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Article

Assessment of the Sustainable Supply Chain Finance Factors

by
Neringa Slavinskaitė
,
Kristina Čižiūnienė
* and
Vytautė Bundonytė
Department of Logistics and Transport Management, Vilnius Gediminas Technical University, Plytinės Str. 25, LT-10105 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(3), 1002; https://doi.org/10.3390/su17031002
Submission received: 12 October 2024 / Revised: 19 January 2025 / Accepted: 22 January 2025 / Published: 26 January 2025
(This article belongs to the Special Issue Green Integration and Supply Chain Governance: A Circular Economy Perspective)
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Abstract

:
In a scientific context, the main focus of sustainable supply chain management is on the creation and optimization of product and information flows; however, the management of financial flows receives insufficient attention. All effectively developed supply chain activities may collapse as a result of inadequate management of sustainable supply chain financial processes. In order to successfully develop systematically functioning processes of the international supply chain, it is necessary to analyze how to apply financing instruments in a targeted and effective manner. Adequate financing of the sustainable supply chain is the effect of great prospects and competitive advantage not only on a national scale but also in international markets. The aim of this research was to assess the importance of financing instruments used in international sustainable supply chain finance. Correlation-regression analysis was chosen for the research, which was designed to assess the factors of financial instruments of the dairy industry sustainable supply chain using the example of a company. The results showed that the key factor in the supply chain processes of the dairy products production company was the turnover ratio of buyers’ debts; therefore, in order for the company to improve the indicators of the sustainable supply chain, it should allocate more financing specifically to the turnover ratio of buyers’ debts.

