithuania’s logistics industry is still expanding and adjusting to shifting consumer demands and technology advancements. The logistics industry in Lithuania is actively looking for methods to lessen its impact on the environment and support sustainability objectives.

5. Research Methodology

Due to the changing market and customer needs, logistics service providers are adapting to technological changes that affect the quality of service and the productivity of the company. In order to achieve the automation of logistics processes, a study is required, which will be conducted in stages. In the first stage, preparations are made for the study by choosing a method. In the second stage, a survey and data processing are carried out. In the third stage, the results are summarized. The expert assessment provides an opportunity to process statistical material, assess the dependence of one criterion on another, and show their interaction [18]. The automation of logistics processes is important for Lithuanian transport companies; therefore, one applied method does not fully reflect the results of the research. The arithmetic, geometric mean, weighted sums (SAW—Simple Additive Weighting), and expert ranking data evaluation and processing methods were selected for this study. SAW was chosen because Çalık [19] indicated that when comparing multi-criteria methods, the SAW method allows for a more accurate assessment of the results. The concordance coefficient is used in the assessment of multi-criteria methods. If the value of the concordance coefficient is close to unity, then the expert assessments are consistent and the concordance of the expert assessments is considered sufficient. If the assessments differ significantly and the value is close to zero, then it is appropriate to conduct a re-assessment.
The geometric mean is calculated according to Formula (1).
G j = w j j = 1 n x i j n ,
where G j —geometric mean, x i j —expert assessment of the relevant alternative,   w j —alternative coefficient, n—number of alternatives, j—alternative sequence number, and i—expert serial number. The geometric mean is used to calculate variables that are presented as percentages or indices. Compared to other calculation methods, its advantage is that it is less sensitive to extreme values. But, the disadvantage is that when using calculations with larger numbers, the geometric mean is less weighted than, for example, the arithmetic mean.
The arithmetic mean method is performed by selecting the weights w for each criterion and calculating them according to Formula (2).
A j = w j j = 1 n x i j n ,
where A j —arithmetic mean, w j —alternative coefficient, n—number of alternatives,   x i j —expert assessment of the relevant alternative, j—alternative sequence number, and i—expert serial number.
The estimates of the weighted sum method are calculated by summing the normalized estimates of each alternative and multiplying by the weight of that alternative, or equal weights can be chosen using Formula (3) ( w = 1 n , where n is the number of criteria).
S j = j = 1 n w j x i j ,
where S j —weighted sum, x i j —expert assessment of the relevant alternative, w j —alternative coefficient, n—number of alternatives, j—alternative sequence number, and i—expert serial number.
Multicriteria analysis methods are used to predict decisions. Such methods provide an opportunity to classify indicators according to expert opinion [20]. The perception and assessment of criteria by different experts differ, which means that the weights of the obtained criteria and their priorities may differ [21]. In order to ensure the quality and reliability of expert research, an expert group is formed, which consists of more than 2 but no more than 10 experts [22,23].
During the evaluation of expert ranking data, a table is created in which a group of experts n quantitatively evaluates objects m. Based on the multi-criteria evaluation method, the evaluations form a matrix of n rows and m columns and are presented in a table [24]. The evaluation can be performed in units of indicators, fractions of units, percentages, or decimals. Expert ranking of indicators is suitable for calculating the concordance coefficient. According to Podvezko [25], the average of the sums of ranks is calculated as follows:
i = 1 n R i j 1 2 n m + 1 ,
where RijR rank, m—number of benchmarks, and n—number of experts. Concordance coefficient W is calculated as follows:
W = 12 S n 2 m m 2 1 = 12 S n 2 m 3 m ,
where S—sum of squares of the arithmetic mean, n—number of experts, and m—number of benchmarks.
The application of selected methods allows for easy comparison of selected criteria.
In order to obtain more accurate reliability of the result, it is possible to compare and correlate the results obtained by different methods. Therefore, polynomial methods for evaluating expert rating data were chosen. Polynomial choice models are used when there are three or more choice options. Regression analysis is used as a tool to study the relationship between two variables [26]. The closer R2 is to 1, the stronger the correlation between the variables under study.

