A Dynamic Model for Vendor Selection

Transcription

1 Feb. 2007, Volume 6, No.2 (Serial No.35) China-USA Business Review, ISSN , USA A Dynamic Model for Vendor Selection ZHU Xue-zhen (School of Business, Soochow University, Suzhou , China) Abstract: In the ever-changing world, vendor selection is useful in supply chain management. Existing models supporting vendor focus on short-term but neglect the time axis. This paper proposes a dynamic model based on AHP and BSC for long-term strategic vendor selection problems. A case study was used to illustrate how to select the partnership supplier through the Expert Choice software. Key words: balanced scorecard (BSC); analytic hierarchy process (AHP); dynamic model As competitive pressures increase, organizations have increasingly restructured their operations by focusing on their core strategically important activities and outsourcing their peripheral operations. According to a recent survey carried out by Accenture, 80% of the companies survey use some form of outsourcing and a majority of these companies are spending close to 45% of their total budget on outsourcing. Moreover, the practice of outsourcing is expanding in both scope and sophistication. It used to be the case that outsourcing only involves non-core activities with the purpose of helping firms to reduce costs and concentrate on their core competency. Nowadays outsourcing has become an universal phenomenon in every area of business, such as engineering, research and development (R&D), new product development, and marketing. Outsourcing also plays a strategic role by helping firms to acquire new capabilities, to bring about fundamental changes to managerial strategy and organizational structure, and to facilitate the transformation of business models. Many firms are looking to outsourcing for a variety of benefits, from cost savings to increased flexibility, and from improvement in service quality to better access to the state-of-the-art technology. As outsourcing has increased, vendor selection problem is an area of tremendous importance in the effective management of a supply chain. 1. Literature Review Vendor selection or evaluation is a common problem for acquiring the necessary materials to support the outputs of organizations. The problem is to find and to evaluate periodically the best or most suitable vendor(s) for the organizations based on various vendors capabilities. This usually happens when the purchase is complex, high-dollar value, and perhaps critical. A process of formal vendor evaluation and ranking is also necessary. The process for vendor selection is indeed a problem-solving process, which covers the work of problem definition, formulation of criteria, qualification, and choice. Since the 1960s when Dickson suggested 23 different selection criteria, the analysis of criteria for selecting vendors has been the focus on a large number of publications. The approaches summarize three kinds of models. Cost-based method is proposed by Timmermann E for vendor selection, all of the costs associated with each ZHU Xue-zhen (1966- ), female, Master, lecturer of School of Business, Soochow University; research fields: managerial accounting, accounting information system, cost accounting. 75