1. Introduction

High competitiveness and globalization have made the supply chain process complex and constantly changing. Supply chain management focuses on creating and optimizing product and information flows [1,2]; however, one aspect that is often neglected and not widely discussed in the scientific literature is financial flows, which are indeed an essential part of business continuity. Every company acknowledges that the emergence of new partners and supply chain integration in foreign markets are efficient means of cutting expenses and advancing business development, but despite being a crucial part of internal business transactions, financial communication is still largely ignored. All effectively developed supply chain activities may collapse as a result of inadequate financial process management. In order to successfully develop systematically functioning processes of the international supply chain, it is necessary to analyze how to apply financing instruments in a targeted and effective manner. Because, according to Medina et al. [3], supply chain financing [4,5,6,7,8,9,10,11,12] can contribute to improving supply chain sustainability.
Trade through the international supply chain makes up an increasing share in the structure of international trade, which justifies the growth of its importance. However, the level of participation of countries in the international supply chain is different. The unfavorable business environment, financial institutions’ passive behavior, the lack of process automation, the disparities in technological advancement between countries, the mistrust of supply chain participants, the financial burden encountered, and other factors all contribute to this trend. And all this creates the conditions for inefficient operations of international supply chain actors, and the supply chain financing in place does not help to solve supply chain problems. The inability to automatically manage some certain processes, particularly those related to sending invoices, contributes to the decline in the effectiveness of the international supply chain [13]. However, large companies tend not to implement an automated process for sending invoices, because doing so gives them more time to settle with suppliers by prolonging the supply chain cycle [14]. As for the development and implementation of information technology in supply chain systems, it is observed that the blockchains’ potential and capabilities are promoting new business models, particularly when it comes to effective cost management and additional value creation. Supply chain management through blockchain technology [15,16] makes it possible to build trustworthy connections between partners, increasing the conditions for the supply of goods/services and essentially contributing to the improvement of the entire supply chain [17].
Although financing management of supply chain system is quite a widely discussed topic among scholars [18,19,20,21,22,23,24,25,26,27,28] worldwide, different financing instruments are found among different scholars. Financing and risk mitigation practices enable optimization of working capital and liquidity, investment in supply chain processes, and transaction management, with certain measures in place [29]. International supply chain financing instruments are divided into three main areas: receivables-oriented, prepayments, and credits. These techniques are applied to help alleviate and reduce problems in international supply chain processes [30]. Some scholars address this issue through firm resilience by using resource configuration and organizational learning [31] or as hybrid closed-loop supply chains [32]. According to Bal and Pawlicka [33], the systematization of financing in this area is essential because supply chain management is a field that is continuously modernizing, changing, and presenting challenges. In the authors’ assessment, it is necessary to discuss and purposefully use knowledge about the coexistence of supply chain finance, applying modern solutions such as sustainable finance or blockchain technology. However, Alkaabi and Nobanee [34] emphasize that sustainability [35] is not just about maximizing a company’s reputation and good intentions but is also important for the long-term success and development of the business, which means financial benefits. Thus, in order for this to be successfully implemented, it is necessary to identify the link between supply chain finance and sustainability policy, and this is where the firm’s strategy [36] should be focused. In terms of supply chain finance, this is a relatively new phenomenon in the fields of supply chain management and finance, as well as control. Supply chain finance is a more common practice in multinational companies, but it is a relatively unknown concept in small and medium-sized companies. However, supply chain finance is believed to offer opportunities, and small and medium-sized companies could also benefit from it. The question is what supply chain finance can add to existing theories of corporate finance and supply chain management, as well as the application of corporate finance, supply chain management, and information and communication technology concepts [37].
Supply chain finance (SCF) plays a transformative role in modern supply chains, enabling companies to improve cash flow, optimize working capital, and strengthen the financial relationships of supply chain participants. These tools enhance collaboration, foster trust, and drive value creation in supply networks. Integrating trade credit and equity into SCF strategies helps companies achieve financial flexibility, secure competitive advantages, and align stakeholder incentives. Trade credit is a cornerstone of supply chain financing, representing credit extended by suppliers to buyers, allowing the latter to defer payments in advance of receiving goods or services. Trade credit, as a financing mechanism, is particularly useful in supply chains because it reduces the direct financial burden on buyers, improves cash flow management, and increases the buyer’s ability to meet operating needs without resorting to external financing. On the other hand, suppliers use trade credit to build stronger buyer relationships, secure repeat business, and negotiate better terms for future transactions. According to Gelsomino et al. [38], “Trade credit is a flexible and cost-effective financial instrument, especially for small and medium-sized enterprises (SMEs) operating in global supply chains”. It should be noted that trade credit facilitates operational efficiency by aligning payment terms with the cash flow needs of suppliers and buyers. It strengthens financial resilience in supply chains by ensuring supplier [39] liquidity and enabling buyers to manage their working capital effectively. Another innovative aspect of supply chain financing is the use of equity or equity-based financing, which goes beyond traditional debt financing models. Equity ownership allows companies to invest directly in their supply chain partners, fostering closer collaboration and aligning financial incentives between stakeholders. Unlike trade credit, which focuses on short-term liquidity, equity management represents a long-term commitment to supply chain partnerships, allowing companies to achieve greater strategic alignment. Equity-based supply chain financing deepens buyer–supplier collaboration and allows companies to share risks and rewards more equitably across supply networks [40,41]. Equity management also plays a key role in enhancing supply chain sustainability by encouraging buyers to invest in suppliers that meet their environmental, social, and governance objectives. It should be noted that equity in supply chain financing aligns financial incentives and fosters long-term collaboration. It provides suppliers with access to affordable capital, while enabling buyers to secure reliable and aligned supply chain partners, particularly in critical or high-risk industries. Although trade credit and equity serve different supply chain financing purposes, they are complementary tools that together enhance financial stability and operational efficiency in supply chains. Trade credit addresses short-term liquidity challenges, ensuring that suppliers can maintain business continuity and buyers can manage cash flow effectively. Equity management, on the other hand, is designed for long-term strategic alignment, fostering collaboration, and investments that increase supply chain resilience and sustainability.
Therefore, it should be remembered that supply chain finance encompasses a set of financial products and strategies that improve the management of cash flows between buyers and suppliers. It often uses digital platforms and advanced technologies such as blockchain to provide visibility and increase efficiency. The main objective of SCF is to reduce financial risk, improve liquidity, and strengthen buyer–supplier relationships. However, it should not be forgotten that the sustainability of supply chains focuses on the environmental, social, and economic impacts of supply chain activities. This includes reducing the carbon footprint, promoting ethical working practices, ensuring resource efficiency, and maintaining the economic viability of all participants. A sustainable supply chain is consistent with the principles of corporate social responsibility and contributes to broader environmental, social, and governance objectives, promoting sustainable practices while providing financial benefits. It should therefore be emphasized that supply chain finance and sustainability are interrelated through six key aspects: (1) incentivizing sustainable practices through financial benefits [38], (2) enhancing transparency and traceability [40,41], (3) reducing financial and environmental risks [12], (4) fostering collaborative relationships, (5) driving innovation and investment in sustainable technologies [42], and (6) alignment with ESG reporting and regulatory requirements [43].
Supply chain finance (SCF) is an important mechanism to optimize cash flow and improve financial stability across supply chains by addressing complex issues of liquidity, cost, and time. From the perspective of suppliers and buyers, SCF essentially revolves around the management and improvement of cash flows related to debt, raw materials, manufactured goods, costs, and sales. Deconstructing SCF using these variables provides valuable insights into how financial instruments and strategies contribute to smoother operations and value creation in supply chains.
However, it should also be noted that supply chain finance can be deconstructed from the following perspectives:
  • Supplier and Buyer Debts. When analyzing supplier and buyer debts, it is important to mention that suppliers often face the challenge of late payments from buyers, which can lead to working capital constraints [19]. As opposed to buyer debts: buyers may choose to extend payment terms to preserve their cash flow while ensuring that suppliers are not negatively impacted. This creates a win–win scenario where buyers improve their cash conversion cycles and suppliers receive payments on time. Dynamic discounting also allows buyers to take advantage of early payment discounts offered by suppliers, resulting in savings over time. By using SCF tools, buyers can better control their debt obligations, strengthen supplier [44] relationships, and enhance the overall resilience of the supply chain [38].
  • Raw Materials. When examining raw materials, it is worth noting that SCF has a significant impact on the acquisition and financing of raw materials, which are the basis of production processes. Suppliers often require upfront capital to purchase raw materials, and late payments from buyers can limit their ability to secure high-quality raw materials. Inventory financing, a key SCF tool, allows suppliers to finance their raw material purchases using inventory as collateral. This ensures that suppliers can acquire raw materials on time and in sufficient quantities, thus avoiding production delays [12].
  • Manufactured Products. The relationship between SCF and manufactured products is fundamentally related to production cycles and inventory management. Suppliers that manufacture products often face high upfront costs related to raw material acquisition, labor, and operational costs. For buyers, SCF tools increase visibility into suppliers’ manufacturing processes, ensuring that manufactured goods are delivered on time and meet quality standards. By integrating SCF with digital technologies such as blockchain or IoT, buyers can monitor production stages, verify compliance with sustainability standards, and reduce the risk of late or defective shipments [40,41].
  • Costs. SCF is designed to reduce costs related to financing, procurement, and operational inefficiencies. SCF also helps reduce hidden costs associated with supply chain disruptions such as production delays, stockouts, or rush purchases [38].
  • Sales. SCF plays an important role in enabling both suppliers and buyers to optimize their sales and revenue generation processes. For example, export financing allows suppliers to enter new international markets, reducing the risks associated with international transactions and currency fluctuations. In addition, SCF tools help buyers align cash flows with sales cycles.
In summary, the variables discussed above—supplier and buyer debts, raw materials, manufactured products, costs, and sales—are closely interrelated in SCF systems. For example, delays in paying supplier debts can limit the purchase of raw materials, disrupt the production of manufactured products, and increase overall supply chain costs. Similarly, inefficient raw material management can lead to higher production costs, lower sales, and strained buyer–supplier relationships.
SCF solutions address these interdependencies by providing liquidity and financial flexibility at critical points in the supply chain. By aligning cash flow with operational needs, SCF strengthens collaboration between buyers and suppliers, reduces risk, and drives overall supply chain efficiency. It also creates a more equitable distribution of financial resources, ensuring that suppliers, especially small and medium-sized enterprises (SMEs), are not disproportionately burdened by cash flow constraints.
The aim of this research is to assess the importance of financial instruments used in international supply chain finance.
The contribution of the research study was as follows: An analysis of the scientific literature was carried out, and the main financial factors of the sustainable supply chain of the dairy industry production company were evaluated. After analyzing the dairy and dairy products production company and evaluating the factors of the sustainable supply chain, it was found that the turnover ratio of buyers’ debts had the greatest influence. In terms of originality, this research study was a case study, and its findings could provide meaningful implications for the supply chain finance management with a greater focus on the turnover ratio of buyers’ debts.
This research article is structured as follows: the second section presents the brief theoretical background of supply chain finance and is followed by a section introducing the methodology of investigation, including detail about measure and procedure used in the research; the latter section is followed by the results and discussion. Future research and limitations are given along with the conclusions in the last section.

