Supplier Performance Evaluation and Selection in the Herbal Industry

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1 Supplier Performance Evaluation and Selection in the Herbal Industry Rashmi Kulshrestha Research Scholar Ranbaxy Research Laboratories Ltd. Gurgaon (Haryana), India Dr. Manoj Kulshrestha Reader in Civil Engineering School of Engineering & Technology Indira Gandhi National Open University, New Delhi, India Prof. S. P. Bhatnagar Head, Pharmaceutical Sciences Birla Institute of Technology Mesra, Ranchi, India Raw materials are the most important component of many production processes. Finding the right raw materials, procured from the right source, at the right price, with the right quality, at the right time can be the key to the success of a production process. Selecting the right source of the raw materials for production of herbal medicinal products poses problems because of the large number of alternatives available, the presence of multiple and conflicting selection criteria, and an increasingly turbulent decision environment. This article presents the appropriateness of the Analytic Hierarchy Process (AHP), a modeling technique of Multiple Criteria Decision Making (MCDM), in supporting the evaluation and selection of suppliers in this industry. The article outlines the complete AHP technique from source selection through supplier rating for herbal drugs. Dr. C. K. Katiyar Head, Herbal Drug Research Ranbaxy Research Laboratories Ltd. Gurgaon (Haryana), India Introduction The procurement of the right kind of raw material from the right source is of vital importance in the production of herbal medicinal products. Quality, cost, continuity in delivery, and availability of supportive documentation are relevant criteria in the selection of suppliers. Makers of herbal drugs must depend on various suppliers with different characteristics in terms of quality, cost, and delivery. The supply base diversity makes the decision process for the selection of the right suppliers quite complex. Complexity is also associated with the fact that alternative sources or suppliers need to be evaluated in the presence of many conflicting criteria. This calls for a decision support system to help managers to make the right decisions in the presence of conflicting qualitative and quantitative parameters. Such a system can be based on the Multiple Criteria Decision Making (MCDM) process, which involves multiple and conflicting objectives or criteria and which often includes both qualitative and quantitative factors. MCDM is a broad and fertile field of research and application aimed at more efficient and effective decision making. Within MCDM, the Analytic Hierarchy Process (AHP) is a technique that can be applied if the decision problem includes multiple objectives, conflicting criteria, and/or incommensurable units. It aims at selecting an alternative from a known set of alternatives and/or requires establishing suitable rankings of all the available alternatives. This article attempts to analyze the decision environment for evaluating the performance of alternative sources or suppliers of herbal drugs by using the AHP technique. Application of AHP for supplier rating is described with the help of a case study involving six potential suppliers of five herbal drugs (active botanical ingredient). The ability of the AHP model to aggregate the different evaluation ratings into an overall perception of supplier performance (Supplier Supply Chain Forum An International Journal Vol. 8 - N

2 Performance Index, or SPI) is demonstrated. The Analytic Hierarchy Process The Analytic Hierarchy Process (AHP), axiomized by Thomas L. Saaty, is an MCDM technique particularly suitable for decision problems involving subjective, abstract, and non-quantifiable attributes. The strength of the AHP technique lies in its ability to structure a complex problem hierarchically. It allows the evaluation and ranking of available alternatives. To evaluate alternatives, the AHP technique is applied through the following steps: (a) develop the decision hierarchy, (b) establish priorities, (c) synthesize, and (d) determine overall priorities. In the AHP approach, the first step is to decompose the problem into its components and then use these components to develop a hierarchy with a number of levels, namely goal, attributes, sub-attributes, and ratings. This hierarchy serves as a framework for addressing both qualitative and quantitative factors simultaneously. The topmost element of the hierarchy stands for the overall goal in the decision problem. The level immediately below consists of sub-goals, which contribute to the attainment of the overall goal. Each sub-goal is decomposed further until a sufficiently detailed representation of the decision problem is obtained. In the case of the classical AHP, decision alternatives are put on the lowest level of the hierarchy, whereas in the AHP rating approach, the ratings occupy the lowest level of the hierarchy. After the problem is posed in a hierarchical form, the next step is to evaluate each element of the decision hierarchy. In this process, each node is evaluated against each of its peers with respect to its parent node. These evaluations are called pairwise comparisons. Pairwise comparisons at each level are made in terms of either importance or preference. Pairwise comparisons of the elements, usually attributes, sub-attributes, or ratings, can be made through a scale indicating the strength with which one element dominates another. The scaling process is then translated into priority weights (scores) for the comparison of all available alternatives. The next step is to form square decision matrices at each level after entering the choices of decision makers into the appropriate boxes of the matrices through pairwise comparison. Construction of the Square Decision Matrix The pairwise comparison procedure is carried out by relating an element, say attribute A, on the left side of the matrix to the element, say attribute B, on the top of the matrix, and then determining which is more important. If attribute A seems to be more important than attribute B, then a suitable integer value from Table 1 is entered in the box of the matrix. If B is more important than A, the reciprocal of that integer value is used. Thus, in this technique, two entries are made simultaneously, with the judgments at mutually transposed positions in the matrix. When an element is compared with itself, the unit weight factor is entered in the decision matrix. The procedure is repeated for each element listed along the left side of the matrix, comparing each with all the elements on the top of the matrix. The decision makers can express their preferences over attributes, sub-attributes, and ratings between each pair of elements as equally, moderately, strongly, very strongly, or extremely preferable by entering appropriate weight factors (given in Table 1). Computation of Priority Vector After the square decision matrix is constructed, it is suitably analyzed to compute the vector of priorities, or priority vector (PV). In terms of matrix algebra, the decision matrix analysis consists of determining the eigenvector of the greatest nonzero eigenvalue of the matrix and then normalizing it to 1.0 to obtain the priority vector. The row components of the priority vector give the priority weights of the elements. These priority weights are used for comparing and, subsequently, ranking the elements. Manual computation of Table 1 Scale of Relative Importance in the AHP Approach Supply Chain Forum An International Journal Vol. 8 - N

