Customized Supplier Selection Methodology: An Application of Multiple Regression Analysis

Transcription

1 Customized Supplier Selection Methodology: An Application of Multiple Regression Analysis Nikhil Chandra Shil East West University Sophisticated manufacturing process demands a strong supplier base for satisfying customers with a quality product at a cheaper price. Thus, supplier selection has been shown to be significant in supply chain management literature, where many research studies have been conducted. Technology-led and sophisticated production processes and bitter competition may be the reasons that attract practitioners to select from a dedicated list of promising suppliers. Most of the previous studies on this subject have concentrated on the selection of either the criteria or methods used to choose the right supplier(s). This paper also addresses these two issues. It focuses on the methodology of selecting the right supplier(s) from a list of suppliers. Criteria have been chosen in line with the requirements of the firm and a multiple regression analysis has been used as a statistical tool to choose the right supplier(s). Here, criteria have been translated into three different indexes from different perspectives, and ultimately supplier selection is based on the index values and their interrelationships. This is the addition to the current state of knowledge in which suppliers' perspective is also considered strategically at the time of selecting the right suppliers. This paper concludes that suppliers' performance largely depends on experience and satisfaction. Additionally, buyers should pay close attention to these two factors to select the right supplier. This paper considers these two factors through the development of two different indexes comprising relevant criteria under each of the factors in which the practitioners enjoy enough flexibility to make it customized. Introduction The supplier is one of the five forces of competitive position, because of its bargaining power, as described by Porter's (1980) Competitive Forces Model. The study on supplier selection even started long before that during 1960s. The business strategy previously used by suppliers became so unpredictable that a new technology, just in time (JIT), was developed so that manufacturing plants could own their suppliers or that suppliers were outsourced under the memorandum of understanding (MoU) that they would always be ready to supply the materials required by the manufacturing plant at the time they needed them. Still, JIT is a dream for most of the manufacturing units across the globe and the dependency on suppliers increased to a greater extent due to the scarcity of varieties and quality of raw materials. Greater dependence on suppliers increases the need of managing suppliers effectively. Three dimensions underlie supplier management: (1) effective supplier selection, (2) innovative Supply Chain Forum An International Journal Vol N

2 supplier development strategies, and (3) meaningful supplier performance assessment mechanisms. Traditionally, the selection of a supplier is often based on price. The cheapest supplier is usually selected without taking into consideration additional costs this supplier may introduce into the value chain of the purchasing organization. Thus, the costs related to unreliable delivery, limited quality of goods supplied, and poor communication usually are not involved in the selection process. However, many studies conducted in the area of criteria identification conclude that supplier selection is based on different interactive criteria, and multi-criteria decision-making techniques become commonplace in supplier selection methodology. Supplier selection decisions are complicated by the fact that various criteria must be considered in the decision-making process. The analysis of criteria for selection and measuring the performance of suppliers has been the focus of many academicians and purchasing practitioners since the 1960s. In his seminal work, Dickson (1966) suggests, "From the purchasing literature, it is fairly easy to abstract a list of at least 50 distinct factors (characteristics of vendor performance) that are presented by various authors as being meaningful to consider in a vendor selection decision" (p. 5). During recent years, supply chain management (SCM) and the supplier/vendor selection (VS) process have received significant attention in business management literature (Verma & Pullman, 1998). SCM can be defined as a set of approaches used efficiently to integrate suppliers, manufacturers, warehouses, and stores so that merchandise can be produced and distributed at the right quantities to the right locations and at the right time in order to minimize system-wide costs while satisfying service level requirements. The introduction of products with short life cycles and heightened expectations of customers have forced business enterprises to invest in and focus attention on their supply chains, probably due to the vigorous competition in today's global market. In addition, the development of technology actually plays an important role in motivating the evolution of the supply chain and of the techniques to manage it. In general, a supply chain consists of suppliers, manufacturing centers, warehouses, distribution centers, retail outlets, as well