2. Review of Literature Sources

Supply chain finance is a relatively new topic in logistics. New research shows that proper application of supply chain financial instruments can reduce working capital and capital costs by up to 40%. Reverse factoring is considered to be the basis for the emergence of supply chain finance. Factoring has traditionally been used to finance a company’s receivables, submitting the existing debt for recovery from the company’s customers and receiving the agreed amount of money immediately [45]. According to Zhu et al. [46], supply chain finance (SCF) is an effective system that is implemented in order to reduce financing costs and improve financing efficiency, and especially in the past year, it has gained tremendous momentum. Supply chain finance is playing an increasingly important role in operational and financial practices (Figure 1).
One of the distinguished meanings of financing is financial debt instruments that are concluded in the form of financial contracts or equity. They are developed by at least two supply chain partners and the parent company, with the aim of improving overall financial performance and reducing supply chain risk [47]. It is also emphasized that the use of financial instruments and technologies aims to optimize the working capital and liquidity of cooperating partners, which is linked to supply chain management processes [48]. Other authors, summarizing the analysis, distinguish the following three main components in supply chain finance: cooperation, technology implementation and application, and the financing aspect itself [29].
More and more research is being conducted to link logistics and supply chain dependencies to business value and financial performance. Supply chain finance enables the improvement of finances through collaboration between manufacturers, suppliers, customers, and logistics intermediaries. Optimizing finances outside the company is achieved by reducing capital costs and increasing cash flow volumes. Definitions of supply chain finance focus (Figure 2) on streamlining the implementation of financial institution or technology solutions to align financial flows with product and information flows in the supply chain [33].
On the other hand, there are theoretical changes in supply chain finance due to cooperation in the supply chain; therefore, it is proposed to create value by having better management of working capital and closer cooperation between departments or even by making decisions about cultural business changes, especially when it comes to small and medium-sized businesses [33].
Implementation of a supply chain finance program typically results in changes to the terms of payables that may affect balance sheet classification [1]. From an accounting point of view, the implementation of supply chain finance focuses on whether the same classification should be continued for payables or whether they should be classified as other liabilities, such as debt financing. The international accounting program does not provide any clear guidance on this issue; therefore, it is important to focus on reclassification in order to improve the transparency of the international supply chain finance program [49]. This will ensure the benefits of supply chain finance (Table 1).
Analyzing the benefits of supply chain finance distinguished in Table 1, it can be summarized that it has a positive impact on the aspects of payment and purchase of goods [37]. Enabling management of payment terms also helps to build long-term and successful relationships with partners [52], but this requires systematic cash flow forecasting and especially allocation [51]. Although different authors distinguish different benefits, almost all scientific articles emphasize the main benefits of supply chain finance: optimizing the company’s financial indicators and reducing the risk of supply processes [50].
Often the benefits of supply chain finance are emphasized from the perspective of suppliers. The benefits of international supply chain finance are manifested when suppliers’ cash flows become more stable and much easier to plan. It is also important to mention that suppliers are given the opportunity to minimize cost financing (Figure 3).
The three delineated aspects of the dimensions of financing can spatially be opposed to each other by the help of a “supply chain finance cube” (Figure 3). The cube indicates that SCM (supply chain management) measures can apply to all three dimensions of the cube in order to reduce the capital costs within the supply chain [53].
Payment can be received earlier and on more convenient terms than would be possible to receive from a bank when financing is provided to a supplier located in another country. Supply chain finance is typically directed toward financing the working capital of the parent company’s supplier [50]. From a theoretical point of view, the parent company is usually assigned to a financially secure environment; meanwhile, the suppliers are characterized by changing economic growth, namely, it is usually considered that they would receive more favorable financial support [49]. International supply chain finance also benefits financial sponsors as they not only receive profit from it but also expand their international relationships since the participants in the supply chain are spread across different countries [50].
Therefore, it is important to remember that the interaction between supply chain ratios and supply chain financing (SCF) is an important area of supply chain management. Analyzing the relationship between these two concepts can reveal how supply chain ratios influence financial strategies, operational decisions, and the overall performance of supply chain networks. The supply chain ratio is a financial performance indicator that measures how effectively a company manages its supply chain assets in relation to its operating results. While there are various ways to calculate this ratio, depending on industry and organizational priorities, one of the most common indicators is the cash to cash (C2C) cycle time. It measures the time it takes a company to convert investments in raw materials and inventory into cash from sales. The supply chain ratio, especially when measured over the C2C cycle, provides insight into the liquidity and efficiency of supply chain operations. The value of the supply chain ratio lies in its ability to highlight inefficiencies in cash flow management and opportunities for improvement. This allows companies to assess the financial health of their supply chains and develop strategies to reduce bottlenecks, reduce costs, and increase liquidity. Supply chain finance is a set of financial practices and tools designed to enhance capital flow in supply chains. It allows companies to optimize working capital by aligning financial incentives with operational needs. The main goal of SCF is to create a financially resilient supply chain that provides liquidity and stability for all stakeholders. This is achieved by addressing time gaps and cash flow inefficiencies reflected in supply chain relationships, especially in the C2C cycle [19,38,40,41].
After systematically analyzing the scientific articles related to the financial support, optimization, and liquidity of the international supply chain, it can be stated that the financing of the international supply chain is significant for all its participants. For the company, it means the management, optimization, cash flow planning, and systematic distribution of working capital and liquidity invested in supply chain processes and transactions. Meanwhile, for the supplier, it is especially relevant due to access to cheaper financial solutions and working capital financing, and for financial intermediaries, it is a way to develop their activities more broadly and create relationships with new partners.

3. Research Methods and Methodology

With the application of various models and methods, international supply chain finance has been examined in various aspects in the chain by various foreign scholars [34,35,36,37,38]. In order to achieve more accurate results of the research and greater reliability of the research, the aim of the paper was to apply correlation and regression analysis.
The purpose of applying correlation and regression analysis was to identify financing factors that determined the company’s international supply chain activities. By identifying the factors that had the greatest impact on a company’s international supply chain, the supply chain process that was most significant could be identified. Therefore, based on research and taking into account the specifics of the company’s activities, the independent variables and the dependent variable were selected.
Dependent variable:
  • Supply chain ratio (%). The supply chain ratio presented by Benigno et al. [54] was chosen because this ratio measures the efficiency of a company’s assets and cash generation. According to the authors, the higher this ratio is, the more efficient the company’s supply chain activities are. Consequently, the supply chain ratio can be used to evaluate the performance of the company’s supply chain.
Independent variables:
  • Turnover ratio of raw materials (times). The ratio was chosen based on the model presented by Lee et al. [55] as it evaluates the company’s cooperation with raw material suppliers. The turnover ratio of raw materials shows how many times per month the company’s raw materials are renewed. Consequently, the turnover ratio of raw materials allows the evaluation of the performance of the raw material supply process in the international supply chain.
  • Cost of sales (thousands of EUR). The cost price shows the costs incurred directly related to the production of goods. Therefore, this ratio is equivalent to the production cost ratio proposed by İncekara [56], which allows the evaluation of the performance of the production process in the international supply chain.
  • Turnover ratio of manufactured products (times). This ratio was chosen based on the model presented by Lee et al. [55] as it evaluates the company’s cooperation with customers. The turnover ratio of the unit’s production shows how many times a year the production makes a full turnover. Consequently, the turnover ratio of manufactured products allows the assessment of the performance of the customer service process in the international supply chain.
  • Turnover ratio of debts to suppliers (times). This ratio evaluates the company’s settlement with suppliers and is therefore used to assess the performance of the supply process in the international supply chain [57].
  • Turnover ratio of buyers’ debts (times). This ratio evaluates the settlement of buyers with the company as it shows how many times buyer debts make turnover. Therefore, it is used to assess the performance of the customer service process in an international supply chain [57]. The turnover ratio of debt to suppliers shows how many times per month the unit’s debts to suppliers make a turnover.
The cost of sales is presented in the company’s statements, so there is no need to calculate it. Other rates are calculated using the formulas presented in Table 2.
Thus, the selected independent variables were rates that evaluated the processes of planning, production, delivery of goods, and customer service. Therefore, the analysis included an assessment of supply chain performance.
Correlation and regression analysis was performed using the statistical data processing program R 4.1.3 software version. The analysis used the data of the company under study for the years 2014–2020. The research was carried out in 2022, so the years 2020–2014 were chosen. This period was sufficient to perform a correlational regression analysis. Variables from the reports of Vilnius Stock Exchange companies were useful for the research. No data normalization was used.

