Production companies: Evaluation of accessibility and efficiency of transportation and manufacturing processes

Smirnova, Elena; Hajiyev, Nazim; Glazkova, Irina; Hajiyeva, Aytaj Article Production companies: Evaluation of accessibility and efficiency of transportation and manufacturing processes Asian Journal of Shipping and Logistics (AJSL) Provided in Cooperation with: Korean Association of Shipping and Logistics, Seoul Suggested Citation: Smirnova, Elena; Hajiyev, Nazim; Glazkova, Irina; Hajiyeva, Aytaj (2024) : Production companies: Evaluation of accessibility and efficiency of transportation and manufacturing processes, Asian Journal of Shipping and Logistics (AJSL), ISSN 2352-4871, Elsevier, Amsterdam, Vol. 40, Iss. 1, pp. 52-60, https://doi.org/10.1016/j.ajsl.2024.01.002 This Version is available at: https://hdl.handle.net/10419/329734 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ The Asian Journal of Shipping and Logistics 40 (2024) 52–60 Available online 18 January 2024 2092-5212/Production and hosting by Elsevier B.V. on behalf of The Korean Association of Shipping and Logistics, Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Production companies: Evaluation of accessibility and efficiency of transportation and manufacturing processes Elena Smirnova a , * , Nazim Hajiyev b , Irina Glazkova c , Aytaj Hajiyeva d a Department of Taxes and Tax Administration, Financial University under the Government of the Russian Federation, Kholmogorskaya Str., 2-3-71, 129347 Moscow, Russian Federation b Department of Business Administration, Azerbaijan State University of Economics (UNEC), 6 Istiglaliyyat, Baku AZ1001, Azerbaijan c Department of Pharmacy, Sechenov First Moscow State Medical University, 8-2, Trubetskaya Str., 119991 Moscow, Russian Federation d Department of Business and Logistics, Azerbaijan State University of Economics (UNEC), 6 Istiglaliyyat, AZ1001 Baku, Azerbaijan ARTICLE INFO Keywords: Cluster analysis Efficiency Enterprise Supply chain Tax Transport logistics performance ABSTRACT Logistics and transport-production processes are critical components in the management of supply chains for enterprises. Increasing demands for the accessibility and efficiency of these processes necessitate further refinement of logistics theory and practice. The primary objective of this research was to establish the interrelationship between the structure and functioning of transport operations within manufacturing enterprises. The application of cluster analysis methods revealed the unique structure of each cluster, reflecting the specificity of transport operations. The obtained results determine the stability of the logistic cluster for a specific type of transport and its impact on overall efficiency. The research also emphasizes the importance of improving the quality of transport-production processes to ensure efficiency in supply chains. Quality and logistics service analysis identified factors that can be enhanced, such as service complexity, responsiveness to unforeseen orders, professionalism of the personnel, and fulfillment of order-related commitments. The conclusions drawn contribute to the development of logistics science and theory, providing practical insights by urging manufacturing companies to implement recommendations for optimizing their transport-production processes and selecting the most effective partners in this context. Thus, the originality of the article lies in expanding theoretical concepts of logistics and defining the specifics of transport-production processes. 1. Introduction Due to rapid economic development and specifics of modern international relations, the focus of economic activity of production companies exclusively on internal efficiency becomes irrelevant (Cui et al., 2018). Instability in the stock market and high competition in the business sphere adjusts the work of production companies with clear organizational boundaries and restrictions in relations with other members of the supply chain (Barkemeyer & Miklian, 2019). Improvement of the transportation support management of production companies provides for the improvement of economic analysis to the use of rolling stock for more effective management decisions (Dias & Ierapetritou, 2017). The efficiency of production companies also depends directly on the efficiency of logistics and its relation to the products manufactured, information, and cash flows (Lai & Cheng, 2003). The essence of the transportation system is to determine the physical flows of products, and raw materials, their efficiency and reliability in supply chains, as well as to generate costs associated with storage (Ertogral & ¨ Oztürk, 2019). However, logistics allow saving for money by choosing the right transport mode or route. Transport must be organized so that the products are delivered to the recipient in the shortest possible time. The development of logistics and understanding of the integration between suppliers and recipients has reduced the waiting period for goods and thus contributed to a comprehensive exchange of products in the economy. There are also mixed interpretations of the supply chain without taking into account financial flows (Borgatti & Li, 2009). Thus, the supply chain can be referred to as all organizations and activities that accompany the flow and transformation of products from the stage of receiving raw materials to the arrival of the finished product to the end-user, considering the * Corresponding author. E-mail addresses: [email protected] (E. Smirnova), [email protected], [email protected] (N. Hajiyev), [email protected] (I. Glazkova), [email protected], [email protected] (A. Hajiyeva). Contents lists available at ScienceDirect The Asian Journal of Shipping and Logistics journal homepage: www.elsevier.com/locate/ajsl https://doi.org/10.1016/j.ajsl.2024.01.002 Received 8 June 2023; Received in revised form 30 November 2023; Accepted 9 January 2024 The Asian Journal of Shipping and Logistics 40 (2024) 52–60 53 associated information flows. The authors point out that both materials and information move up and down the supply chain. However, in each of these contexts, the supply chain is a broader concept than the logistic chain (Zhu et al., 2008). The latter implies a warehouse and vehicles and also takes into account the technological connection of transport routes with points of storage and transshipment of goods (Luburi´ c & Bortas, 2020). The decisions and actions taken may also concern the organizational, financial, and legal coordination of operations, processes, orders, and work of the warehouses in all areas of this chain (Hussain, 2019; Karpuntsov & Veresha, 2022; Moons et al., 2019). Regardless of differences, there is general agreement on the key importance of supply chain integration factors, i.e., standardization, solution unification, information transparency, information technology, management strategy and concepts, confidence, partnership, inventory centralization, inventory co-management, co-planning, and product design (Ghandour et al., 2021; Yao & Askin, 2019). At the same time, each link in the chain has its position in the effort to achieve the objective and share the benefits of synergy cooperation (Cao & Zhang, 2011). In the context of sustainable development and corporate social responsibility, ‘green supply’ is widely used in manufacturing enterprises (García-Arca et al., 2017). Supply chains in the context of sustainable development include the use of clean resources and their transformation in such a way that their side properties can be improved or recycled in the existing environment without damaging it. The goal of a sustainable network is the creation, protection, and long-term development of environmental, social, and economic value when delivering products and services to the market (Evans et al., 2017). In the rapidly evolving landscape of escalating global supply volumes and the imperative for prompt responses to market challenges, modern manufacturing companies are compelled to navigate the necessity of maximizing the efficiency of their logistics and transportation processes. The significance of this task is underscored not only by the competitive environment but also by the continual transformations within global supply chains. The foundations of this research lie in the recognition that the accessibility and efficiency of transportation and manufacturing processes are crucial for the successful operation of manufacturing enterprises. However, up to this point, there exists a gap in scholarly investigations that would encompass a comprehensive evaluation of these aspects, particularly in the context of enhancing supply chain efficiency and supporting the competitiveness of manufacturing enterprises. This research aims to conduct a systematic analysis and assessment of the quality of accessibility and efficiency in the transportation and manufacturing processes of manufacturing enterprises. This endeavor seeks to identify factors influencing their efficiency and develop strategies for enhancing logistical and manufacturing practices. The research endeavors to bridge both scientific and practical gaps, contributing to the understanding of the interaction between transportation and manufacturing processes to achieve optimal efficiency in industrial supply chains. Thus, the article elucidates the relevance of the research, and the necessity to address specific issues, and delineates its targeted direction for resolving crucial tasks in the field of logistics and manufacturing. 2. Literature review 2.1. Exploring sustainable supply chains and logistics efficiency A comparative analysis of the concept of sustainable supply chains points to many common areas, especially the convergence of objectives and the means to achieve them in terms of respect for the environment and wide public recognition. The authors believe that there is an analogy in the relation between the level of customer service and company profits in the context of the low volatility of the transportation and production environment (Lewis & Talalayevsky, 1997). The concepts of reverse logistics, recovery, utilization, and recycling are narrower concepts as they only concern the waste flow. The terms ‘reverse supply chain’, ‘after-sales logistics’, ‘retrogistics’, and ‘after-sales supply chain’ now also exist. Reverse logistics aims to maximize waste extraction and use environmentally friendly forms of disposal. The current direction in the economy of enterprises is to increase performance by dividing it into logistics processes. The classification is designed to serve enterprises in building a program to increase the efficiency of transportation and manufacturing processes. Thus, the classification includes an indication of when to use which tool or method to improve the selected process. The concepts, methods, and tools presented serve to improve many aspects of the enterprise. The classification is intended to reduce the time required for companies to develop improvement solutions. The efficiency of transport and production processes understood as the ratio of the number of products manufactured and sold in a certain period to the resources consumed or used in that period, can also refer to the logistic processes. The authors propose many indicators to determine the efficiency of logistic processes (Barilla et al., 2020). Their classification can be found in works (Hu et al., 2019; Stevi´ c & Brkovi´ c, 2020). Some of these indicators consider the number of logistic operations performed during the production process instead of production itself. In this case, logistic processes are analyzed as services when evaluating efficiency. Therefore, some indicators do not directly express the performance level but reflect it well. There are many methods to increase the efficiency and performance output of the enterprise. Enterprises do not always realize the possibility of improving many operational aspects. Appropriate classification of such methods is missing in the literature, so the authors have tried to develop a classification that would include concepts, methods, and tools to improve the efficiency of logistics processes. This classification is used to implement its efficiency testing methodology for logistics processes. The prospects of creating a conceptual model to improve the efficiency of transportation and manufacturing processes are discussed by many authors (Abuova et al., 2019; Levi¨ akangas & ¨ O¨ orni, 2020). One aspect involves the utilization of reverse logistics means to maximize waste recovery and the application of environmentally friendly disposal methods. This typically pertains to waste circulation, resource restoration, and the reduction of manufacturing impacts on the environment. Reverse logistics and the after-sales supply chain are also