A Different Approach to Global Supplier Risk: A Finance Based Model

Kuresellesmeyle birlikte artan rekabet, cok uluslu sirketleri ve tedarikcilerini rekabet avantaji saglayabilmeleri amaciyla, ozellikle maliyet etkin cozumler bulmaya yonlendirmistir. Bu yonelim, isletmelerin uretimlerini kismi olarak daha dusuk maliyetli pazarlara kaydirmalarina neden olmustur. Ancak bu yaklasim, ozellikle tedarikte ongorulemeyen ve gomulu maliyetleri ve riskleri goz ardi etmektedir Bu nedenle “En Iyi Ulkeden Tedarik Stratejisi” gittikce onem kazanmaya baslamistir. Bu calismanin amaci, “En Iyi Ulkeden Tedarik Stratejisi” kapsaminda tedarikcilerin seciminde risklerinin belirlenmesine yonelik bir model onerisi sunmaktir. Olusturulan model ile 11 Avrupa Birligi ulkesinde yerlesik KOBI’leri etkileyen ulkeye ve firmalara ozgu mali riskler ve kuresel riskler 2006-2016 yillari arasinda bulanik mantik yontemi ile analize tabi tutulmustur.

The study aims to develop a model that measure the financial soundness of suppliers in selected countries by taking into account the financial risks of the country where the supplier is located and the financial risk factors specific to the firms and global risks. The developed model has been tested by fuzzy logic method which is highly effective in uncertainty and risk analysis.
In the model that is based on the best cost country supply consists of three main parts that evaluate the country risk as firm specific financial risk, national financial risk and global risk. Firstly, firm specific financial risk factors used in the study are analyzed by financial performance ratios related to supply chain performance in the financial statements of the supplier enterprises. Secondly, national financial risk factors are the variables indicating the macroeconomic risks of the supplier's country. Finally, global risk factors are based on the "Global Risk Report", which is announced annually by the "World Economic Forum". According to the report, 29 risks that should be taken into consideration in doing business globally are considered as the starting point of global risk indicators in the thesis. In the global risk analysis section, the probability and impact values of these risks are taken as basis.
The study distinguishes from studies that take into account macroeconomic and political risk characteristics of countries which are frequently used in the selection of supplier countries, by taking into account global risk factors and risk factors specific to the company.
The study consists of literature, data and methodology, findings, and finally the conclusion section, after the introduction.

Literature Review
The studies on the selection of supplier countries in the literature consist of studies on the total cost of ownership (TCO), low cost country sourcing and the best cost country sourcing. These are the main approaches in the literature for procurement. TCO and Low cost country sourcing are the approaches that are based on the cost savings.
TCO is an expression used to describe all costs associated with the acquisition, use and purchase of a good or service (Ellram and Siferd, 1993). TCO takes into account the costs of doing business with a supplier in general, rather than simply looking at the purchase price. Ellram (1993) allocates the TCO as pre-transaction costs, transaction costs and post-transaction costs. Alard and others (2009) designed a specific TCO concept that focuses not only on the supplier risk assessment but also on country risk assessment. They have shown that suppliers can obtain certain products more effectively from which countries. Meldrum (2000) states that all business transactions involve a risk. When commercial transactions go beyond international borders, they contain additional risks that are not available in domestic transactions. These additional risks, referred to as country risks, typically include the risks arising from various national differences in the economic and financial structure, policy, socio-political institutions, geography. Country risk analysis seeks to determine the potential of these risks to reduce the expected return on a cross-border investment.
Alphanumeric Journal Volume 7, Issue 3, 2019 Low-cost country supply is the use of global resources that focus on lower production costs and countries that have culturally and / or geographically important distance to the buyer (Kusaba, 2011). Low production cost consists of low raw material cost and labor cost (Dev, 2017). Due to the rise in commodity prices in most sectors, global sourcing strategy is considered to be a more proactive way for companies to have more competitive advantages. Lockström (2006) also showed that low-cost country sourcing is at the core of the phenomenon of globalization, and that low-cost countries use economies of scale, benefit from comparative advantages and take advantage of internal competitiveness, a way for foreign companies to increase their competitiveness. Some of the studies on low cost country sourcing is concerned with the strategic dimension of global supply. Hätönen andEriksson (2009) andJavalgi et al. (2009) discuss the strategic aspects of global supply in developing countries, while Hanna and Jackson (2015) demonstrate the strategic and operational impacts of global sourcing.
Another studies reveal the impact of low-cost country sourcing on the supply chain process and performance. These studies address different factors related to low-cost countries. These factors include the adequacy of employees (Kusaba et al., 2001), low-wage country sourcing (Ruamsook et al., 2009;Vos et al., 2016), management structure and resource zone distance (Schneider et al., 2013) logistics performance (Ruamsook et al., 2009;Kumar et al., 2010), total cost of ownership  and supply chain performance (Schneider et al., 2013).
Since the best-cost country supply is a new concept in the literature, it has not been defined yet. According to Dev (2017), the best cost country supply strategy does not consider only low material cost and low labor cost, but also long-term sustainability, environmental supply chain, logistics cost, suppliers' integration, selective demands and preferences for different products, as well as the macroeconomic characteristics of a country and new factors such as demography.
The starting point of the best country sourcing is not low material cost and low labor cost. Low production costs are an advantage for ideal best country supply. However, the best country sourcing also evaluates long-term risk factors such as political, macroeconomic, socio-demographic, environmental risk factors and sustainability.
Generally, there are three approaches to model risk and uncertainty for supplier selection Hamdi et al., 2018). The first approach employs fuzzy sets theory to incorporate inherent uncertainty to the decision model (Azadnia et al., 2015;Govindan et al., 2013a;Paul, 2015;Vahidi et al., 2018). Combined models of fuzzy multi-criteria decision making (MCDM) benefiting from the advantages and capabilities of different methods are very common in the supplier selection literature (Azadnia et al., 2015;Govindan et al., 2013a;Ju and Wang, 2013). Second most common modeling approach is scenario-based modeling, which welcomes randomness and utilizes stochastic problem parameters (Scottet al., 2015;Xanthopoulos et al., 2012;Vahidi et al., 2018). Finally, the third approach is using quantitative risk measures including the value at risk (VaR) and conditional value at risk (CVaR) (Fang et al., 2015;Sawik, 2013Sawik, , 2014. Fuzzy logic is used as a method in the study.