4. Results and Discussion

The research results are presented based on the example of a company operating in the dairy and dairy products industry. This company not only operated on the territory of the country but also exported goods to such countries as Poland, Latvia, Slovakia, the Czech Republic, Finland, and others (Figure 4). Hence, the activities of this company were carried out in the international supply chain.
The analysis of the division of the company’s sales volume by market revealed that from 2014 the export of milk and its products had a tendency to grow until 2018 and constituted an increasing structural share in the overall structure of sales volumes. From 2019, the share of exports in the structure began to decrease as the global COVID-19 pandemic had a huge impact on international sales flows, which had been significantly reduced for a while.
Distribution (probability distribution or distribution law) is a relationship between attribute values, or random variables, and their probabilities. A normal distribution is a distribution of continuous characteristic values (distribution law) that meets the conditions when the mean (μ), mode, and median values coincide; the distribution curve is symmetric; and the axis of symmetry is at the mean [58]. Thus, before starting to analyze the correlation of variables, it is necessary to find out whether the data dispersion of the y function is distributed according to the normal distribution. Performing the Kolmogorov–Smirnov test helps to check whether the real distribution corresponds to the normal distribution since the results obtained depend on which analysis methods will be applied further. The distribution analyzed in the research differed significantly from the normal one, if the obtained p value was lower than the established significance level (0.05). Using this criterion, the following hypothesis was formulated:
H0. 
The values of the variable are distributed according to the normal distribution.
H1. 
The values of the variable are not distributed according to the normal distribution.
As previously mentioned, the distribution analyzed in the research differed significantly from the normal one (Table 3), if the obtained p value was lower than the established significance level (0.05).
After carrying out the normality test, it was found that the probability of the Kolmogorov–Smirnov criterion for the supply chain ratio, turnover ratio of raw materials, turnover ratio of manufactured products, and turnover ratio of buyers’ debts was higher than the chosen confidence level of 0.05. Therefore, the H0 hypothesis was accepted, in which the data of these indicators were distributed according to the normal distribution and could be used in the regression analysis. It was also noticed that the indicators of the turnover ratio of debts to suppliers and the cost of sales were not distributed according to the normal distribution, because the p values of these indicators were lower than the significance level (0.05).
Based on the obtained results, it was found that there were exceptions in the data. According to Balabonienė et al. [59], in order to remove random fluctuations, data were often “logarithmized” to smooth them, but the values of these indicators were negative and could not be logarithmized. Thus, when approaching the values of indicators of turnover ratio of debts to suppliers and cost of sales to the normal distribution, they were squared.
As it can be seen from the results presented in Table 4, after raising the values of indicators of turnover ratio of debts to suppliers and cost of sales, the probability of their Kolmogorov–Smirnov criterion remained lower than the chosen confidence level of 0.05.
Distribution of values of the supply chain indicator, raw material turnover indicator, manufactured product turnover indicator, customer debt turnover indicator according to the normal distribution in 2014–2020 was presented in Figure 5.
Distribution of values of the turnover ratio of debts to suppliers (sq.) and cost of sales (sq.) in 2014–2020 was presented in Figure 6.
Thus, based on the results of the normality test, it was determined that the following indicators were suitable for regression analysis: supply chain ratio, turnover ratio of raw material, turnover ratio of manufactured products, and turnover ratio of buyers’ debts.
In order to determine how strong the influence of the selected performance indicators on the company’s supply chain was, a correlation analysis was performed. For normally distributed interval variables, the Pearson correlation coefficient was calculated. Spearman’s coefficient was calculated for interval variables that did not meet the assumption of normality. Evaluation of the correlation coefficient intervals was presented in Table 5.
Hypotheses were formulated during the correlation analysis:
H0. 
bxy = 0, i.e., thereis no linear connection.
H1. 
bxy ≠ 0, i.e., there isa linear connection.
After analyzing how the variables of the company’s supply factors were correlated (Table 6), it was observed that the supply chain ratio formed a strong connection with the turnover ratio of buyers’ debts (0.76) since it was a relative quantity calculated by dividing net cash flows by the difference between the company’s assets and current liabilities. The supply chain ratio had a weak connection with other variables and did not have a significant influence on each other. Further examining the turnover ratio of raw materials, it was noticeable that there was a strong correlation effect with the debt to suppliers (sq.) ratio; since the turnover ratio of debts to suppliers is often calculated from sales revenue in the same way as when calculating the turnover ratio of raw materials, this means that these quantities both depend on the volume of sales revenue. Examining the connection between the correlation of the turnover ratio of manufactured products and other indicators, it was observed that this indicator was not strongly influenced by other indicators, except for the supply chain ratio, which had a moderately strong connection. It was also observed that the correlation connection of the turnover ratio for debts to suppliers was the strongest with the cost of sales variable, as it was a relative value that was calculated from the cost of sales, since it was the cost price that was the closest evaluation value for debts to suppliers.
Correlation analysis, which is the first stage of statistical research, can only examine the interconnections of random variables. Further analysis of the statistical dependence of the company’s supply chain ratio and the factors affecting it (Table 7), which was determined based on the financial indicators of the dairy and dairy products company, showed that the turnover ratio of manufactured products and the turnover ratio of raw materials had the greatest influence on the indicators of the supply chain. Both of these indicators determined the efficiency of the company’s activities. Although previously examining the mutual influence of individual sizes, it was seen that the turnover ratio of buyers’ debts had the greatest influence on the supply chain ratio. In order to find out how they affected the result y in the general equation, multiple regression analysis should be applied, which determined the nature of the statistical relationship and described the dependence of the average values of the dependent (outcome) variable on the values of other independent variables in a mathematical formula.
The supply chain ratio = −6.258 × 101 − 1.537 × 100 (turnover ratio of manufactured products) − 1.483 × 10−9 (cost of sales (sq.)) + 6.216 × 10−2 (turnover ratio of buyers’ debts) + 1.445 × 10−2 (turnover ratio of debts to suppliers (sq.)).
After analyzing the estimates of multiple regression parameters, it can be seen from the presented research results (Table 8) that the p values of the coefficients were higher than 0.05, so it cannot be said that they were statistically significant in the application of this model. The calculated p value of the model F = 1.193 statistic was equal to 0.5981 < 0.05. After the regression analysis, the obtained results showed that the model was not suitable according to the formula F and the statistic p value.
In order to statistically correctly assess the company’s supply chain factors, it is recommended to choose a statistically significant and reliable model. For that purpose, the stepwise model selection function was used according to the AIC criterion, which automatically selected the most appropriate model by identifying variables that were statistically reliable and significant.
The equation for a statistically significant model is as follows:
Supply chain ratio = −7.94 + 0.07 (turnover ratio of buyers’ debts).
Thus, after performing the regression model repeatedly, it could be seen from the research data presented in Table 9 that only one variable—turnover ratio of buyers’ debts—was statistically significant in the equation. Analyzing the p value of the parameter estimates, it was observed that it was lower than the significance level of 0.05; therefore, it can be said that the turnover ratio of buyers’ debts was significant in the company’s supply chain equation. The calculated statistical p value of F = 201.6 for the model that was selected as the best fit based on the stepwise model function was 0.045, indicating that the model was appropriate.
Further analysis of the variables of the selected regression model showed that, as can be seen in Figure 7, the distribution of the turnover ratio of buyers’ debts variable was more scattered, but the data were purposefully arranged in a straight line; therefore, as it was determined from the parameters, the data of this variable was statistically more significant because it had fewer exceptions.
Thus, summarizing the supply chain of the sector under consideration, it was observed that the turnover ratio of buyers’ debts had the most significant influence on the activity, which showed the turnover of the amount of buyers’ debt from time to time, i.e., how many times the buyers’ debt “turned over” compared with sales during the selected time period. If a company were to maximize its supply chain performance, it would likely have to allocate financing to the turnover ratio of buyers’ debts. According to the obtained coefficients of the equation, it could be assumed that if the turnover ratio of buyers’ debts increased by EUR 0.07 thousand, the value of the company’s supply chain ratio decreased by EUR 1 thousand.
The performed analysis made it possible to identify that the turnover ratio of buyers’ debts had the greatest influence; according to the coefficients of the obtained equation, it could be assumed that if the turnover ratio of buyers’ debts increased by EUR 0.07 thousand, the value of the company’s supply chain ratio was EUR 1 thousand. The applied multiple regression model revealed that it was the data of the variable—the turnover ratio of buyers’ debts—that were statistically significant. The calculated F = 201.6 for the model, and the statistical p value was 0.045, which meant that the model was statistically significant. In order to assess whether the applied regression model statistically significantly reflected the obtained results, it was decided to apply heteroscedasticity and autocorrelation tests. Heteroscedasticity is when, as the values of the independent variables increase, the dispersion interval of the dependent variable remains constant. In other words, if the model errors become heteroscedastic, the resulting estimates are not efficient. A Breusch–Pagan test was performed to determine whether the dispersion of the turnover ratio of buyers’ debts in the fitted model was heteroscedastic. A hypothesis was put forward, whether the dispersion of the variable was heteroscedastic: if the p value is less than 0.05, the hypothesis is accepted; otherwise, it is rejected, and it can be said that the dispersion is homoscedastic. The Breusch–Pagan test revealed that the p value was 0.5473, which meant that the hypothesis was rejected, and it could be said that the dispersion of GDP was not heteroscedastic.
The main limitation of this research was that only the financing factors of the international supply chain of the dairy industry were evaluated. Research could also be conducted in other industries, and the obtained results could be compared. The further research should focus on these limitations, and academics are invited to test and develop the research model in other industries.