gaining attention, underscoring the importance of resource conservation and meeting the demands of the contemporary market. Additionally, in the context of enhancing enterprise productivity, it is crucial to consider logistic processes as a key instrument. The classification developed by the authors provides enterprises with solutions for constructing programs to improve the efficiency of transportation and manufacturing processes. This may encompass route optimization, the selection of transportation means, and the utilization of other tools to enhance specific aspects of production and supply (Zhu et al., 2008). Furthermore, it is important to consider that the efficiency of transportation and manufacturing processes can also be defined as the effectiveness of logistic processes, where numerous indicators play a crucial role. Some of these indicators may analyze logistic operations, viewing them as services when assessing efficiency. Moreover, many enterprises may not be cognizant of the opportunities to enhance their operational aspects; thus, it is crucial to develop a classification of concepts, methods, and tools to improve the efficiency of logistic processes. This classification is utilized for implementing a methodology for assessing the efficiency of logistic processes, which can aid enterprises in identifying optimal pathways for optimizing their logistic operations. Considering these aspects, it becomes evident that the issues of resilient supply chains and the efficiency of logistic processes are closely intertwined, and their integration can emerge as a pivotal factor in achieving sustainable development for enterprises in the contemporary economic environment. E. Smirnova et al. The Asian Journal of Shipping and Logistics 40 (2024) 52–60 54 2.2. Enhancing transportation and production processes: Technologies and strategies for efficiency In his work, Utku (2023) notes that modelling transportation flows and other aspects allows for a detailed analysis and optimization of various elements within logistic processes. The use of models facilitates the identification of potential bottlenecks and the determination of optimal strategies. Supporting this notion, Cavalcanti et al. (2022) indicate that the application of optimization algorithms can assist in identifying the best delivery routes, thereby enhancing the utilization of transportation resources and reducing delivery time. This is crucial for the efficiency of logistic systems. The collection and analysis of production data also play a crucial role in identifying potential issues in manufacturing processes. A detailed examination of equipment productivity, resource utilization, and other parameters allows for the precise identification of areas for further improvement (Erceg & Mularifovi´ c, 2019; Nurmi et al., 2019). According to Cavagnini et al. (2020), the establishment of key performance indicators (KPIs) aids in objectively assessing the performance of transportation and manufacturing processes. This becomes a significant step in the development of strategies for enhancing productivity. According to Bueno et al. (2020), conducting audits of manufacturing and transportation processes enables a systematic exploration, identification of potential shortcomings, and the development of recommendations for further improvements. The use of Internet of Things (IoT) technologies in the logistics domain allows for the collection of real-time data and the automation of processes, facilitating efficient tracking and management of transportation (Bendul & Blunck, 2019). Considering these aspects, the assessment of the accessibility and efficiency of transportation and manufacturing processes becomes a key task for achieving optimal productivity and competitiveness (Kim, 2021). In this regard, the utilization of automated systems can contribute to operational efficiency improvements and error avoidance (Kim, 2021). According to Kuryło et al. (2022), considering environmental aspects in transportation and production processes, such as reducing emissions of harmful substances and utilizing eco-friendly technologies and renewable energy sources, contributes to mitigating the negative impact on the environment and ensures the sustainability of production processes (Kuryło et al., 2022). Modibbo et al. (2021) assert that process evaluation helps identify problem areas and deficiencies that limit productivity. This enables the implementation of measures to address them and enhance overall enterprise productivity. Furthermore, analyzing transportation and production processes aids in identifying sources of excessive costs, unnecessary operations, or delays. This allows for the implementation of measures to reduce expenses and optimize resource utilization (Modibbo et al., 2021). Logistics processes include the delivery, i.e., ordering and taking care of the delivery of raw materials or semi-finished products for production, storage, i.e., keeping, manipulation, and acquisition of goods, production, i.e., mainly transport operations directly in the process: of production, distribution, i.e., sale of goods, customer service, etc. Accessibility is a complex and ambiguous concept that is defined in different ways, tested by different methods, and measured by different indicators, depending on the organization conducting the research and the purpose for which it is performed. The main problem is the incomparability of results obtained during transport accessibility research by various researchers based on the primary data obtained by them. Preferably, the research should be conducted using the same tools and at the same time to compare different spatial units, which requires quite a lot of research effort. For this reason, transport accessibility studies are usually fragmented. The tools usually used to determine travel time by car are travel planners, both professional and employed by transport companies, and are available on the Internet (Müller & D¨ aschle, 2018). The attempts to evaluate travel time for the same transport operation using different applications give different results. Another difficulty is the dynamics of travelling time. Commissioning even a small investment in infrastructure can significantly affect travel time on many transport routes due to the network nature of the infrastructure (Bubalo & Rajsman, 2020). Thus, investigating problematic aspects of transportation and production processes and developing recommendations for improvement can ensure continuous progress and process optimization. Moreover, any enterprise must reduce costs, enhance resource utilization efficiency, and increase profitability, particularly in a competitive market environment. Based on the analysis of scientific publications, this research aims to develop a conceptual model for assessing the correlation between accessibility and efficiency of transportation and production processes in commercial companies. 