6. Results and Discussion

This study was conducted in 2025 from April to May. Eight experts working in the field of logistics and teaching logistics processes participated in this study. All experts have a higher university education. The experts were given two questionnaires: in one, the evaluation criteria were written down and processed using the arithmetic, geometric mean, and SAW methods; in the other, the experts were asked to rank the criteria, and the ranking of expert indicators was applied for processing.
Using the geometric mean, arithmetic mean, and weighted sum (SAW—Simple Additive Weighting) methods, experts assessed the problem area separately in the questionnaire, indicating its importance from 1 (not at all important) to 8 (very important).
  • RVB1—Speeds up order fulfillment;
  • RVB2—Speeds up and makes delivery more flexible;
  • RVB3—Reduces errors;
  • RVB4—Reduces operating costs;
  • RVB5—Reduces overhead costs;
  • RVB6—Reduces the need for personnel;
  • RVB7—Reduces storage space costs;
  • RVB8—Reduces energy use.
Normalization was performed on the sum of the total estimates, but this did not affect the final result. The results are presented in Table 1.
The use of advanced technologies in logistics processes improves decision-making and simplifies logistics procedures.
The analysis of the results of the evaluation of the automation criteria of the geometric (GV), arithmetic (AV), and weighted sum (SAW) methods is presented in Table 2.
Summarizing the results of the expert assessment, we can distinguish the most important criteria that influence logistics cooperation and service quality during automation: reduces energy consumption and speeds up order fulfillment.
Experts were asked to identify the missing management technology measures in logistics processes, indicating their importance from 1 (not at all important) to 7 (very important).
  • PVT1—Collection and transfer of inventory data to ERP systems;
  • PVT2—Data integration into company databases;
  • PVT3—Digitization;
  • PVT4—Wireless barcode scanner ecosystems;
  • PVT5—Automated guided vehicles (AGVs);
  • PVT6—Goods-to-person (G2P) technology;
  • PVT7—Autonomous mobile robots (AMRs).
The results obtained are presented in Table 3. Experts identified the missing automation tools in logistics processes, namely autonomous mobile robots (AMRs) and automated guided vehicles (AGVs).
The consistency of expert assessments, which affects the assessment results, is 0.168. The result shows that the opinions of experts are consistent.
Summarizing the expert assessments, it can be stated that automation in logistics processes is important and must be adapted to logistics processes and functions. In the second questionnaire, experts were asked to rank control technologies (automation) by indicating their importance from 1 (very important) to 8 (not at all important). That is, an attempt was made to present a different assessment prism than when applying the geometric mean, arithmetic, and weighted sum methods.
The distribution of expert ranks is presented in Figure 1.
The distribution of the obtained criteria ranking data is presented in Table 4.
Calculated (Equation (6)) concordance coefficient:
W = 12 S n 2 m 3 m = 12 · 2460 8 2 8 3 8 = 0.915 .
Automation (control technologies) is important in logistics collaboration processes. To obtain a random variable, the concordance coefficient is calculated (Equation (7)).
χ 2 = n m 1 W = 12 S n m m + 1 = 12 · 2460 8 · 8 8 + 1 = 51.25 .
The χ 2 value is 51.25 higher than the critical value (14.0671). The result shows that the experts’ opinions are in agreement, and the average ranks indicate the general opinion of the experts [27]. The minimum concordance value is calculated (Equation (8)).
W m i n = χ v , α 2 n m 1 = 14.0671 8 8 1 = 0.251198 < 0.915 .
The obtained result Wmin = 0.2511 < 0.915 shows that the experts’ opinions are consistent. The importance indicators Qj are calculated. The obtained results with the criteria and order of importance are presented in Table 5.
Based on expert assessments and data, important automation (control technology) criteria in logistics cooperation processes are presented in the following order:
  • RVB8—Reduces energy use;
  • RVB1—Speeds up order fulfillment;
  • RVB3—Reduces errors;
  • RVB2—Speeds up and makes delivery more flexible;
  • RVB4—Reduces operating costs;
  • RVB5—Reduces overhead costs;
  • RVB6—Reduces the need for personnel;
  • RVB7—Reduces storage space costs.
The impact of control technologies (automation) on logistical cooperation and quality of care is presented in Figure 2.
The estimates of the second-order polynomial criteria show (Figure 2) that the development of management technologies increases the quality of services. In this case, there is a strong correlation R2 = 0.9794.
Experts were asked to rank, indicating in order of importance from 1 (very important) to 7 (not at all important), the criteria of management technology tools missing in logistics processes, which are most lacking when it comes to improving logistical cooperation and service quality. The distribution of the obtained expert ranks is presented in Figure 3.
The distribution of the obtained criteria ranking data is presented in Table 6.
Calculated (Equation (9)) concordance coefficient:
W = 12 S n 2 m 3 m = 12 · 1620 8 2 7 3 7 = 0.904 .
Automation (control technologies) is important in logistics collaboration processes. To obtain a random variable, the concordance coefficient is calculated (Equation (10)).
χ 2 = n m 1 W = 12 S n m m + 1 = 12 · 1620 8 · 7 7 + 1 = 43.39 .
The χ 2 value is 43.39 higher than the critical value (12.5916). The result shows that the experts’ opinions are in agreement, and the average ranks indicate the general opinion of the experts [22]. The minimum concordance value is calculated (Equation (11)).
W m i n = χ v , α 2 n m 1 = 12.5916 8 7 1 = 0.262325 < 0.904
The obtained result Wmin = 0.2623 < 0.904 shows that the experts’ opinions are consistent. The importance indicators Qj are calculated. The obtained results with the criteria and order of importance are presented in Table 7.
Based on expert assessments and data, the criteria for the importance of the lack of management technologies in logistics cooperation processes are presented in the following order:
  • PVT7—Autonomous mobile robots (AMRs);
  • PVT5—Automated guided vehicles (AGVs);
  • PVT2—Data integration into company databases;
  • PVT6—Goods-to-person (G2P) technology;
  • PVT4—Wireless barcode scanner ecosystems;
  • PVT1—Collection and transfer of inventory data to ERP systems;
  • PVT3—Digitization.
The impact of the development of control technologies (automation) on the solution of problem areas is presented in Figure 4.
The estimates of the second-order polynomial criteria show (Figure 4) that the development of control technologies reduces the occurrence of problems in logistics processes.
In summary, it can be stated that the automation of logistics processes (development of control technologies) can be performed by applying autonomous mobile robots (AMRs) and automatically guided vehicles (AGVs) and by performing data integration into company databases. In this case, there is a strong correlation, with R2 = 0.9676.
The results of the evaluation of the criteria for management technology tools missing in logistics processes were obtained using different methods and their comparison is presented in Table 8.
Comparisons of the results obtained by different methods in Table 8 and the arrangement of criteria confirm that the same result is obtained when calculating using different methods. This means that the automation criteria important for Lithuanian companies in the RVB logistics cooperation processes are arranged in the same way. The most important is the automation criterion that reduces energy consumption (RVB8). A comparison of the results of missing management technological tools obtained by different methods is presented in Table 9.
The comparisons of the results of management technologies missing in logistics processes obtained by different methods in Table 9 and the placement of criteria confirm that the same result is obtained when calculating using different methods. This means that the missing management technology tools important for Lithuanian companies are ordered in the same way. The most important is the PVT7 criterion, which is autonomous mobile robots (AMRs), which companies lack when improving logistics cooperation and ensuring service quality.