5. Conclusions

After conducting the analysis of the scientific works by Lithuanian and foreign authors related to the financial support, optimization, and liquidity of the sustainable supply chain, we can conclude that the financing of the international supply chain is significant for all its participants. For the company, this means working capital and liquidity management, optimization, cash flow planning, systematic distribution, and financial responsibility. Meanwhile, for the supplier, it is especially relevant due to access to more affordable financial solutions and working capital financing, and for financial intermediaries, it is a way to expand their operations and build relationships with new partners.
After analyzing the dairy and dairy products production company and evaluating the factors of the sustainable supply chain, it was found that the turnover ratio of buyers’ debts had the greatest influence; according to the obtained coefficients of the equation, it could be assumed that after the turnover ratio of buyers’ debts had increased by EUR 0.07 thousand, the value of the company’s supply chain ratio was EUR 1 thousand. The applied multiple regression model revealed that it was the data of the variable—turnover ratio of buyers’ debts—that were statistically significant. The calculated F = 201.6 for the model, and the statistical p value was 0.045, which meant that the model was statistically significant. In order to assess whether the applied regression model statistically significantly reflected the obtained results, it was decided to apply heteroscedasticity and autocorrelation tests.
The results of the heteroscedasticity and autocorrelation tests revealed that the fitted multiple regression model was significant and reliable. In summary, it can be claimed that the turnover of customers’ debts is the primary factor in the sustainable supply chain processes of the dairy and dairy products production company; therefore, in order to improve the indicators of the sustainable supply chain, the company should allocate more financing specifically to finance the turnover ratio of buyers’ debts.
Further research could be related to the modeling of solutions to the financing problems of factors determining the turnover ratio of customers’ debts.