3. Methods 3.1. Methods to estimate the accessibility and efficiency of transportation and manufacturing processes in production companies The methods of testing the accessibility and productivity of transportation and manufacturing processes in production companies are presented in Fig. 1. The scheme of the research procedure includes four stages (Rostek & Knosala, 2016): I - separation of processes (in Fig. 1 an oval with a pink background), II - preparation of data for analysis (in Fig. 1 rectangles with a green background), III - analysis and efficiency assessment (in Fig. 1 rectangles with a yellow background), IV - development of an improvement and control program (in Fig. 1 rectangles with a blue background). The research on the efficiency of production companies was performed based on direct and indirect performance indicators for the period 2015–2019. The selection of these five years is justified from the perspective of obtaining a comprehensive and representative depiction of the state of transportation and manufacturing processes within enterprises during this time. Utilizing five years allows for the coverage of a significant duration and ensures a comprehensive representation of the dynamics in the realms of transportation and production. The aim was to improve the company’s performance by improving the aspects that indirectly affect the production processes. The audit of the transportation system aimed to define the interrelationships between transportation and production processes at the enterprise. The implementation of the fourth stage in the presented methodology of efficiency assessment starts with the development of the solution to be implemented, and the expected effect is to increase performance. At that, various concepts, methods, and tools aimed at increasing the performance output of the company would be useful. Particular attention should be paid to the assessment of tax consequences to justify the tax benefit by forming a "portfolio of commercial prudence" containing documents on: – the ability of the parties to carry out the transaction; – the real interaction of counterparties (before, during, and after the transaction); – the expediency and legality of the price, as well as the conclusion of the transaction. When analyzing the efficiency of logistics processes, it is necessary to identify the logistics bottleneck and develop a program to correct it. 3.2. Statistical processing Methods of service quality assessment. According to ´ Swiderski et al. (2018), a comprehensive delineation has been provided encompassing a multitude of facets, including financial expenditures, potential hazards, E. Smirnova et al. The Asian Journal of Shipping and Logistics 40 (2024) 52–60 55 and essential resource allocations. Evaluation of transport service was presented as a formula: Rn( τ ) = f(wn,1( τ ),wn,2(t),…., wn,k( τ )) (1) where: Rn( τ )is the evaluation of the n-th transport service for the time t; wn, k ( τ ) is the evaluation of the k-th application of the n-th transport service for the time t. The transportation and production system of enterprises was examined for patterns and regularities through the application of cluster analysis using the STATISTICA 7.0 program. Natural values of indicators were used in calculations. The clustering of objects was carried out following the Ward method. Calculation of quality coefficients of transportation and forwarding service in production companies. The paper uses the Rosstat statistical data of the Russian Federation and The State Statistical Committee of the Republic of Azerbaijan. Service quality indicators were determined by GOST R-52297, R-52298, and GOST R 51133–98, as well as following international quality standards (ISO 9000:2000), and transport periodicals. Preservation of cargoes by quantity, preservation of cargoes by quality, timely delivery of cargoes, insurance guarantees, the complexity of customer service, customer satisfaction, flexibility, efficiency on the processing of CIF, LCL application, efficiency on the processing of FOB, EXW, DDU applications, readiness, availability of representative offices in the countries of delivery and origin of cargo, presence of the simplified customs registration received using the anonymous questionnaire of subjects of transport and logistical activity of the Russian Federation and the Republic of Azerbaijan. The integral indicator of the quality of transportation ki was determined by the formula (Naish et al., 2015): ki= (ktqt)2+ (kcqc)2+ (ksqs)2+ (keqe)2 √(2) where kt is the timely transportation factor, qc is the transportation completeness factor, ks is the coefficient of transportation safety, ke is the transportation economy factor, qt is the weightiness factor of timely transportation, qc is the weightiness factor of transportation completeness, qs is weightiness factor of transportation safety, qe is the weightiness factor of transportation economy. 