as raw materials, work-in-progress inventory, and finished products that flow between the facilities. Suppliers' performance largely depends on experience and satisfaction. Individual businesses no longer compete as autonomous entities but rather join a supply chain alliance due to the current state of highly competitive business situations. Therefore, suppliers, manufacturers, logistic companies, and retailers in the SC always forge stronger alliances, vertically or horizontally, to compete against other supply chains (Lin & Chen, 2004). A supply chain is an integrated process wherein a number of various business entities (i.e., suppliers, manufacturers, distributors, and retailers) work together in an effort to: (1) acquire raw materials/components, (2) convert these raw materials/components into specialized final products, and (3) deliver these final products to retailers'' (Beamon, 1998, p. 281). One of the competencies essential to supply chain success is an effective purchasing function (Cakravastia & Takahashi, 2004; Giunipero & Brand, 1996; Porter & Millar, 1985). In most industries raw materials and component parts constitute the main cost of a product; in some cases it can account for up to 70% of the total cost (Ghobadian et al., 1993). In high-technology companies, purchased materials and services represent up to 80% of the total product cost (Burton, 1988; Weber et al., 1991). The raw material purchased for most U.S. firms constitutes 40-60% of the unit cost of a product. For large automotive manufacturers, the cost of components and parts purchased from outside vendors may total more than 50% of sales. Coal purchases for large electric utilities, such as TVA, approach $1 billion annually (Bender et al., 1985). Savings in cost of materials purchased may be very insignificant if considered separateby, but it may bring competitive advantage in market place by ensuring 'value for money' to the consumers. The current research aims to evaluate how to select the right vendor from a list of vendors within a corporate set up. The author was privileged to have the scope of using the internal database of a multinational corporation (MNC) to apply the methodology. The name of the MNC is intentionally hidden and a hypothetical name, Company X, is used for reference. Company X is a subsidiary of a UK company that operates in Bangladesh and holds 43% of the market share in the thread industry. It is a recommended, by one and all, threads manufacturer and market leader in supplying industrial sewing thread. It imports basic raw materials such as yarn, dyes and chemical, plastic cones, spare parts, and machineries for the purpose of producing thread. The company maintains a list of both local and foreign suppliers; this study is based on the record of 38 suppliers from their database. This paper analyzes vendor selection methodology used in a company based on different indexes. The indexes are designed from criteria to make the number of parameters manageable. Different criteria (similar in nature) have Supply Chain Forum An International Journal Vol N

3 been grouped under the same index. The indexes are used to form a regression equation and statistics are used to make the equation generalized within the company for every supplier. Traditionally regression analysis helps to identify the relationship among variables. Similar to supplier selection we depend on criteria, which are considered here as variables, and a logical relationship among the variables is established. The initial model considered the maximum database of the company and so the result of the model may be used as a reference point for other similar situations. X Company can use the model in the future by putting the index values into the model. The higher the value is, the more effective the performance of the vendor is. The methodology as applied here is rarely used in the supplier selection in literature so this paper adds to the current knowledge. Additionally, the methodology offers a strategic use because it shows a way to customize so that everybody has a sufficient scope to adjust to the typical requirements of an agent under consideration that is very much situational and considers contextual variables like cost, quality, timings, commitments and so on. formal decision models can assist purchasers in a variety of ways when selecting suppliers. Their study, however, involved buyers' receiving explanation and assistance while using the models and little is known about what actually happens if formal methods are applied incorrectly. Without any doubt, supplier selection is one of the decisions that determine the long-term viability of a company (Thompson, 1990). However, as mentioned in the introduction, different actors from varying perspectives look differently at formal supplier selection methods. These differences could lead to a number of problems, namely identifying (1) the criteria or the basis of how the suppliers are tested for ultimate selection and (2) the suitable tools or models to be used for selecting the supplier. Dickson (1966) has become a reference for the majority of papers dealing with supplier or vendor selections. Dickson's study was based on a questionnaire sent to 273 purchasing agents and managers selected from the membership list of the National Association of Purchasing Managers. Twenty-three