Author Contributions

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

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are available upon request to the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Min, S.; Zacharia, Z.G.; Smith, C.D. Defining Supply Chain Management: In the Past, Present, and Future. J. Bus. Logist. 2019, 40, 44–55. [Google Scholar] [CrossRef]
  2. Koberg, E.; Longoni, A. A systematic review of sustainable supply chain management in global supply chains. J. Clean. Prod. 2018, 207, 1084–1098. [Google Scholar] [CrossRef]
  3. Medina, E.; Caniato, F.; Moretto, A. Framing Sustainable Supply Chain Finance: How can supply chain sustainability practices and supply chain finance solutions be integrated? J. Purch. Supply Manag. 2023, 29, 100837. [Google Scholar] [CrossRef]
  4. van Bergen, M.; Steeman, M.; Reindorp, M.; Gelsomino, L. Supply chain finance schemes in the procurement of agricultural products. J. Purch. Supply Manag. 2019, 25, 172–184. [Google Scholar] [CrossRef]
  5. Pellegrino, R.; Costantino, N.; Tauro, D. Supply Chain Finance: A supply chain-oriented perspective to mitigate commodity risk and pricing volatility. J. Purch. Supply Manag. 2019, 25, 118–133. [Google Scholar] [CrossRef]
  6. Dong, J.; Zhang, Q. Sustainable Development of the Real Economy: Supply Chain Finance and Enterprise Financialization. Sustainability 2024, 16, 3574. [Google Scholar] [CrossRef]
  7. Liu, H.; Wei, Z.; Hu, D.; Yang, J.; Linghu, D. Robust Financing Decisions of Green Supply Chain under Market Risk. Sustainability 2024, 16, 7942. [Google Scholar] [CrossRef]
  8. Zhao, H. Risk Management of Supply Chain Green Finance Based on Sustainable Ecological Environment. Sustainability 2023, 15, 7707. [Google Scholar] [CrossRef]
  9. Li, S.; Qu, S. The Three-Level Supply Chain Finance Collaboration under Blockchain: Income Sharing with Shapley Value Cooperative Game. Sustainability 2023, 15, 5367. [Google Scholar] [CrossRef]
  10. Wu, J.; Yue, L.; Li, N.; Zhang, Q. Financing a Capital-Constrained Supply Chain under Risk Regulations: Traditional Finance versus Platform Finance. Sustainability 2024, 16, 7268. [Google Scholar] [CrossRef]
  11. Dou, Y.; Zhao, J. The Impact of Supply Chain Finance on the Investment Efficiency of Publicly Listed Companies in China Based on Sustainable Development. Sustainability 2024, 16, 8234. [Google Scholar] [CrossRef]
  12. Wuttke, D.A.; Blome, C.; Heese, H.S.; Protopappa-Sieke, M. Supply chain finance: Optimal introduction and adoption decisions. Int. J. Prod. Econ. 2016, 178, 72–81. [Google Scholar] [CrossRef]
  13. Auboin, M.; Smythe, H.; Teh, R. Supply Chain Finance and SMEs: Evidence from International Factoring Data; WTO Staff Working Paper No. ERSD-2016-04; World Trade Organization (WTO): Geneva, Switzerland, 2016; pp. 2–29. [Google Scholar] [CrossRef]
  14. Wolter, A.; Goedegebuure, R.; Moeke, D.; Jansen, J. The Analytics Maturity of Logistics SMEs: Gaining a Deeper Understanding; Special Edition, Research group Logitics & Alliances; HAN University of Applied Sciences: Nijmegen, The Netherlands, 2021; pp. 116–127. [Google Scholar]
  15. Wu, C.; Xu, C.; Zhao, Q.; Zhu, J. Research on financing strategy under the integration of green supply chain and blockchain technology. Comput. Ind. Eng. 2023, 184, 109598. [Google Scholar] [CrossRef]
  16. Zhu, Q.; Zong, R.; Xu, M. Three-Party Stochastic Evolutionary Game Analysis of Supply Chain Finance Based on Blockchain Technology. Sustainability 2023, 15, 3084. [Google Scholar] [CrossRef]
  17. Rejeb, A.; Rejeb, K. Blockchain and supply chain sustainability. Sci. J. Logist. Logforum 2020, 16, 363–372. [Google Scholar] [CrossRef]
  18. Randall, W.S.; Farris, T.M. Supply chain finance: Using cash-to-cash variables to strengthen the supply chain. Int. J. Phys. Distrib. Logist. Manag. 2009, 39, 669–689. [Google Scholar] [CrossRef]
  19. Caniato, F.; Gelsomino, L.M.; Perego, A.; Ronchi, S. Does finance solve the supply chain finance problem? Supply Chain. Manag. 2016, 21, 534–549. [Google Scholar] [CrossRef]
  20. Liang, X.; Zhao, X.; Wang, M.; Li, Z. Small and Medium-Sized Enterprises Sustainable Supply chain finance Decision Based on Triple Bottom Line Theory. Sustainability 2018, 10, 4242. [Google Scholar] [CrossRef]
  21. Kabir, R.; Islam, A.; Marniati; Herawati. Application of blockchain for supply chain finance: Explaining the drivers using SEM. J. Open Innov. Technol. Mark. Complex. 2021, 7, 167. [Google Scholar] [CrossRef]
  22. Leuschner, R.; Choi, T.Y.; Rogers, D.S.; Hofmann, E.; Templar, S. ‘To fund’ as a new purpose of supply chain management: Making a case for supply chain finance. J. Purch. Supply Manag. 2023, 29, 100881. [Google Scholar] [CrossRef]
  23. Choi, T.-M. Supply chain finance using blockchain: Impacts on supply chains selling fashionable products. Ann. Oper. Res. 2023, 331, 393–415. [Google Scholar] [CrossRef]
  24. Lu, Q.; Zhou, Y.; Luan, Z.; Song, H. The effect of SMEs’ ambidextrous innovations on supply chain finance performance: Balancing effect and moderating effect. Int. J. Oper. Prod. Manag. 2024, 44, 424–461. [Google Scholar] [CrossRef]
  25. Azizian, M.; Sepehri, M.M.; Mirzapour Al-e-Hashem, S.M.J. Simulation-Based Models of Multi-Tier Financial Supply Chain Management Problem: Application in the Pharmacy Sector. Mathematics 2023, 11, 4188. [Google Scholar] [CrossRef]
  26. Su, L.; Cao, Y.; Li, H.; Tan, J. Blockchain-Driven Optimal Strategies for Supply Chain Finance Based on a Tripartite Game Model. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 1320–1335. [Google Scholar] [CrossRef]
  27. Li, X.; Yan, J.; Cheng, J.; Li, J. Supply-Chain Finance and Investment Efficiency: The Perspective of Sustainable Development. Sustainability 2023, 15, 7857. [Google Scholar] [CrossRef]
  28. Xiao, P.; Salleh, M.I.; Cheng, J. Research on Factors Affecting SMEs’ Credit Risk Based on Blockchain-Driven Supply Chain Finance. Information 2022, 13, 455. [Google Scholar] [CrossRef]
  29. Marak, Z.R.; Pillai, D. Relationship Between International Factoring and Cross-border Trade: A Granger Causality Approach. J. Appl. Econ. 2021, 15, 320–337. [Google Scholar] [CrossRef]
  30. Global Supply Chain Finance Forum. Corporate Payment Undertaking. 2021. Available online: http://supplychainfinanceforum.org/2021-Corporate-Payment-Undertaking.pdf (accessed on 3 September 2024).
  31. Jiang, Q.; Wang, N.; Jiang, B.; Bu, F. Relationship between supply chain disruption orientation and firm’s resilience in China: The dynamic capabilities perspective. Asia Pac. Bus. Rev. 2024, in press. [CrossRef]
  32. He, Q.; Wang, N.; Jiang, B. Hybrid closed-loop supply chain with different collection competition in reverse channel. Int. J. Prod. Econ. 2024, 276, 109371. [Google Scholar] [CrossRef]