3.3. Cluster analysis of transportation and manufacturing processes Cluster analysis is a research method that includes various classification algorithms. The main purpose of its use in research practice is to identify sets of similar data and then group them so that objects belonging to the same group are relatively well interconnected, taking into account the adopted criterion or criteria. Experimental data were imported into the STATISTICA 7.0 software package. Individual samples undergo a comparison process using designated methods of similarity, difference, and cohesion, followed by their classification into distinct clusters. Due to its inherent nature as a research methodology, cluster analysis offers the capability to identify structures without providing causal explanations. Consequently, it frequently serves as a catalyst for subsequent research endeavors that strive to unravel the factors that govern the particular structure exhibited by the objects under investigation. In research practice, cluster analysis includes three classes of algorithms (Li et al., 2019): a) hierarchical methods, in which a tree of relationships between objects belonging to the dataset under study is constructed; b) grouping through the k-average method, which searches for objects that are most closely related to each other for a predetermined number of classes; c) fuzzy cluster analysis method, in which each object under study can be assigned to more than one class and its belonging to certain classes are described as fuzzy. The use of cluster analysis is related to the association of relatively closely related objects with the accepted criterion of distance in one-dimensional or multidimensional space, although this distance does not have to be a real unit. Statistical analysis of the results was performed using χ 2 and t-criteria. A 95% confidence interval was determined. The statistical significance was set at P <0.005. Also, the mean square deviation, percentile, standard deviation, and median were calculated. 4. Results The concepts, methods, and tools with the division into logistics processes have been classified to assess the efficiency of transportation and manufacturing processes in production companies (Rostek & Knosala, 2016). Table 1 presents the classification in which the selected concepts, methods, and tools have been divided according to the applications aimed at improving the respective logistic processes and, Fig. 1. Performance evaluation model of manufacturing companies. E. Smirnova et al. The Asian Journal of Shipping and Logistics 40 (2024) 52–60 56 consequently, their performance. Of particular interest is the structure of the transportation and manufacturing system. This classification includes most concepts and methods that can be implemented in production companies. However, there is a space to elaborate solutions designed for specific enterprises, in particular, for high-tech manufacturing processes. Therefore, the use of traditional classification methods for a deeper study of the modern status of transportation and logistics services should be replaced by characteristics of the stratification type and supported by specific research tools, such as cluster analysis. Table 2 and Fig. 2 present the results of the transport system cluster analysis. As can be seen from the dendrogram, road transport takes a leading place in the transportation sector of production firms (Fig. 2). Repeated observations of the behavior of transport flow in companies allow the conclusion that business entities tend to gravitate towards a certain area, sometimes consciously, sometimes only intuitively choosing the conditions of their functioning, in which they believe it is best to achieve economic goals. Consequently, certain layers of subjects appear in the market, which in some way are similar to each other (with similar competitive potential, willingness to achieve similar goals, or the same degree of efficiency in achieving the goals). The stratification image of the transport market, which is an important reference system for all economic processes of individual enterprises, determines their position in this market space but is also important for ordering the market space itself. The results of clustering (Table 3) showed that the share of road transport in the structure of transport operations is from 59 to 61.66667. The share of air, sea, and mainline transport is from 13 to 14 (Table 4). Clusters included all areas of business using certain types of transportation in the period 2014–2019. For example, cluster analysis allowed for the determination of the stratification structures of transport objects in the market, which may indicate the general conditions of work for more entities. Of certain interest are factors that define the reason for these objects to be concentrated at a certain level (Table 5). The study of the structure of transport operations in production companies using cluster analysis has shown that the stability of a logistics cluster for a particular transport type is proportional to the values of structural and functional elements included in the cluster. Thus, the need to apply mathematical statistics methods to determine the system’s stability and vulnerability arises (Perebyinis & Perebyinis, 2005) (Fig. 3). This scheme is aimed at improving the quality of logistics and production processes with a simultaneous reduction in transportation costs. The adoption of these two approaches facilitates the augmentation of enterprises’ profitability, as elucidated by Bergami (2011). The ratio of accessibility and efficiency of transportation and manufacturing processes is a list of quality indicators obtained by calculating quality coefficients of transport and forwarding services in production companies, which are presented in Table 6. Considering the performance evaluation provisions defined, vehicle users and experts estimated individual requirements. The data on 812 transport operations performed in the last five years on the delivery of goods to the production site have been collected. According to this information, a consolidated table of the quality of transport and forwarding services of production companies has been comprised (Table 5). To assess the quality of freight forwarding services of production companies, experts have assigned weight characteristics on a scale (0−1), where 0 means little and 1 means very important. The requirements are assigned the following weight values: 0….1. Based on the weighted average value of individual requirements, the quality of transport services was assessed as positive or negative. According to Table 6, the level of transport and forwarding services quality was found to be satisfactory and set at the level of 0.6 and higher. Noteworthy, the quality of transport and forwarding services of production companies tend to improve, and although the level of generalization of the study findings is limited since the sample of companies under study is difficult to be considered representative, the results confirm the validity of the adopted thesis and indicate that transport and forwarding enterprises affect the effectiveness of the supply chain through a wide range of factors. At the same time, the study of the implementation degree of these factors allowed identifying those issues whose application should be improved. It is known that if transport and forwarding companies meet agreed delivery deadlines, ensure that the goods will be shipped intact, and the time from order placement to delivery to the final recipient will be reduced, as well as are ready to overcome sudden and unforeseen obstacles and fully satisfy orders, the supply chain works more efficiently. Factors related to parcel processing are performed by transport and forwarding companies at a good level. Consequently, transport and forwarding companies are compelled to enhance various dimensions, including the intricacy of services rendered, the responsiveness to unforeseen and spontaneous requests, the proficiency of their personnel, and the capacity to diligently fulfil the obligations arising from contracted orders. The improvement of these factors will undoubtedly contribute to more efficient supply chain operations. 