criteria were ranked with respect to their importance observed during the early 1960s. At that time, the most significant criteria were quality of the product, on-time delivery, performance history of the supplier, and the warranty policy used by the supplier. Weber et al. (1991) presents a classification of Table 1 Criteria for Vendor Selection (Author's Personal Compilation) Brief Literature Review Supplier selection has attracted significant attention from academics and practitioners alike because of its perceived importance, its visibility (at least in the sense that the ultimate outcome is identifiable), and its suitability for formal, mathematical modeling. Many academic papers describe and compare various formal decision methods, decision elements, and quantitative and qualitative decision criteria for supplier selection, for example, De Boer et al. (1998), Narasimhan (1983), and Weber and Current (1993). De Boer et al. (2001) present a review of decision methods reported in the literature for supporting the supplier selection process. De Boer and Van der Wegen (2003) conclude on the basis of four empirical experiments that Supply Chain Forum An International Journal Vol N

4 all the published papers (since 1966), according to the studied criteria. The result, based on 74 papers, shows that price, delivery, quality, and production capacity and location are the criteria most often addressed in the literature. Overall, the 23 criteria presented by Dickson still cover the majority of the criteria presented in the literature until today. Ellram (1990) proposes three principal criteria: (1) the financial statement of the supplier, (2) the organizational culture and strategy of the supplier, and (3) the technological state of the supplier. For each one of these three criteria, the author presents several subcriteria. In another study Barbarosoglu and Yazgac (1997) distinguish three principal criteria: (1) the performance of the supplier, (2) the technical capability and financial strength of the supplier, and (3) the quality system of the supplier, and propose some subcriteria such as those discussed in Ellram (1990). A list of vendor selection criteria with reference to different research work is given in Table 1. approach (Ghodsypour & O'Brien, 1998; Weber & Current, 1993; Weber et al., 1991) 3. Probabilistic methods (Soukup, 1987) 4. Other methods: activity-based cost approach (Roodhooft & Konings, 1996), total cost of ownership (Degraeve et al., 2000), transaction cost theory (Qu & Brocklehurst, 2003), fuzzy logic approach (Bevilacqua & Petroni, 2002), integrated AHP and linear programming (Ghodsypour & O'Brien, 1998), visual interactive goal programming Karpak, Kumcu, et al., 1999), expert system approach (Vokurka et al., 1996; Yigin et al., 2007), genetic algorithm (Kubat & Yuce, 2006) The problem of supplier selection is not new. Before supply chain management became a buzz phrase, supplier selection was discussed under the term vendor selection. In addition to Weber et al. (1991), Ghodsypour and O'Brien (1998) also provide a short but insightful overview of the supplier selection research. Interested readers should refer to these two papers for more information. In this Table 2 Summary of Supplier Selection Research (Author's Personal Compilation) Once the criteria are selected and set, it becomes necessary to select suitable approaches. Most of the approaches as used in supplier selection are quantitative in nature. Weber et al. (1991) grouped the quantitative approaches to supplier selection into three categories: linear weighting models, mathematical programming models, and statistical/probabilistic approaches. However, to solve the supplier selection problem, existing methods can be broadly classified into four principal categories. A method, of course, can be the combination of the elementary methods presented following. 1. Elimination methods (Crow et al., 1980; Wright, 1975) 2. Optimization methods a) Without constraints: AHP (analytical hierarchic process) approach (Golden et al., 1989) b) Subject to a set of constraints: mathematical programming Supply Chain Forum An International Journal Vol N

5 paper, supplier selection research is classified into four major categories; a few representative publications are listed in Table 2. In Table 2, one can see that quite a few researchers treat supplier selection as an optimization problem, which requires the formulation of an objective function. Because not every supplier selection criterion is quantitative, usually only a few quantitative criteria are included in the optimization formulation. Ghodsypour and O'Brien (1998) recognize the problem and proposed an integrated method using AHP and linear programming to deal with both qualitative and quantitative criteria. Table 3 Indexes with Respective Criteria Table 4 Different Forms Filled Up by Different Persons Most of the supplier selection literature focuses on the buyer's perspective. Choi and Hartley (1996) consider the influence of a buyer's position in the supply chain on supplier selection. Other perspectives such as those of the supplier, the researcher, and the government are considered to a lesser extent. The government perspective, for instance, is often seen just as a constraint in the selection of suppliers. Munson and Rosenblatt (1997) describe local government