  33. Bal, M.; Pawlicka, K. Supply chain finance and challenges of modern supply chains. LogForum Sci. J. Logist. 2021, 17, 71–82. [Google Scholar] [CrossRef]
  34. Alkaabi, A.; Nobanee, H. Sustainable Capital Market: A Mini-Review; Elsevier: Amsterdam, The Netherlands, 2020. [Google Scholar] [CrossRef]
  35. Lu, Y.; Sun, S.; Zhang, M.; Yang, Z. Moving Towards Sustainable Development: Can Supply Chain Finance Promote Corporate Green Innovation? J. Knowl. Econ. 2023, 15, 13001–13026. [Google Scholar] [CrossRef]
  36. Wang, N.; Jiang, Q.; Jiang, B.; He, Z. Enterprises’ Green Growth Model and Value Chain Reconstruction; Springer: Singapore, 2022. [Google Scholar]
  37. Jansen, J.H. Principles of Supply Chain Finance; Amazon: Seattle, WA, USA, 2017. [Google Scholar]
  38. Gelsomino, L.; Mangiaracina, R.; Perego, A.; Tumino, A. Supply chain finance: A literature review. Int. J. Phys. Distrib. Logist. Manag. 2016, 46, 348–366. [Google Scholar] [CrossRef]
  39. Zeng, K.; Xu, X.; Zhou, P.; Bai, Q. Financing the newsvendor with vendor credit line. Oper. Manag. Res. 2024, 17, 833–849. [Google Scholar] [CrossRef]
  40. Gong, Y.; Zhang, Y.; Alharithi, M. Supply Chain Finance and Blockchain in Operations Management: A Literature Review. Sustainability 2022, 14, 13450. [Google Scholar] [CrossRef]
  41. Rijanto, A. Blockchain Technology Adoption in Supply Chain Finance. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 3078–3098. [Google Scholar] [CrossRef]
  42. Yupat, M.; Rojniruttikul, N. Influences of Pressure and Green Supply Chain Management on Sustainable Performance of Green Manufacturing Industry in Thailand. Rev. Gestão Soc. Ambient. 2024, 18, e04973. [Google Scholar] [CrossRef]
  43. Seuring, S.; Gold, S. Sustainability management beyond corporate boundaries: From stakeholders to performance. J. Clean. Prod. 2013, 56, 1–6. [Google Scholar] [CrossRef]
  44. Liu, X.; Zhou, Q. Interactions between supplier encroachment format and pricing scheme with horizontal shareholding. Expert Syst. Appl. 2024, 256, 124934. [Google Scholar] [CrossRef]
  45. Jansen, J.; Tascher, A.; Beyer, H.-M. Supply Chain Finance in SMEs: A Comparative Study in the Automotive Sector in Germany and The Netherlands; KennisDC Logistiek: Zoetermeer, The Netherlands, 2018; pp. 58–81. [Google Scholar]
  46. Zhu, Y.; Xie, C.; Wang, G.-J.; Yan, X.-G. Predicting China’s SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods. Entropy 2016, 18, 195. [Google Scholar] [CrossRef]
  47. Jansen, J.; Montero, M.L.B.; Zom, L. Supply Chain Finance in the Coffee Market; ResearchGate: Berlin, Germany, 2020. [Google Scholar]
  48. Tchamy, J.; Peihua, F.; Osabutey, W.; Yoboue, W. Supply Chain Finance Analysis. Int. J. Acad. Res. Bus. Soc. Sci. 2018, 8, 385–395. [Google Scholar] [CrossRef]
  49. Feenstra, R.C.; Weinstein, D.E. Globalization, Markups, and U.S. Welfare. J. Pol. Econ. 2017, 125, 1041–1074. [Google Scholar] [CrossRef]
  50. Arsyida, T.; van Delft, S.; Rukanova, B.D.; Tan, Y. Trade and compliance cost model in the global supply chain. In Proceedings of the 12th Annual WCO PICARD Conference, Hammamet, Tunisia, 26 September 2017; pp. 1–22. [Google Scholar]
  51. Pawlicka, K.; Wlaszynowicz, M. Methods and performance measures of supply chain finance. In Proceedings of the 15th International Strategic Management Conference, Poznan, Poland, 27–29 June 2019; The European Proceedings of Social & Behavioural Sciences: London, UK, 2019; pp. 179–189. [Google Scholar]
  52. Hsieh, C.-C.; Chiu, J.-Z. System Review: A Text Analysis on Supply Chain Finance. Univers. J. Manag. 2019, 8, 29–32. [Google Scholar] [CrossRef]
  53. Pfohl, H.-C.; Gomm, M. Supply chain finance: Optimizing financial flows in supply chains. Logist. Res. 2009, 1, 149–161. [Google Scholar] [CrossRef]
  54. Benigno, G.; di Giovanni, J.; Groen, J.J.J.; Noble, A.I. The GSCPI: A New Barometer of Global Supply Chain Pressures; Staff Report No. 1017; Federal Reserve Bank of New York: New York, NY, USA, 2022. [Google Scholar] [CrossRef]
  55. Lee, H.-H.; Zhou, J.; Hsu, P.-H. The role of innovation in inventory turnover performance. Decis. Support Syst. 2015, 76, 35–44. [Google Scholar] [CrossRef]
  56. İncekara, M. Analyzing Resource Efficiency Methods in Turkey: The Impact on Production Cost and Sales. J. Manag. Econ. 2022, 29, 121–132. [Google Scholar] [CrossRef]
  57. Gadenne, L.; Nandi, T.K.; Rathelot, R. Taxation and Supplier Networks: Evidence from India. In Proceedings of the Annual Conference on Taxation and Minutes of the Annual Meeting of the National Tax Association, New Orleans, LA, USA, 15–17 November 2018; National Tax Association: Washington, DC, USA, 2019; pp. 1–31. [Google Scholar]
  58. Li, H.; Hou, J.; Hong, T.; Nord, N. Distinguish between the economic optimal and lowest distribution temperatures for heat-prosumer-based district heating systems with short-term thermal energy storage. Energy 2022, 248, 123601. [Google Scholar] [CrossRef]
  59. Balabonienė, I.; Bliekienė, R.; Stundžienė, A. Econometrics: Practical Applications of Regression and Time Series Models; Technologija: Kaunas, Lithuania, 2013. (In Lithuanian) [Google Scholar]
  60. Kasiulevičius, V.; Danapienė, G. Application of statistics in research analysis. Gerontologija 2008, 9, 176–180. (In Lithuanian) [Google Scholar]
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Figure 1. Supply chain finance management totality. Source: compiled by the authors of the paper based on [29,47].
Figure 1. Supply chain finance management totality. Source: compiled by the authors of the paper based on [29,47].
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Figure 2. Key areas of supply chain finance. Source: compiled by the authors of the article, based on [33].
Figure 2. Key areas of supply chain finance. Source: compiled by the authors of the article, based on [33].
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Figure 3. Supply chain finance (SCF) cube model. Source: compiled by the authors of the article based on [53].
Figure 3. Supply chain finance (SCF) cube model. Source: compiled by the authors of the article based on [53].
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Figure 4. Sales by markets (%) (2014–2020). Source: compiled by the author of the article.
Figure 4. Sales by markets (%) (2014–2020). Source: compiled by the author of the article.
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Figure 5. Distribution of values of the supply chain ratio, turnover ratio of raw materials, turnover ratio of manufactured products, and turnover ratio of buyers’ debts according to the normal distribution in 2014–2020. Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