3 Table 2 Geometric Mean Approximation Approach the eigenvector of a matrix is not very difficult but is time-consuming for higher order matrices; therefore, analysis of the decision matrix is carried out by the geometric mean approximation technique, which provides sufficiently close results for application. In the geometric mean approximation approach, the geometric mean of the elements in each row of the square decision matrix is computed. This mean is used to normalize the vector so that its elements add to unity. This forms the column of priority vector. Each row component of this column vector corresponds to the priority weight of the element of that row. Table 2 shows the sequence of steps of the geometric mean approach of priority vector determination for an illustrative decision matrix involving three elements. Analysis of Consistency in Decision Making AHP, as a technique, has a distinct characteristic of having a provision to identify the degree of consistency in the judgments of the decision maker. It involves the computation of the greatest nonzero eigenvalue (λ max ), the consistency index, and, finally, the consistency ratio. For determining greatest non-zero eigenvalue (λ max ), the totals of each column of the decision matrix are found. Then, the sum of each column of the decision matrix is multiplied by the corresponding row component of the priority vector (i.e., the sum of column 1 is multiplied by the component of priority vector for row 1, and so on). The sum of the products so obtained is the maximum or principal eigenvalue (λ max ) of the decision matrix. The deviation of λ max from the order of the matrix (n) is regarded as the measure of inconsistency in judgments. The closer λ max is to the order n, the greater is the consistency exhibited by the matrix. In order to measure the relative consistency, a consistency index (CI) for a single matrix is introduced as λ max For a reciprocal matrix, it is always true that λ max > n. A random index (RI) is defined as a consistency index of a randomly generated reciprocal matrix. For matrices of different orders, the corresponding random consistency is presented in Table 3. Finally, a consistency ratio (CR) is defined as The value of the CR should be within 10% for good results, but generally, up to 15% is acceptable. If the CR is higher than the limit, then decision makers should consider revising their judgments. After analyzing and applying consistency checks for all the decision matrices, the local priorities (LP) are placed at suitable places in the hierarchy. The composite or global priorities (GP) of the rating (i.e., the lowest level of hierarchy) are determined by aggregating the weights through the hierarchy. The outcome of this process is the ratio scale priorities of various ratings used for this purpose. Finally, a rating spreadsheet can be used to capture the preferences of decision makers on each of the Table 3 Random Consistency Supply Chain Forum An International Journal Vol. 8 - N