7. Discussion and Limitations

Singh and Namekar [28] argued that when automating logistics processes, robots perform work more accurately than logistics specialists. In the article, research was performed in order to examine the conditions for increasing logistics automation processes in Lithuanian logistic companies. The responses of experts made it possible to evaluate the biggest obstacles of automation logistics processes in Lithuanian companies. The research results stated that the automation of logistics processes (development of control technologies) can be performed by applying autonomous mobile robots (AMRs); automatically guided vehicles (AGVs); and data integration into company databases.
When implementing AI in logistics processes, higher quality and faster execution of logistics operations are observed [29]. Automation in logistics processes has a significant impact on processing large data flows [30]. Although the findings of this study were unique and important, there were also some limitations. The limitation of this study is its generalization. This article has analyzed the information by collecting data from experts working in small and medium logistics and teaching logistics enterprises in Lithuania. Further research on the automation of logistical procedures with a bigger research sample and more detailed questions should be conducted among different countries’ enterprises to increase the generality of this study.

8. Conclusions

In recent years, the automation of logistic processes has experienced a remarkable transformation driven by the goal of operational efficiency, shifting consumer needs, and technology advancements.
As the importance of e-commerce grows, supply chain responses are also changing in the form of solutions used in modern warehouses. Warehouse automation has been significantly impacted by the emergence of Industry 4.0 technologies, which is defined by the use of digital technologies into manufacturing and logistical activities. Warehouse automation involves the use of various IT-based technologies that enable a warehouse to operate much more effectively and efficiently in order to achieve greater outcomes with significantly less efforts.
Lithuania’s logistics industry is still expanding and adjusting to shifting consumer demands and technology advancements. The logistics industry in Lithuania is actively looking for methods to lessen its impact on the environment and support sustainability objectives.
In this article, a methodical study was carried out in attempts to offer a strong and pertinent basis, focusing on the automation of logistical procedures that are used in supply chain management in Lithuania. This article has also analyzed information by collecting data from experts working in small and medium logistics and teaching logistics enterprises in Lithuania.
The novelty of this research is that experts have identified important criteria for determining the influence of automation processes (control technologies) that improve collaboration and service quality: reducing energy consumption (RVB8), accelerating order fulfillment (RVB1), and reducing errors (RVB3).
Also, experts have identified criteria for control technologies that are missing in logistics collaboration processes: autonomous mobile robots (AMRs) (PVT7), automatically guided vehicles (AGVs) (PVT5), and data integration into company databases (PVT2).
The research results showed that the automation of logistics processes (development of control technologies) can be conducted by integrating autonomous mobile robots (AMRs) and automatically guided vehicles (AGVs) and by performing data integration into company databases.
The comparisons of the research results obtained by different methods and the placement of criteria confirm that the same result is obtained when calculating using different methods. This means that the missing management technology tools important for Lithuanian companies are ordered in the same way. The most important is the PVT7 criterion, autonomous mobile robots (AMRs), which companies lack when improving logistics cooperation and ensuring service quality.
The implementation of these research results will help to practically increase the level of automation of logistics processes in Lithuanian companies.
It was found that the significances calculated by the different methods for the priorities of automation and missing control technologies corresponded to the same criteria. The most important are the energy-saving (RVB8) automation criterion and the PVT7 criterion, which is autonomous mobile robots (AMRs), which are missing for companies to improve logistics collaboration and ensure service quality.