rules and develop models to select suppliers while satisfying these rules. Thus, this research is intended to fill up the gap by bringing the suppliers' point of view into consideration to make the final decision more accurate and worthy. Suppliers themselves will be more serious when they become aware that their performance also depends on their activities. Earlier research dealt with the selection of criteria affecting suppliers' performance and the use of those criteria for developing models to supplement the selection process. In this research, a relationship among the criteria has been made and tested through the model. It indicates which users to select based on the strength of variables that produce significant results. Methodology The primary objective of this paper is to develop a supplier selection methodology from an agent's perspective. Secondary objectives are to identify different criteria, grouping related criteria to form three different indexes, establishing relationships among the indexes through regression, and using the results of regression and index values to choose the best supplier from a list of candidates. To achieve the objectives, both primary and secondary data have been used. A detailed literature survey was conducted to identify the criteria and available tools used for vendor selection. On the basis of the selected criteria, three different indexes were formed and used for a statistical model. The indexes with the respective criteria are given in Table 3. A total of 16 criteria have been considered in the study: seven criteria are used for the vendor performance index (VPI), five for the vendor satisfaction index (VSI), and the remaining four for the vendor experience index (VEI). To collect information, three different forms have been used with different persons being responsible for filling in the forms to avoid response bias (see Table 4). A total of 38 suppliers have been selected randomly from the list of vendors for the study. Initially two hypotheses were tested: H1: Foreign suppliers are more efficient and have a more satisfactory performance than local suppliers. H2: Among local suppliers, more experienced and satisfied ones have better performances than inexperienced and dissatisfied ones. In line with the hypotheses as identified, the researcher developed three different forms with different questions and criteria that ultimately led to developing three different indexes. These indexes have been used for all 38 suppliers selected for the study. SPSS 15.0 was used for data manipulation and frequency; test of association and regression analysis were used as tools for analysis. Analysis and Findings Criteria Considered Types of vendor: This survey was conducted on two categories of vendors: local and foreign. Local vendors are locally registered Supply Chain Forum An International Journal Vol N

6 Bangladeshi companies and foreign vendors are those registered in foreign country but operate in Bangladesh through their local branch office. These results show that most of the respondents included in the survey were local vendors because they were easy to get in touch with. However, a number of foreign vendors, around 26%, were also included in the survey. Product knowledge: Product knowledge is an important factor for better performance. About 32% of the vendors considered in the study have satisfactory product knowledge. If the vendor's knowledge of the product is poor, it will cause a lot of trouble for the buyers in terms of cost and quality. Proactiveness in handling issues: Buyers want suppliers to address any problematic issues as quickly as possible. Otherwise, buyers will lose trust and confidence in the level of commitment of the supplier. Around 30% of the respondents in the study agree that suppliers should be speedy enough to handle any query of the buyer. Completion timeliness: The time required to ship the goods to the buyer on receiving the order (lead time) is an important yardstick to evaluate vendor performance. The lower the lead time is, the better the performance is. This is an important criterion to consider when placing an order. Satisfaction in dealing with vendor staff: The interpersonal qualities and professional skills of the vendor staff carry a lot of meaning when selecting vendors. Some studies have considered this behavioral criterion from different perspectives (Buffa & Jackson, 1983; Monczkaet al., 1981; Soukup, 1987) like firm characteristics, vendor's characteristics, value of the order, risks attached in dealings etc. Access to vendor: At a time of emergency, the buyer needs an easy and sometimes informal access to the vendor just to reduce the lead time. According to the researchers, many factors affect this easy access such as geographical location (Ansari & Modarress, 1986; Burton, 1988; Dickson, 1966), service (Bernard, 1989), communication system (Dickson, 1966), desire for business (Soukup, 1987), and so on. Vendor's ability to resolve problems: Very often problems occur regarding product specifications, delivery date, payment terms, and so on. A vendor's ability to solve such problems is considered as an important criterion of vendor performance. Many researchers consider problem solving important about issues such as technical capability (Hahn et al., 1986; Soukup, 1987), labor relations record (Dickson, 