2 potential vendor are calculated and the least expensive vendor will be selected. Although managers frequently declare quality to be the most significant criterion, many firms select their vendors on cost or delivery conditions. Linear weighting method proposed by Wind and Robinson for vendor selection decision is the most common way of rating different vendors on the performance criteria for their quota allocations. Gregory linked this approach to a matrix representation of data and then rated the different vendors for their quota allocations. Monozka and Trecha proposed multiple criteria vendor service factor ratings and an overall supplier performance index. Mathematical programming models use the techniques of linear programming, mixed integer programming, and goal programming to determine the vendor selection and order quantity decisions simultaneously. The purpose of mathematical programming models is to select several vendors in order to minimize or maximize an objective function subject to both vendor and buyer constraints. Chaudry et al consider the vendor selection problem under quality, delivery, and capacity constraints as well as under price-break regimes. Kasilingam and Lee present a single-period vendor selection model regarding fixed cost of establishing a vendor. One drawback of the above approaches is neglecting multi-period planning horizon and non-financial criteria for vendor selection, and many decision makers or experts select vendors based on their experience and intuition. These approaches are obviously subjective and their weaknesses are clear. To overcome these problems, we use balanced scorecard and analytic hierarchy process to propose the decision model. 2. A Dynamic Analytic Hierarchy Process Model The Analytic Hierarchy Process is a powerful and flexible decision making process to help people set priorities and make the best decision when both qualitative and quantitative aspects of a decision need to be considered. By reducing complex decisions to a series of one-on-one comparisons, then synthesizing the results, many researchers have concluded that AHP is a useful, practical and systematic method for vendor rating and has been applied successfully. But one of the AHP s limits is decision model should structure the complete hierarchy which reflects all frameworks of goal. For completely estimating the performance of enterprises, balanced scorecard (BSC) is useful. The BSC promotes the balanced pursuit of objectives in four key areas: customers, finance, internal business processes, and learning and growth. The balanced scorecard provides summary-level data emphasizing the most critical to the company in each of the four areas. 1.1 Propose the dynamic hierarchy framework Decompose the decision problem into a hierarchy with a goal at the top, criteria and sub-criteria at levels and sub-levels and decision alternatives at the bottom of the hierarchy in a series of periods. A dynamic hierarchy structure is as Figure 1. The notations are defined as Table Construct the decision matrix Based on Saaty s nine points scale, the decision maker uses the fundamental 1-9 scale, the assessment of 1 indicates equal importance, 3 moderately more, 5 strongly more, 7 very strongly and 9 indicates extremely more importance. The values of 2, 4, 6, and 8 are allotted to indicate compromise values of importance. The decision matrix involves the assessments of each alternative in respect to the decision criteria in a series of periods. If the decision making problem consists of n criteria and m alternatives, the elements {dijk} signify the rating of the ith alternative in respect to the jth criteria in kth period. The decision matrix takes the form: 76

3 d11k d 21k Dk = dm1k d d d 12k 22k m2k d1nk d 2nk dmnk K=1 Goal K=2,3,4 C F I L C C C C F F F F I1 I2 I3 I4 L L L L A1 A2 A3 Figure 1 The dynamic hierarchy framework for vendor selection Table 1 The notations of the dynamic hierarchy framework Major framework Attributes (C1) Number of customers complaint (C2) Response speed to customers Customers (C) (C3) Customer profitability (C4) Market share (F1) Unit price (F2) Quality Finance (F) (F3) Return on investment (F4) Economic value added (I1) Facility and technology (I2) Number of on-time deliveries Internal business process (I) (I3) Responsiveness to customer needs (I4) Flexible (L1) Employees training hours (L2) Knowledge sharing Learning and growth (L) (L3) Professionalism of salesperson (L4) Quality of relationship with vendor 2.3 Set the priorities with pairwise comparison matrix This step involves the comparison in pairs of the elements of the constructed hierarchy in each period. The aim is to set their relative priorities with respect to each of the elements at the next higher level. The elements {a ijk } will satisfy the following conditions: a ijk =1/a jik and a iik =1 with i, j=1, 2, n. In the comparison matrix, a ijk can be interpreted as the degree of preference of ith criteria over jth criteria in kth period. 77