Author Contributions

Conceptualization, L.N.,V.P., K.V. and R.P.; methodology, L.N. and K.V.; software, V.P., K.V. and R.P.; validation, V.P., K.V., R.P. and L.N.; formal analysis, V.P. and K.V.; investigation, L.N. and R.P.; resources, L.N., V.P., K.V. and R.P.; data curation, L.N., V.P. and K.V.; writing—original draft preparation, V.P. and K.V.; writing—review and editing, V.P. and K.V.; visualization, V.P., K.V., L.N. and R.P.; supervision, V.P. and K.V.; project administration, L.N. and K.V.; funding acquisition, L.N. and K.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AGVsautomated guided vehicles
AIartificial intelligence
AMRsautonomous mobile robots
ASautomated storage
G2Pgoods-to-person
IoTInternet of Things
RSretrieval systems
SAWSimple Additive Weighting
ERPenterprise resource planning

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Figure 1. Expert assessment of the impact of automation (control technologies) on logistics cooperation and service quality (developed by the authors).
Figure 1. Expert assessment of the impact of automation (control technologies) on logistics cooperation and service quality (developed by the authors).
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Figure 2. The influence of management technologies on logistics cooperation and service quality (developed by the authors).
Figure 2. The influence of management technologies on logistics cooperation and service quality (developed by the authors).
Systems 13 00608 g002
Figure 3. Expert assessment of the lack of automation (control technology) tools (developed by authors).
Figure 3. Expert assessment of the lack of automation (control technology) tools (developed by authors).
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Figure 4. The influence of the development of control technologies (automation) (developed by the authors).
Figure 4. The influence of the development of control technologies (automation) (developed by the authors).
Systems 13 00608 g004
Table 1. Importance of automation (robotics and autonomous vehicles) criteria for logistics collaboration and service quality (developed by the authors).
Table 1. Importance of automation (robotics and autonomous vehicles) criteria for logistics collaboration and service quality (developed by the authors).
ExpertE1E2E3E4E5E6E7E8
Criterion RankNormalized ValueRankNormalized ValueRankNormalized ValueRankNormalized ValueRankNormalized ValueRankNormalized ValueRankNormalized ValueRankNormalized Value
RVB170.19480.22250.13980.22280.22260.16770.19470.194
RVB250.13970.19470.19460.16760.16740.11150.13950.139
RVB360.16750.13960.16750.13950.13970.19460.16760.167
RVB440.11130.08340.11130.08330.08350.13940.11140.111
RVB530.08340.11130.08340.11140.11130.08320.05630.083
RVB620.05610.02820.05610.02810.02820.05630.08320.056
RVB710.02820.05610.02820.05620.05610.02810.02810.028
RVB880.22260.16780.22270.19470.19480.22280.22280.222
Table 2. Importance of automation by method (developed by the authors).
Table 2. Importance of automation by method (developed by the authors).
CriterionGeometric MeanLocation (GV)Arithmetic MeanLocation (AV)SAW
Coefficient
Expert Appraisal AmountSAWSAW Location
RVB16.9222720.1945610.8892
RVB25.53645.62540.156457.0314
RVB35.71335.7530.160467.3473
RVB43.69353.7550.104303.1255
RVB53.17763.2560.090262.3476
RVB61.62271.7570.049140.6817
RVB71.29781.37580.038110.4208
RVB87.46417.510.2086012.5001
Table 3. Ranking results of missing automation tools in logistics processes (developed by the authors).
Table 3. Ranking results of missing automation tools in logistics processes (developed by the authors).
CriterionGeometric MeanLocation (GV)Arithmetic MeanLocation (AV)SAW CoefficientExpert Appraisal AmountSAWSAW Location