1966; Monczka et al., 1981), training aids (Dickson, 1966), operational controls (Burton, 1988), production facilities, and capacity (Bragg & Hahn, 1989; Browning et al., 1983). Quality of product and services of the vendor: This criterion requires no clarification and justification. Almost every researcher (almost 98%) considers this criterion an important measure for evaluating vendor performance. Buyers are always concerned about quality because of the huge pressure from the end consumers. Pricing: Similar to quality, pricing is also a common criterion considered in most of the studies (Ansari & Modarress, 1986; Bender et al., 1985; Bernard, 1989; Buffa & Jackson, 1983; Cardozo & Cagley, 1971; Dickson, 1966). Vendors who charge the least often ultimately get the order. This is due to the need to minimize the cost of production. Overall satisfaction with the vendor: Considering all perceived criteria, the overall satisfaction level of the buyer with reference to each vendor under the study was analyzed. This criterion is used to test the response bias of the respondents. The ultimate result concludes that the respondents were not biased when responding to the questions. Overall satisfaction of vendor with the buyer: In this study, the researcher was privileged to be able to include the vendors' perspective in the ultimate selection process. Vendor performance is also determined by billing, payment procedure, behavior, time to receive money, terms and conditions, and so on. All of these criteria lead to the development of a VSI that is used in the model formulation. Experience of the vendor: Experience in terms of years, number of orders handled, number of problems resolved, and annual turnover are also important issues that set the performance level. The researcher also considers all of these factors related to experiences to develop the VEI used in the model. Test of Association between Different Variables A test of association between different variables has been made to determine the important factors of vendors' performance. A contingency table is constructed to test the degree of association between different important variables. Even though the statistical significance of the association is commonly measured by a chi-squire statistic, a chi-square test could not be used in this analysis because almost all the elements in contingency table are higher than 2 x 2 tables. Therefore, a contingency coefficient was used here because it can assess the strength of association in a table of any size. The following information shows a contingency coefficient of some of the important variables that were copied from the SPSS output (crosstabulation is intentionally avoided). In every case, the contingency coefficient shows a high association Supply Chain Forum An International Journal Vol N

7 between variables except in the last one. Even though it is assumed that there would be a strong relationship between proactive in handling issues and problem resolution, these findings show a moderate relation between the two variables. Therefore, it cannot be concluded that those vendors who are proactive are also good in problem resolution. The ability to resolve problems may also depend on a vendor's amount of human and capital resource and the experience and expertise of the staff. Regression Analysis In line with the hypotheses to be tested, a multiple regression analysis with one dependent and three independent variables was conducted. The list of variables with their operational definitions is given in Table 5. Considering the previous variables' definitions and notations, a regression model is presented as follows: Y = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + ε.. (eq. 1) Here, β 0 is the intercept of the regression line, β 1, β 2, β 3 are the coefficient of respective independent variables, and ε represents the random error with the mean zero. Assumptions of the model: The model is based on the assumption that other variables that have influences on the VPI, such as economic condition, political situation, labor productivity, and so on, is constant. To use an ordinary least-square method, further assumptions were made regarding some standard clauses that are known as a classical group of assumptions, as shown in the following: 1. X ι 's fixed or X ι 's independent of ε ι 's. 2. ε ι 's random variables with mean = V(ε ι ) = Ε [ε ι - Ε (ε ι )] 2 = σ 2 Ε is constant. 4. Ε ι 's are independent of each other. 5. There is no set of nonzero constant C 0, C 1,, C κ such that, C 0 + C 1 X 1 + C κ X κ = O. So, X 1,, X κ are linearly independent. Regression model: Data was collected on the 38 vendors through different means like questionnaire survey, interviews etc. as mentioned in Table 4 and have been manipulated and analyzed using the SPSS program, resulting the following regression equation: Y = -1,45 = 0,864X 1 + 0,371X 2 +0,702X 3.. (eq. 2) As per the model, the intercept of the line (β 0 ) becomes means, and if all the independent variables take the value zero, the VPI will be If other things remain the same, the VPI for foreign vendors (β 1 ) is higher than for local vendors. If the VEI increases by 1, on an average, the VPI will increase by an additional (β 2 ), and if the VSI increases by 1, on an average, the VPI will increase by an additional (β 3 ). Except for VEI, the other two