Data and Methodology
The model presented by the study analyzes supplier country risk with three main categories. The model presented by the study analyzes the supplier country risk with three main classifications as the firm specific risk, national financial risk and global risk.
The firm specific financial risk consist of the EBITDA over interest on financial debt, Return on Equity, Return on Assets, Inventories/Net turnover, Trade receivables/Net turnover, Trade Payables/ Net turnover, Operating working capital/ Net turnover ratios while Real GDP Growth (%), Annual Inflation Rate (%), Current Account as a Percentage of GDP, Foreign Debt as a Percentage of GDP, Current Account as a Percentage of Exports of Goods and Services, Net International Liquidity as Months of Import Cover, Exchange Rate Stability are indicators of national financial risk. The data used in the firm-specific financial risk analysis was obtained from the Banque de France database for 11 EU countries. Global risk factors are based on the Global Risk Report, which is announced from 2006 annually by the World Economic Forum (WEF). While national and firms specific financial risks indicate the risks that are present, the WEF Global Risk Report, which includes future global risks, is used to increase the explanatory power of the model. Because, WEF global risks are a projection for next 10 years. According to the report, there are 29 risks in five main categories, economic, geopolitical, environmental, social and technological, which need to be taken into account in doing business globally. The first five risks that should be taken into account in global business are considered as the starting point of global risk indicators in the paper. These risks include unemployment and underemployment, financial crises, failures of national governments, shocks in energy prices and social events. In the global risk analysis section, the probability and impact values of these risks are taken as basis. firm specific financial risk, national financial risk and global risk factors are analyzed on the basis of fuzzy logic method by considering 11 European countries including Austria, Spain, France, Germany, Czech Republic, Poland, Portugal, Slovenia, Italy, Denmark and Belgium, where the manufacturing sector balance sheets can be reached. Due to the correct output results in uncertain environment, fuzzy logic method was preferred in the analysis. The vast majority of studies in the literature on fuzzy logic are made in MATLAB fuzzy logic toolbox. In this study, MATLAB program was used in the analysis of risk factors by fuzzy logic method. In the fuzzy logic method, certain input values are defined in the program and the output results are obtained according to the appropriate rules.
The steps of method followed in the study is summarized below. 1st Step: Determination of input variables and value ranges and transfer to the system 2nd Step: Fuzzyfication of data by assigning membership functions and degrees in accordance with the range of input variables. Step: Obtaining outputs by means of fuzzy inference system according to / if rules In the context of the methodology followed, the risk levels of 11 European countries were determined as output, while firm specific financial risk, national financial risk and global risk factors were considered as inputs.
The inputs and value ranges used in each analysis group are given below; Inputs used for firm specific financial risk factors; One of the most critical points of the fuzzy logic method is the classical cluster and fuzzy set concepts. A group of objects, which can be tangible and / or intangible, is called a cluster. Object evaluation in the classical cluster concept is expressed as; In the concept of fuzzy sets, unlike the classical set concept, objects are evaluated according to their degree of being a member of the cluster. In this context, Zadeh (1965) describes the fuzzy cluster as a community of objects with a degree of uninterrupted membership. Accordingly, membership in fuzzy clusters is expressed as follows: S= {{( , µ ( ))| ∈ , µ ( ) ∈ [0,1]}} Another feature of the fuzzy sets is that the membership degrees are expressed with different functions according to the characteristics of the elements. For example, if only one of the variables is the full of the cluster and the others are members of different membership degrees, the membership degree is expressed by the triangle membership function.
Triangle membership function; If the triangle membership form is not sufficient to explain the variable, the crooked membership function can be used.
Crooked membership function; Membership ratings are based on subjective evaluation. Accordingly, the appropriate membership functions and value ranges of the inputs are shown in Table 2, Table 3 and Table 4 for three different analysis respectively. Membership functions and ratings defined for input variables should also be defined for supplier country risk as output variable. In the study, the results of the analysis are designed to be in a range of 0-100 in order to make the results more understandable and easier to interpret. Accordingly, as the output value approaches 0, the risk level will be low and the risk level will be higher as it approaches 100.