PVT11.70761.87560.067151.0046
PVT24.59934.62530.165376.1123
PVT31.54271.62570.058130.7547
PVT42.28052.550.089201.7865
PVT56.09526.12520.2194910.7192
PVT64.44944.540.161365.7864
PVT76.73516.7510.2415413.0181
Table 4. Ranking results (developed by the authors).
Table 4. Ranking results (developed by the authors).
FormulaRVB1RVB2RVB3RVB4RVB5RVB6RVB7RVB8
i = 1 n R i j 1628264245596111
R ¯ j = i = 1 n R i j n 23.53.255.255.6257.3757.6251.375
i = 1 n R i j 1 2 n m + 1 −20−8−10692325−25
[ i = 1 n R i j 1 2 n m + 1 ] 2 400641003681529625625
Table 5. Indicators of importance of the control technology (automation) criterion (prepared by the authors).
Table 5. Indicators of importance of the control technology (automation) criterion (prepared by the authors).
IndicatorCriterion (m = 8)
RVB1RVB2RVB3RVB4RVB5RVB6RVB7RVB8
qj0.0560.0970.0900.1460.1560.2050.2120.038
dj0.9440.9030.9100.8540.8440.7950.7880.962
Qj0.1350.1290.1300.1220.1210.1140.1130.137
Qj0.1940.1530.1600.1040.0940.0450.0380.212
In order of importance24356781
Table 6. Distribution of the obtained data (developed by the authors).
Table 6. Distribution of the obtained data (developed by the authors).
FormulaPVT1PVT2PVT3PVT4PVT5PVT6PVT7
i = 1 n R i j 4927514416289
R ¯ j = i = 1 n R i j n 6.1253.3756.3755.523.51.125
i = 1 n R i j 1 2 n m + 1 17−51912−16−4−23
i = 1 n R i j 1 2 n m + 1 2 2892536114425616529
Table 7. Criteria importance indicators (developed by the authors).
Table 7. Criteria importance indicators (developed by the authors).
IndicatorCriterion (m = 7)
PVT1PVT2PVT3PVT4PVT5PVT6PVT7
qj0.21880.12050.22770.19640.07140.12500.0402
dj0.78130.87950.77230.80360.92860.87500.9598
Qj0.13020.14660.12870.13390.15480.14580.1600
Qj0.06700.16520.05800.08930.21430.16070.2455
In order of importance6375241
Table 8. Comparison of the importance of RVB criteria between methods (developed by the authors).
Table 8. Comparison of the importance of RVB criteria between methods (developed by the authors).
CriterionGeometric Mean Location (GV)Arithmetic Mean Location (AV)The Expert Assessment LocationSAW Location
RVB12222
RVB24444
RVB33333
RVB45555
RVB56666
RVB67777
RVB78888
RVB81111
Table 9. Comparison of the importance of PVT criteria between methods (developed by the authors).
Table 9. Comparison of the importance of PVT criteria between methods (developed by the authors).
CriterionGeometric Mean Location (GV)Arithmetic Mean Location (AV)The Expert Assessment LocationSAW Location
PVT16666
PVT23333
PTV37777
PTV45555
PTV52222
PTV64444
PTV71111
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Naujokienė, L.; Peleckienė, V.; Vaičiūtė, K.; Pocevičienė, R. Conditions for Increasing the Level of Automation of Logistics Processes on the Example of Lithuanian Companies. Systems 2025, 13, 608. https://doi.org/10.3390/systems13070608

AMA Style

Naujokienė L, Peleckienė V, Vaičiūtė K, Pocevičienė R. Conditions for Increasing the Level of Automation of Logistics Processes on the Example of Lithuanian Companies. Systems. 2025; 13(7):608. https://doi.org/10.3390/systems13070608

Chicago/Turabian Style

Naujokienė, Laima, Valentina Peleckienė, Kristina Vaičiūtė, and Rasa Pocevičienė. 2025. "Conditions for Increasing the Level of Automation of Logistics Processes on the Example of Lithuanian Companies" Systems 13, no. 7: 608. https://doi.org/10.3390/systems13070608

APA Style

Naujokienė, L., Peleckienė, V., Vaičiūtė, K., & Pocevičienė, R. (2025). Conditions for Increasing the Level of Automation of Logistics Processes on the Example of Lithuanian Companies. Systems, 13(7), 608. https://doi.org/10.3390/systems13070608

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