independent variables have a strong relationship with VPI. Hypothesis Testing In this section of regression analysis, the methodology of hypothesis testing is used to test a null hypothesis to find out whether the combination of all the variables is a useful predictor of the dependent variable. Other tests of hypothesis for partial regression coefficients (that is, H0: j = 0) are used to determine if a specific independent variable is conditionally important in the multiple regression model. By using the student's t statistics and p- value it is concluded whether or not a particular predictor variable is conditionally significant, given the other variables in the regression model. The following two tables provide the required value for the hypothesis test and interval estimation. Table 5 Identification of Variables and Notations with Respective Definitions Test of significance for the whole regression: A null hypothesis is tested to find out whether the independent variables are useful predictors of the dependent variable aggregately. Null Hypothesis H n : β 1 = β 2 = β 2 = 0 Alternative Hypothesis H a : At least one β j = 0 (j = 1, 2, 3, 4, 5) Decision Rule: Null hypothesis will be rejected if F cal > F tab F cal = 5.88 F tab = F k,n-k-1,α = F 3,38-3-1,0.05 = F 3,34-1,0.05 = 2.90 Supply Chain Forum An International Journal Vol N

8 Here, F cal > F tab and therefore the null hypothesis that all coefficients are zero against the alternative hypothesis at a 5% level of significance is rejected. The p-value or the smallest significance level at which the null hypothesis can be rejected is So, the entire variable as a whole can predict the dependable variable and the combined effect of these variables do improve the model. Test of hypothesis for each of the coefficients: Four null hypotheses have been tested for four different variables and the result is given in the following table. Among the four hypotheses, two are not rejected and two others are rejected. 1. The null hypothesis regarding intercept (β 0 ) is not rejected against the alternative hypothesis at a 5% level of significance with the two-sided test but the p-value or the lowest level of significance at which the null hypothesis can be rejected is The null hypothesis regarding β 1 is not rejected against the alternative hypothesis at a 5% level of significance. Therefore, the customer type is not a statistically significant predictor of the dependent variable (VPI); however, the p-value or the smallest significance level at which the null hypothesis can be rejected is The null hypothesis regarding β 2 is rejected against the alternative hypothesis at a 5% level of significance; however, the p-value or the smallest significance level at which the null hypothesis can be rejected is This indicates that the vendor's experience status is a statistically significant variable for predicting vendor's performance. 4. The null hypothesis regarding β 3 is rejected against the alternative hypothesis at a 5% level of significance; however, the p-value or the smallest significance level at which the null hypothesis can be rejected is This indicates that the vendor's satisfaction status is a statistically significant variable for predicting vendor's performance. Confidence interval Another way of determining whether a specific independent variable is important in the multiple regression models is to find the confidence interval. This section estimates confidence intervals at 95% for β 0, β 1, β 2, β The 95% confidence interval for the Y-intercept (β 0 ) ranges from to Here the 95% confidence interval for β 0 includes 0, and thus the two-tail hypothesis that the intercept coefficient is 0 cannot be rejected. Based on this confidence interval, it can be concluded that the intercept is not a statistically significant predictor in the multiple regression model. 2. The coefficient for X 1 has a 95% confidence interval -0.51< β 1 <2.24. Here the 95% confidence interval for 1 includes 0, and thus the two-tail hypothesis that this coefficient is 0 cannot be rejected. Based on this confidence interval, it can be concluded that vendor type is not a statistically significant predictor variable in the multiple regression model. 3. The coefficient for X 2 has a 95% confidence interval 0.05< β 2 <0.79. As the confidence interval does not include 0, the two-tail hypothesis that the coefficient is 0 is rejected. Based on this confidence interval, it can be concluded that the vendor's experience is a statistically significant predictor variable in the multiple regression model. 