Figure 5. Distribution of values of the supply chain ratio, turnover ratio of raw materials, turnover ratio of manufactured products, and turnover ratio of buyers’ debts according to the normal distribution in 2014–2020. Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
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Figure 6. Distribution of values of the turnover ratio of debts to suppliers (sq.) and cost of sales (sq.) in 2014–2020. Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
Figure 6. Distribution of values of the turnover ratio of debts to suppliers (sq.) and cost of sales (sq.) in 2014–2020. Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
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Figure 7. Distribution of the turnover ratio of buyers’ debts by supply chain indicator. Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
Figure 7. Distribution of the turnover ratio of buyers’ debts by supply chain indicator. Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
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Table 1. Distinguished benefits of supply chain finance.
Table 1. Distinguished benefits of supply chain finance.
Author and SourceBenefits of Supply Chain Finance
[49]It optimizes the management of working capital and liquidity invested in supply chain processes and transactions.
[50]The financial results of the company are improved, and most importantly, the risk in supply is reduced.
[37]The processes of purchasing goods/services become easier to implement, and payment is not only simpler but also faster.
[51]It helps to forecast cash flows and systematically distribute them.
[52]Strengthening relations with reliable partners.
Source: compiled by the authors of the article.
Table 2. Formulas for calculating the international supply chain and its factors.
Table 2. Formulas for calculating the international supply chain and its factors.
RateCalculation
Company’s supply chain ratio (%)Net cash flows from operations/(assets − current liabilities) × 100
Turnover ratio of buyers’ debts (times)Sales revenue/buyers’ debts
Turnover ratio of raw materials (times)Cost of sales/raw materials
Turnover ratio of debts to suppliers (times)Cost of sales/debts to suppliers
Turnover ratio of manufactured products (times)Cost of sales/manufactured products
Table 3. Values of the continuous characteristics of the company’s supply chain factors.
Table 3. Values of the continuous characteristics of the company’s supply chain factors.
Serial No.Factorp Value
1.Turnover ratio of raw materials0.3078
2.Turnover ratio of debts to suppliers0.0024
3.Cost of sales0.0046
4.Turnover ratio of manufactured products0.5463
5.Turnover ratio of buyers’ debts0.1432
6.Supply chain ratio0.5979
Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
Table 4. Squared values of the continuous characteristics of the company’s supply chain factors.
Table 4. Squared values of the continuous characteristics of the company’s supply chain factors.
Serial No.Factorp Value
1Turnover ratio of debts to suppliers (sq.)0.003397
2Cost of sales (sq.)0.000561
Source: compiled by the author of the article based on the statistical data processing program R 4.1.3 software version.
Table 5. Evaluation of the correlation coefficient (r value).
Table 5. Evaluation of the correlation coefficient (r value).
r ValueAssessment
0.00–0.19Very weak interconnection
0.20–0.39Weak connection
0.40–0.69Moderate connection
0.70–0.89Strong connection
0.90–1.00Very strong interconnection
Source: [60].
Table 6. Correlation of supply chain factors.
Table 6. Correlation of supply chain factors.
FactorsSupply Chain RatioTurnover Ratio of Raw MaterialsTurnover Ratio of Manufactured ProductsTurnover Ratio of Buyers’ DebtsTurnover Ratio of Debts to Suppliers (sq.)Cost of Sales (sq.)
Supply chain ratio1−0.08−0.530.76−0.18−0.09
Turnover ratio of raw materials−0.0810.19−0.09−0.88−0.9
Turnover ratio of manufactured products−0.530.191−0.32−0.12−0.25
Turnover ratio of buyers’ debts0.76−0.09−0.321−0.010.07
Turnover ratio of debts to suppliers (sq.)−0.18−0.88−0.12−0.0110.98
Cost of sales (sq.)−0.09−0.9−0.250.070.981
Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
Table 7. The dependence of the supply chain indicator on the factors affecting it.
Table 7. The dependence of the supply chain indicator on the factors affecting it.
Serial No.FactorEquationCoefficient of Determination (R2)
1Turnover ratio of raw materialsy = −0.23 turnover ratio of raw materials + 5.560.0072
2Turnover ratio of manufactured productsy = −1.89 turnover ratio of manufactured products − 20.260.2843
3Turnover ratio of buyers’ debtsy = 0.07 turnover ratio of buyers’ debts − 7.940.7662
4Turnover ratio of debts to suppliers (sq.)y = −0.01 turnover ratio of debts to suppliers + 14.550.035
5Cost of sales (sq.)Y = −1.1 × 10−10 cost of sales + 13.920.0075
Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
Table 8. Multiple regression model parameter estimates.
Table 8. Multiple regression model parameter estimates.
VariablesParameter EstimatesStandard ErrorStatistics tp Value
Constant−6.258 × 1014.962 × 101−1.2610.427
Turnover ratio of manufactured products−1.537 × 1001.978 × 100−0.7770.579
Cost of sales (sq.)−1.483 × 10−94.137 × 10−9−0.3590.781
Turnover ratio of buyers’ debts6.216 × 10−24.117 × 10−21.5100.372
Turnover ratio of debts to suppliers (sq.)1.445 × 10−22.165 × 10−10.0670.958
Turnover ratio of raw materials−2.246 × 1002.371 × 100−0.9470.517
Source: compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
Table 9. Parameter estimates for the rescaled regression model.
Table 9. Parameter estimates for the rescaled regression model.
VariablesParameter EstimatesStandard ErrorStatistics tp Value
Constant−7.947.92−1.0020.36
Turnover ratio of buyers’ debts0.070.022.6660.04
Source: Compiled by the authors of the article based on the statistical data processing program R 4.1.3 software version.
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Slavinskaitė, N.; Čižiūnienė, K.; Bundonytė, V. Assessment of the Sustainable Supply Chain Finance Factors. Sustainability 2025, 17, 1002. https://doi.org/10.3390/su17031002

AMA Style

Slavinskaitė N, Čižiūnienė K, Bundonytė V. Assessment of the Sustainable Supply Chain Finance Factors. Sustainability. 2025; 17(3):1002. https://doi.org/10.3390/su17031002

Chicago/Turabian Style

Slavinskaitė, Neringa, Kristina Čižiūnienė, and Vytautė Bundonytė. 2025. "Assessment of the Sustainable Supply Chain Finance Factors" Sustainability 17, no. 3: 1002. https://doi.org/10.3390/su17031002

APA Style

Slavinskaitė, N., Čižiūnienė, K., & Bundonytė, V. (2025). Assessment of the Sustainable Supply Chain Finance Factors. Sustainability, 17(3), 1002. https://doi.org/10.3390/su17031002

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