Findings
First of all, in the empirical part, firm specific financial risk analysis was performed using financial ratios related to the supply chain performance in the financial statements of the manufacturing sector. In the second stage of the analyzes, national financial risk of the countries was determined by using the variables included in the national financial risk indicators. In the last stage, the global risk analysis was performed by calculating the global risk compound index value consisting of the global risks, which are determined by the World Economic Forum and expected to be effective on the next 10 years, by multiplying the probability and impact values of the first five most effective risks.
The limitation of the study can be summarized as follows;  The study was carried out with only SMEs.
 In this study, three main risk factors (company specific financial, national financial and global) to be considered in the selection of suppliers are discussed.
 The study was implemented on 11 European countries due to limited access to data on sector financial statements used in the analysis of company specific financial risk factors.
 In addition, among the 29 global risk factors of WEF, the first five risk factors are taking into consideration for the analysis of global risk.
In this context, the results of firm specific financial risk analysis are given in Table 5 below.    According to the results of the global analysis risk in Table 7, Germany, Czech, Denmark, Austria, Slovakia, Poland are the lowest risk countries respectively.

Conclusion
The study aims to develop a model that measure the financial soundness of suppliers in selected countries by taking into account the financial risks of the country where the supplier is located and the financial risk factors specific to the firms and global risks. The developed model has been tested by fuzzy logic method which is highly effective in uncertainty and risk analysis.
The need to reduce the costs of enterprises has brought with them the demand to benefit from suppliers beyond their borders. "Global sourcing" is the answer to the question of how to meet this need. The aim of global sourcing is to take advantage of global advantages in the delivery of products and services. Countries should take Alphanumeric Journal Volume 7, Issue 3, 2019 account of open and hidden costs and risks while shifting their production and / or supply chains partially to cost-effective developing countries. The model that is based on the best cost country supply consists of three main parts that evaluate the country risk as firm specific financial risk, national financial risk and global risk. Firstly, firm specific financial risk factors used in the study are analyzed by financial performance ratios related to supply chain performance in the financial statements of the supplier enterprises. Secondly, national financial risk factors are the variables indicating the macroeconomic risks of the supplier's country. Finally, global risk factors are based on the "Global Risk Report", which is announced annually by the "World Economic Forum". As a result of the analysis, Germany, Czech and Poland are the lowest risk countries. The results of the study were compared with Global Resilience Index 2018, which is widely used in supplier country risk analysis and presented by FM Global. The 2019 FM Global Resistance Index is the only index that allows you to compare risks in approximately 130 countries. This index can help to make more informed strategic choices about regions, operations, supplier selection and institutional endurance. This index consists of three main categories as economic risk quality and supply chain. Risk quality score represents the rank of the country according to its natural hazard risks and cyber-attacks while supply chain risks are based on control of corruption, quality of infrastructure, corporate governance, and supply chain visibility.
Global Resilience Index results are given in Table 8. According to the Global Resilience Index, the three countries (Germany, Denmark and Austria), which are among the first countries in the risk ranking, are matched with the 3 countries in the top 5 are presented in the analysis results of our model. In addition, Germany, Czechia and Poland are considered as the least risky among the countries examined, and this result is compatible with European countries, with increasing investment attractiveness (Mc Kinsey&Company, 2015).
As a result of the study conducted by the Polish-German Chamber of Commerce and Industry with the participation of 300 foreign companies operating in Poland and 1700 investors in Europe, Poland is the second most preferred country by investors after Czech Republic. In addition, Poland and Czech are seen as a base for Asian investors such as South Korea and Japan, used to be included in the European market (Poland-in, 2018).