5. Discussion Of high relevance is the optimization of performance and accessibility of transport and manufacturing processes, but the schemes for decision-making in most cases are not universal and are characterized by a rather complex structure (Barkemeyer & Miklian, 2019; Yao & Askin, 2019). Given the fact that modern transport is the area of implementation of modern solutions for supply chain organization and transport process management, a conceptual scheme to optimize the quality of transport services in the context of production and commercial activities of enterprises has been developed in this paper. First of all, it is necessary to pay attention to organizational decisions in the field of transport integration. In Hu’s research (Hu et al., 2019), aimed at assessing the efficiency of Table 1 The classification of concepts, methods, and tools for efficiency improvement through separation of transport logistics processes. Supply Distribution LT, MRP, EWZ, information level update, update based on periodic review, MIN-MAX system ABC, OEE, POK, LT, DRP Production Storage SMED, CONWIP, FIFO, FEFO, RFID, 3 R, OEE ABC, XYZ, ABC/XYZ, VMI, FIFO, FEFO, LIFO, RFID, system MIN-MAX, information level update, update based on periodic review, OEE, WMS, VMI All processes Benchmarking, LM, TQM, TOC, kanban, Poka-Yoke, 5 S. kaizen, TPM Table 2 The structure of transportation in production companies on the territory of Russia (1) and Azerbaijan (2). Transport type 2015 2016 2017 2018 2019 (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) Automobile 61 62 59 64 59 58 62 60 62 65 Railway 25 18 27 17 27 18 25 17 26 17 Other transport 14 20 14 19 14 24 13 23 12 18 E. Smirnova et al. The Asian Journal of Shipping and Logistics 40 (2024) 52–60 57 logistic processes in manufacturing enterprises using the network analysis method, it was found that through the development of effective planning and management strategies, optimal resource utilization can be achieved while avoiding unnecessary time and cost expenditures. This may involve time optimization, priority setting, and the use of automation tools (Hu et al., 2019). Evans et al. (2017) argue that optimal warehouse placement can ensure fast and accurate order processing, preventing delays and errors. Furthermore, establishing optimal transportation routes can positively impact the integrity and security of goods during delivery (Evans et al., 2017). According to Dias and Ierapetritou (2017), companies typically adhere to agreed-upon delivery terms, guaranteeing product integrity, reducing the time from order placement to delivery, and being prepared to overcome obstacles, thus forming an efficient supply chain. However, to further enhance its effectiveness, companies should be ready to handle unforeseen orders, demonstrate professionalism among employees, and exhibit the ability to fully fulfil the commitments arising from accepted orders, as supported by the conducted research (Dias & Ierapetritou, 2017). The study conducted by Li et al. (2019), which employs cluster analysis to analyze the structure of transportation operations in manufacturing enterprises, revealed that each cluster possesses its unique structure reflecting the specificity of transportation operations. Understanding how different structural elements impact the efficiency and quality of transportation operations is crucial for improving logistic processes, as indicated in the conducted research (Li et al., 2019). Moons et al. (2019) discovered that specific clusters of transportation operations exhibit high delivery speeds but high costs, while others have low prices but longer delivery times. This provides grounds for identifying different optimization strategies based on the priorities of the enterprise (Moons et al., 2019). Therefore, the statements made by researchers regarding the importance of factors for achieving efficiency in the transportation and logistics sector, as well as their research findings, hold significant scientific value. They underscore the significance of these factors and elucidate their impact on the processes and outcomes of logistical operations. A decrease in the efficiency of the logistics process in the long term or Fig. 2. Dendrogram of the structure of transportation and manufacturing system of production companies as per types of transport. Table 3 Cluster means. Case ID Cluster No. 1 Cluster No. 2 C_1 59.00000 61.66667 C_2 27.00000 25.33333 C_3 14.00000 13.00000 Table 4 Euclidean distances between clusters. Cluster number No. 1 No. 2 No. 1 0.000000 3.629630 No. 2 1.905159 0.000000 Table 5 The descriptive statistics. Variable 2015 2016 2017 2018 2019 Mean 33.33 33.33 33.33 33.33 33.33 Valid N 3.00 3.00 3.00 3.00 3.00 Median 25.00 27.00 27.00 25.00 26.00 Mode no mode no mode no mode no mode no mode Minimum 14.00 14.00 14.00 13.00 12.00 Maximum 61.00 59.00 59.00 62.00 62.00 25.000th Percentile 14.00 14.00 14.00 13.00 12.00 75.000th Percentile 61.00 59.00 59.00 62.00 62.00 Geometric Mean 27.74 28.15 28.15 27.21 26.84 Harmonic Mean 23.47 23.92 23.92 22.55 21.75 Std.Dev. 24.58 23.16 23.16 25.54 25.79 Variance 604.33 536.33 536.33 652.33 665.33 Average Deviation 18.44 17.11 17.11 19.11 19.11 Range 47.00 45.00 45.00 49.00 50.00 Quartile Range 47.00 45.00 45.00 49.00 50.00 Skewness 1.35 1.14 1.14 1.31 1.18 Sum 100.00 100.00 100.00 100.00 100.00 E. Smirnova et al. The Asian Journal of Shipping and Logistics 40 (2024) 52–60 58 a lack of stability indicates the need for improvement. Thus, a classification has been developed that can reduce the time required to find a satisfactory solution. We have identified several possible directions for improving the supply chain: 1. Reducing transportation costs: By optimizing routes, utilizing efficient vehicles, and enhancing logistical processes, transportation expenses can be lowered. This will enable the provision of more competitive prices for consumers and improve the profitability of the enterprise (Alqodsi, 2021). 