4 alternatives, with respect to each sub-attribute under consideration. The ratings can be used to evaluate each alternative against each subattribute. Parameters for the Evaluation of Supplier Performance In this study, the following broad categories of attributes were considered for the supplier rating system: (a) Cost. The attributes within this category are basically productspecific attributes related to the price of the inputs. They include other associated costs such as material and transportation costs. (b) Performance. The attributes within this category (i.e., solvent extractive values, ash values, and foreign matter) are related to the quality of the inputs. (c) Logistics. The attributes within this category are basically supplier-specific attributes and include lead time, delivery reliability, small order supply, and emergency supply. These attributes pertain to the delivery mechanism under which a particular supplier operates and to the capability to respond to specific requirements of the organization such as small orders and emergency supply. (d) Other related attributes including standing of the firm, past performance, and support documentation. Only the performance attributes are specific to the herbal industry. The cost, logistics, standing of the firm, past performance, and support documentation attributes are general attributes. Attributes-Alternatives Listing In this study, product and supplierspecific attributes that are important for the purpose of performance evaluation of sources of herbal drugs are identified in Table 4. As explained earlier, the MCDM model requires a list of attributes as well as a set of alternatives to be evaluated. Having developed the list of attributes, a set of six supplier sources (represented by SS-1, SS-2, SS-3, SS-4, SS-5, and SS-6) has been taken for performance evaluation for the five herbal drugs, namely Bacopa monniera, Centella asiatica, Tinospora cordifolia, Gymnema sylvestre, and Commiphora mukul. For this study, for each herbal drug, at least three suppliers have been considered, as shown in Table 5. Step-by-step application of the AHP for Performance Evaluation In order to evaluate the performance of herbal drug suppliers using the AHP approach, various steps were taken. These are described in the following paragraphs. Develop the Decision Hierarchy The classic AHP approach is not capable of handling a large number of alternatives, as it results in highorder decision matrices that are difficult to analyze manually. Miller (1956) showed that it is not appropriate to conduct pairwise comparisons among more than seven elements at a time. Because the number of sources (i.e., suppliers) of various herbal drugs to be evaluated may be larger, the use of the AHP rating approach is recommended in this study. Table 4 List of Attributes and Sub-Attributes Supply Chain Forum An International Journal Vol. 8 - N

5 Figure 1 Pairwise Comparison of Attributes with Respect to Goal Appendix A illustrates measurement of the preference of the suppliers with respect to performance attributes, logistics attributes, and other attributes. The ratings used are Outstanding (O), Good (G), Satisfactory (S), and Poor (P). In the case of cost factor attributes, the ratings used are High (H), Average (A), and Low (L). Establish Priorities In this step, different square decision matrices are formed by pairwise comparisons. In this process, each node is evaluated against each of its peers with respect to its parent node. For example, Figure 1 shows a decision matrix resulting from comparing the preference of the attributes (Level 2) with respect to a goal (evaluate the sources of herbal drugs-level 1). For the decision hierarchy developed for the present case study, 18 such decision matrices were formed and analyzed as per the details given in Table 6. After the decision matrices are formed, the relative priorities of various elements of the model are determined. As shown in Appendix A, each element has two priorities, the Local Priority (LP) and the Global Priority (GP). The LP of an element is the relative priority of the element with respect to its peers. The GP is computed by multiplying the LP of the element by the GP of its parent element. For example, the LP of sub-attribute "Water Soluble Extractive (BB)" is and the global priority of "Performance (Quality) Attributes (B)" is 0.560; therefore, the GP of sub-attribute "Water Soluble Extractive (BB)" is (0.117 x 0.560) or Synthesize The global (composite) priorities of the rating (the lowest level of the AHP model in appendix A) are determined by aggregating the weights through the hierarchy. The outcome of this process is the ratio scale priorities of the various Table 6 Details of Decision Matrices Supply Chain Forum An International Journal Vol. 8 - N