4. The coefficient for X 3 has a 95% confidence interval 0.09< β 3 <1.32. As the confidence interval does not include 0, the two-tail hypothesis that the coefficient is 0 is rejected. Based on this confidence interval, it can be concluded that the vendor's satisfaction is a statistically significant predictor variable in the multiple regression model. Analysis of Residual The normality plot indicates an approximate linear relationship, thus, it is impossible to reject the assumption of normally distributed residuals (see Figure 1). Findings Hypothesis tests of the coefficients of this model indicate that the independent variables VEI and the VSI are significant whereas another independent variable, the dummy variable, is not statistically significant. The confidence interval analysis also supports these findings. Therefore, the findings indicate that a vendor's performance is positively related with a vendor's experience and satisfaction. However, the findings also indicate that there is no clear distinction between local and foreign vendors in terms of their performance. Thus, it is imperative for the supply chain managers of the company to include suppliers' experience and satisfaction as important factors for the final model. Supplier selection should be based on the performance. And as per the model, performance becomes a function of experience and satisfaction. Thus, every prospective candidate for selection should be required to complete the two indexes, the VEI and the VSI. Later on, the value should be put in the model so that it ends up with a value indicating the performance level. The higher the value of performance is, the more the Supply Chain Forum An International Journal Vol N

9 Figure 1 Analysis of Residuals vendor will be selected. The company may also run the general regression in regular interval to test whether there are any changes in variables and their interrelationships and adjust the model accordingly. Limitations This study is conducted from a buyer's perspective. The assumption is that the buyer has a certain number of years of experience with several suppliers. Only then, the buyer may use such a method for selecting the best supplier from the list of suppliers. The success of regression analysis depends on the availability of data covering a large period of time. Again, the linear relationship among the variables is assumed here to make the methodology simple. In this study, a total of 16 criteria were used to form three different indexes and then a relationship was made among these three indexes through a regression model. Other than the two independent variables included in this model, there may be some other variables that have been omitted that are important predictors of VPI. Omitting them from this model causes speciation bias, which may be the reason for the poor explaining power of this model. The calculation of VPI, VEI, and VSI is somewhat controversial because the dimensions of each variable are chosen arbitrarily and the weights to each variable are also given arbitrarily. Moreover, there may be some other omitted dimensions that are useful to measure these variables. The model may be an illustrative one and any buyer may customize it by including different criteria depending on the situation. For this specific buyer, it is informative that vendor performance largely depends on vendor experience and satisfaction. Therefore, the buyer should give more weight considering these two factors when selecting the right vendor for the maximum performance. One positive element of the study is that vendors' satisfaction is also considered as an important predictor for evaluating vendors' performance, which is rare in the vendor selection literature. Inclusion of new variables, formation of new indexes, and explaining non-linearity are some areas for future research opened by this study. Conclusion Supplier selection essentially deals with the selection of the right supplier and the quota allocation (Kaur et al., 2008), which also needs to consider a variety of vendor attributes such as price and quality (Junyan et al., 2008). Thus, it becomes a common issue in the multi-criteria decision-making process of every company. With the development of twenty-three criteria by Dickson (1966) in the 1960s, further research extended the list. Criteria on the basis of what the vendor selection rests ultimately depend on the company and the product in perspective. This paper, for example, deals with the identification of the right supplier from a dedicated list of suppliers on the basis of their experience and satisfaction with the buyers. It is statistically proved that suppliers' performance is explained by experience and satisfaction. This paper also rejects the hypothesis that the performance of foreign suppliers is better than local suppliers. There is sufficient scope for researchers to identify the subcriteria of the experience and satisfaction; for example, the paper proposes four sub-criteria for experience and another five subcriteria for satisfaction. These are ultimately considered as criteria for defining the level of performance of suppliers in the form of different indexes and interrelationships. Whatever may be the criteria selected or methodology used, this paper considers the suppliers' perspective at the time of selection. It proved that the performance level of suppliers also depends on the extent of their satisfaction with the buyers. Some suppliers may have different performance level expectations for different buyers due to the changes in satisfaction across the buyers. Thus, it is also imperative that to enjoy maximum performance from the suppliers the buyers should try to keep them highly satisfied. It is important that a company has a more fully committed and dedicated supplier base than its competitors, however insignificant may be the savings, in terms of cost of material, for example. The personnel responsible for the supplier selection will benefit from incorporating this issue into their current vendor selection methodology. Supply Chain Forum An International Journal Vol N

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