2. Decreasing transportation delays: Improving the organization of transportation processes, aligning delivery schedules, and employing smart monitoring systems can help reduce delays during product transportation. This will ensure timely order delivery and customer satisfaction. 3. Enhancing service quality: By providing high-quality service, including timely delivery, reliability, and product integrity, customer needs can be satisfied. This will help retain existing customers, attract new ones, enhance the company’s reputation, and increase sales volumes. 4. Efficient inventory management: By optimizing inventory management processes, reducing excess inventory, and improving demand forecasting accuracy, the efficiency of the production process can be enhanced, and costs can be reduced. The research has the potential to make a significant contribution to several key areas: 1. Assessment of Transportation and Freight Forwarding Service Quality: The study provides a systematic approach to evaluating the quality of transportation and freight forwarding services of manufacturing companies based on data from 812 transport operations. The assessment was conducted using weighted characteristics assigned by experts, offering a valuable tool for enterprises in selecting optimal transportation and freight forwarding partners. 2. Enhancement of Supply Chain Efficiency: The results indicate that the quality of transportation and freight forwarding services influences the efficiency of supply chains. It is elucidated that the performance of transportation and freight forwarding companies, such as timely delivery and cargo integrity preservation, significantly impacts the efficiency of supply chains. 3. Improvement of Factors Affecting Service Quality: The research points out specific aspects, such as service complexity, response to unforeseen requests, professionalism of the personnel, and commitment fulfilment, which can be improved by transportation and freight forwarding companies to enhance the efficiency of supply chains. 4. Validation of the Impact of Transportation and Freight Forwarding Companies: The study validates the thesis that transportation and freight forwarding companies have a substantial impact on the efficiency of supply chains through various influencing factors. In conclusion, the article can serve as a foundation for the development of strategies aimed at enhancing the quality and efficiency of logistic services within manufacturing companies, thereby contributing to further advancement and refinement of supply chain management. Fig. 3. Conceptual scheme of transport services quality optimization with respect to production and commercial activity of enterprises. Table 6 Quality coefficients of transport and forwarding services in production companies. Service Quality Factors 2015 2016 2017 2018 2019 р χ 2 t-test Preservation of goods by quantity 0.999 1.000 1.000 1.000 1.000 0.0001 0.98 0.71 Preservation of goods by quality 0.990 0.991 0.987 0.989 0.990 0.0001 0.98 0.72 Timely delivery of goods 0.753 0.827 0.789 0.786 0.759 0.005 0.52 0.91 Insurance guarantees 1.000 0.750 0.400 0.333 1.000 0.005 0.96 0.71 Complex customer service 0.827 0.761 0.829 0.810 0.806 0.005 0.96 0.71 Customer satisfaction 0.965 0.967 0.970 0.969 0.971 0.05 0.80 0.67 Flexibility 0.942 0.942 0.911 0.901 0.964 0.05 0.28 0.80 Timeliness to process CIF, LCL application. 0.700 0.769 0.833 0.750 0.933 0.15 0.85 0.87 Timeliness to process FOB, EXW, DDU application 0.921 0.833 0.758 0.714 0.800 0.05 0.96 0.92 Readiness 0.981 0.983 0.975 0.979 0.978 0.05 0.96 0.96 Representative offices in countries of delivery and cargo composition 1 1 1 1 1 0.05 0.98 0.77 Simplified customs clearance 0 0 0 1 1 0.0001 0.98 0.71 Compliance of documents with international standards 1 1 1 1 1 0.0001 0.98 0.71 Possibility to independently track the cargo by customers 0 1 1 1 1 0.0001 0.98 0.72 Integral indicator of transport service quality 0.816 0.802 0.783 0.932 1 0.005 0.52 0.91 E. Smirnova et al. The Asian Journal of Shipping and Logistics 40 (2024) 52–60 59 Thus, the scientific value of this research lies in its contribution to the understanding and improvement of transportation and production processes within the context of manufacturing companies. This research makes an important contribution to the development of logistics theory and practical activities in manufacturing enterprises, as it helps refine theoretical concepts and develop new methods for the analysis and optimization of transportation and production processes. 6. Conclusions The research on evaluating the accessibility and efficiency of transportation and production processes in enterprises makes a significant contribution to the development of logistics theory and practical activities. An essential aspect of this study is the establishment of a relationship between the structure and functioning of transportation operations in manufacturing enterprises. It has been established that each cluster possesses its unique structure, which reflects the specificity of transportation operations. The application of cluster analysis has allowed for the identification of the stability of the logistics cluster for a particular type of transportation, which is proportional to the values of the structural-functional elements that constitute the cluster. The research findings also confirm the importance of improving the quality of transportation and production processes to achieve efficiency in supply chains. The assessment of the quality of transportation and logistics services has revealed that companies have the potential to enhance factors such as service complexity, readiness to handle unforeseen orders, professionalism of employees, and the ability to meet the obligations associated with accepted orders in a comprehensive manner. Key Conclusions: 1. Contribution to Science and Theory: The article makes a substantial contribution to the scientific domain of logistics by specifying the interconnections and significance of factors influencing the quality of transportation and freight forwarding services. It expands theoretical concepts and provides a foundation for further research in this area. 2. Practical Significance: The research holds direct practical value for manufacturing companies, offering methodologies that can be utilized to enhance their logistic processes and select optimal transportation and freight forwarding partners. 