6 ratings used for the purpose. For example, the GP of "Outstanding" with respect to sub-attribute "Water Soluble Extractive (BB)" with respect to "Performance (Quality) Attributes" with respect to "goal" is (0.473 x x x 1.00) or Determine the Overall Priorities The ratings are used to evaluate the achievement/performance of each source (supplier) against each subattribute. The ratings given to various suppliers with respect to considered sub-attributes for different herbal drugs are presented in Appendix B. For example, supplier SS-1 has been rated Average (A) with respect to material cost, whereas the same supplier has been rated Poor (P) in delivery lead time and delivery reliability. These ratings are translated into a priority factor (i.e., GP, determined in the previous step). A total priority for each source (supplier) under consideration can be computed by summing the priorities of the ratings of the source (supplier) on each sub-attribute. Overall ratings of suppliers under consideration can be determined by comparing "Total of Priorities of Ratings" as calculated and presented in Appendix C. In Appendix C, the row "Total of Ideal Priorities of Ratings" for a source (supplier) represents the sum of the numerical value of priority ratings of an ideal source (supplier) that has "outstanding/low" rating for all the sub-attributes. Finally, the "Supplier Performance Index (SPI)" for a particular herbal drug can be defined as the ratio between the "Total of Priorities of Ratings" of the supplier for the particular herbal drug and the "Total of Ideal Priorities of Ratings" calculated in the model. Results and Discussion The study illustrates the ability of the AHP to provide a rational and scientific approach to the performance evaluation of different sources (suppliers) of various herbal drugs. The computational results presented in Appendix C clearly show the importance of incorporating all influencing factors in performance evaluation. In Appendix C, the overall ratings of suppliers under consideration is given as I, II, and III for suppliers SS- 3, SS-1, and SS-2, respectively, for Bacopa monniera. Similarly, the overall rating of suppliers for Centella asiatica is given as I, II, and III for suppliers SS-4, SS-3, and SS-1, respectively. The overall ratings of suppliers of Tinospora cordifolia are given as I, II, III, and IV for suppliers SS-3, SS-5, SS-2, and SS-1, respectively. The overall ratings of suppliers for Gymnema sylvestre are given as I, II, III, and IV for suppliers SS-4, SS-2, SS-1, and SS-3, respectively. The overall ratings of suppliers of Commiphora mukul are given as I, II, III, IV, and V for suppliers SS-6, SS-3, SS-1, SS-2, and SS-4, respectively. These supplier ratings have been arrived at by comparing the "Total Priorities of Ratings" of all the suppliers considered for the supply of a particular herbal drug. On the basis of "Total of Ideal Priorities of Ratings" (i.e., in the present case), the "Supplier Performance Index (SPI)" for all the suppliers under consideration has been calculated, giving an overall evaluation of different sources through a single digit number. Ideally, this number would be 1.00 if the performance of a supplier is outstanding with respect to all attributes (criteria). Furthermore, a classification mechanism based on SPIs of different suppliers may be evolved to rate the performance of various suppliers. The SPIs of SS-3 and SS-5 for Tinospora cordifolia are very close to each other even though they have different ratings on various sub-attributes being evaluated. Similarly, the SPIs of SS-2 and SS-4 for Commiphora mukul are very close even though they have different ratings for various subattributes. This demonstrates the ability of the model to aggregate the different evaluation ratings into an overall perception of a particular supplier. Conclusions The AHP technique of MCDM is a very useful tool for evaluating the performance of different source (suppliers) of various herbal drugs. While implementing the model, a "Committee for Performance Evaluation of Suppliers" may be constituted, and the judgmental values of the members may be aggregated by the mean aggregation method. Various decision matrices may be formed by the aggregated judgmental values and can be analyzed suitably. Although some computer programs are available for computing the eigenvector of a decision matrix of the AHP, the use of an electronic spreadsheet like MS-Excel can make the calculations of rating spreadsheets an easy and less time-consuming task. In this method, the decision matrix is square, and advantage is taken of the properties of eigenvalues and eigenvectors of a square matrix to check the consistency of the judgmental values assigned to various elements in the decision matrix. The concept of consistency ratio in the AHP also makes the performance evaluation more scientific and rational. Decision makers may not desire the evaluation to be based on judgments that have such low consistency that they appear to be random. The procedure also gives the decision maker scope for revising and improving the judgments. The model developed is quite effective in determining the overall performance of any supplier by supplying just one number (i.e., the Supplier Performance Index, or SPI). Trend charts of SPI may show the historical performance of the supplier, which may be used for various administrative and financial purposes apart from the technical feasibility of using the supplier. Supply Chain Forum An International Journal Vol. 8 - N

7 References British herbal pharmacopoeia. (1996). British Herbal Medicine Association Publication. Indian herbal pharmacopoeia (Rev. new ed.). (2002). Mumbai: Indian Drug Manufacturer's Association Publishers. Kulshrestha, M., & Deshpande, V. B. (1996). Effective tool for functional evaluation of intricate cases in construction projects. Proceedings of the 36th SAVE International Conference, Society of American Value Engineers, Kulshrestha, M., Deshpande, V. B., & Saxena, A. K. (1995). Multiple criteria decision theory: An effective tool for value engineering implementation. Value Engineering in Project Management, Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for information processing. Psychological Review, Mukherjee, P. K. (2002). Quality control of herbal drugs An approach to evaluation of botanicals. New Delhi: Business Horizons Pharmaceutical Publishers. Saaty, T. L. (1980). The analytical hierarchy process. New York: McGraw- Hill. Saaty, T. L. (1982). Decision making for leaders. Lifetime Learning Publications/ Wadsworth. Saaty, T. L. (1986). Axiomatic foundation of the AHP. Management Science, 32(7), Supply Chain Forum An International Journal Vol. 8 - N

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