3. Differentiation and Justification of Conclusions: Drawing on the analysis of data from 812 transport operations, the article presents well-founded and differentiated conclusions regarding the quality and efficiency of transportation and freight forwarding services within manufacturing companies. 4. Call to Action: Concluding the findings, a call to action can be incorporated, urging manufacturing companies and professionals to implement the proposed approaches for improving logistic and transportation freight forwarding processes. Research Limitations: The effectiveness and accessibility under investigation may be influenced by the economic context, which is subject to changes, and the application of technical solutions and innovations may vary over time, potentially impacting the results. The prospect of further research lies in the necessity of developing and refining solutions tailored to specific enterprises, particularly those involving high-tech production processes. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments Not applicable. References Abuova, A., Lakhno, V., Oshanova, N., Yagaliyeva, B. and Anosov, A. (2019) Conceptual model of the automated decision-making process in analysis of emergency situations on railway transport, in International Conference on Research and Practical Issues of Enterprise Information Systems, Springer, pp. 153–162. https://doi.org/10.1007/ 978–3-030–37632-1_14. Alqodsi, E. M. (2021). The right to pre-contractual information in e-commerce consumer contracts: UAE law and comparative perspectives. Journal of Legal, Ethical and Regulatory Issues, 24, 1. Barilla, D., Carlucci, F., Cir` a, A., Ioppolo, G., & Siviero, L. (2020). Total factor logistics productivity: A spatial approach to the Italian regions. Transportation Research Part A: Policy and Practice, 136, 205–222. https://doi.org/10.1016/j.tra.2020.03.033 Barkemeyer, R., & Miklian, J. (2019). Responsible business in fragile contexts: Comparing perceptions from domestic and foreign firms in Myanmar. Sustainability, 11(3), 5–98. https://doi.org/10.3390/su11030598 Bendul, J. C., & Blunck, H. (2019). The design space of production planning and control for industry 4.0. Computers in Industry, 105, 260–272. https://doi.org/10.1016/j. compind.2018.10.010 Bergami, R. (2011). Incoter MS 2010: What you need to know for smooth trading. MHD Supply Chain Solutions, 41(1), 38. Borgatti, S. P., & Li, X. (2009). On social network analysis in a supply chain context. Journal of Supply Chain Management, 45(2), 5–22. https://doi.org/10.1111/j.1745493X.2009.03166.x Bubalo, T., & Rajsman, M. (2020). Quality of transport service in intercity road passenger transport: A literature review. Journal of Traffic and Logistics Engineering, 8(1), 18–28. https://doi.org/10.18178/jtle.8.1.18-28 Bueno, A., Godinho Filho, M., & Frank, A. G. (2020). Smart production planning and control in the Industry 4.0 context: A systematic literature review. Computers & Industrial Engineering, 149, Article 106774. https://doi.org/10.1016/j. cie.2020.106774 Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163–180. https://doi.org/10.1016/j.jom.2010.12.008 Cavagnini, R., Hewitt, M., & Maggioni, F. (2020). Workforce production planning under uncertain learning rates. International Journal of Production Economics, 225, Article 107590. https://doi.org/10.1016/j.ijpe.2019.107590 Cavalcanti, J. H., Kov´ acs, T., & K˝ o, A. (2022). Production system efficiency optimization using sensor data, machine learning-based simulation and genetic algorithms. Procedia CIRP, 107, 528–533. https://doi.org/10.1016/j.procir.2022.05.020 Cui, L., Fan, D., Guo, F., & Fan, Y. (2018). Explicating the relationship of entrepreneurial orientation and firm performance: Underlying mechanisms in the context of an emerging market. Industrial Marketing Management, 71, 27–40. https://doi.org/ 10.1016/j.indmarman.2017.11.003 Dias, L. S., & Ierapetritou, M. G. (2017). From process control to supply chain management: An overview of integrated decision making strategies. Computers & Chemical Engineering, 106, 826–835. https://doi.org/10.1016/j. compchemeng.2017.02.006 Erceg, ˇ Z., & Mularifovi´ c, F. (2019). Integrated MCDM model for processes optimization in supply chain management in wood company. Operational Research in Engineering Sciences: Theory and Applications, 2(1), 37–50. https://doi.org/10.31181/ oresta1901015e Ertogral, K., & ¨ Oztürk, F. S. (2019). An integrated production scheduling and workforce capacity planning model for the maintenance and repair operations in airline industry. Computers & Industrial Engineering, 127, 832–840. https://doi.org/ 10.1016/j.cie.2018.11.022 Evans, S., Vladimirova, D., Holgado, M., Van Fossen, K., Yang, M., Silva, E. A., & Barlow, C. Y. (2017). Business model innovation for sustainability: Towards a unified perspective for creation of sustainable business models. Business Strategy and the Environment, 26(5), 597–608. https://doi.org/10.1002/bse.1939 García-Arca, J., Garrido, A. T. G. P., & Prado-Prado, J. C. (2017). Sustainable packaging logistics’. The link between sustainability and competitiveness in supply chains. Sustainability, 9(7), 1098. https://doi.org/10.3390/su9071098 Ghandour, A., Kintonova, A., Demidchik, N., & Sverdlikova, E. (2021). Solving tourism management challenges by means of mobile augmented reality applications. International Journal of Web-Based Learning and Teaching Technologies, 16(6), 1–16. https://doi.org/10.4018/IJWLTT.293280 Hu, W., Dong, J., Hwang, B. G., Ren, R., & Chen, Z. (2019). A scientometrics review on city logistics literature: Research trends, advanced theory and practice. Sustainability, 11(10), 2724. https://doi.org/10.3390/su11102724 Hussain, Z. (2019). Developing a novel based productivity model by investigating potential bounds of production plant: A case study. International Journal of Production Management and Engineering, 7(2), 151–159. https://doi.org/10.4995/ ijpme.2019.10911 E. Smirnova et al.