4 a11k a12k a1nk a21k a22k a2nk A = k am1k am2k annk 2.4 Verify the consistency of decision AHP calculates a consistency ratio to reflect the consistency of decision maker's judgments during the evaluation phase. The consistency ratio in both the decision matrix and in pairwise comparison matrices could be calculated with the equation: CI=(λ max -N)/(N-1); CR=CI/RI The closer the inconsistency index is to zero, the greater the consistency. The consistency of the assessments is ensured. The relevant ratio should be lower than 0.10 to accept the AHP results as consistent. If this is not the case, the decision-maker should go back to Steps 2 and 3 and redo the assessments and comparisons. 2.5 Use the software of Expert Choice to determine the optimal selection All the calculations about comparison matrix of priorities and selection of alternatives are common in mathematics. We have made use of the Expert Choice software, which is a multi-objective decision support tool to select vendor. 3. An Illustrative Example This numerical example presents a mathematical model to select suppliers in a multi-period. 3.1 Define the criteria for vendor selection Table 2 The attributes pairwise comparison matrix in kth period (k=1, 2, 3, 4) Customers Financial Internal business process Learn and growth C (1,1,1,1) (5,5,5,4) (5,5,5,4) (3,2,1,1) F - (1,1,1,1) (1/2,1/2,2,2) (2,1,1/4,1/3) I - - (1,1,1,1) (2,1,1/4,1/3) L (1,1,1,1) C1 C2 C3 C4 C1 (1,1,1,1) (1,2,3,4) (1/2,1/2,1/3,1/4) (1/7,1/7,1/3,1/4) C2 - (1,1,1,1) (1,1,1/3,1/4) (1/6,1/7,1/3,1/5) C3 - - (1,1,1,1) (1/5,1/5,1,1) C (1,1,1,1) F1 F2 F3 F4 F1 (1,1,1,1) (2,2,2,3) (1,1/3,2,3) (1,1/2,2,4) F2 - (1,1,1,1) (1/3,1/3,1/3,1/4) (1,1,1,1) F3 - - (1,1,1,1) (2,1,1,2) F (1,1,1,1) I1 I2 I3 I4 I1 (1,1,1,1) (2,1,2,2) (2,2,2,3) (1/3,1/3,1/3,1/4) I2 - (1,1,1,1) (1,1,1,2) (1/5,1/4,1/4,1/5) I3 - - (1,1,1,1) (1/4,1/5,1/5,1/6) I (1,1,1,1) L1 L2 L3 L4 L1 (1,1,1,1) (2,2,2,3) (3,3,3,4) (2,2,2,3) L2 - (1,1,1,1) (2,2,2,3) (1,1,1,2) L3 - - (1,1,1,1) (1,1,1,1) L (1,1,1,1) 78

5 For the evaluation process of the vendors, 16 different criteria are defined under the BSC framework by a team from the purchasing department. To build the model and to calculate the weights, Expert Choice is used. The Expert Choice software is a simple easy-to-use package for constructing decision models using the super matrices of the AHP. The attributes pairwise comparison matrix in 4 periods is illustrated in Table 2. The attributes pairwise comparison matrix of alternatives in kth period (k=1, 2, 3, 4) are illustrated in Table 3. Table 3 The attributes pairwise comparison matrix of alternatives in kth period (k=1, 2, 3, 4) A1 A2 A3 A1 (1,1,1,1) (4,3,2,2) (4,4,1,1) A2 - (1,1,1,1) (2,1/3,1/3,1/5) A3 - - (1,1,1,1) 3.2 Calculate the priorities of the criteria and verify the consistency of the matrices After inputting all data above into the Expert Choice software, we can get the final results as illustrated in Figure 2. Meantime, we can calculate the consistency ratio for each comparison matrix, all the consistency ratio is less than 0.1. The author would not like to list the entire ratio due to limited space. Figure 2 The priorities of the criteria in 1-4 periods 3.3 Analyze the priorities of BSC in different periods As we can see in Figure 3, the priority of customer perspective becomes relatively less important because of quality improvement of whole vendor s service, however, the priority of learning and growth perspective gets more important, and the priorities of financial perspective and internal business process perspective keep almost at the same level in the multi-periods. Table 4 The priorities of BSC in period 1-4 C F I L Priority weights Priority weights of BSC Figure 3 The priorities of BSC in period Analyze the priorities of vendors in different periods Periods C F IL 79

6 From Figure 4, we know the trend of vendor s priority, vendor A1 is the most suitable vendor in period 1 for company A. But A3 is the best choice in the long-term period. The trend of synthetic scores Table 5 The priorities of vendors in period 1-4 A A A The synthetic scores Period A1 A2 A3 Figure 4 The priorities of vendors in period Conclusions In the trend of global supply chain management, enterprises try to select a suitable vendor and cooperate over a long period of time. The attributes and weights may change in time axis under the changeable business environments. Traditional multiple attributes decision making methods are hard to solve the long-term performance measurement problems. This paper proposes a dynamic approach based on AHP and BSC for vendor selection problems. The analytic hierarchy is structure by the 4 major frameworks including customers, finance, internal business processes, and learning and growth. In future work, we will try to extend AHP method to ANP method to analyze the interdependence between criteria. References: [1] Chaudhry, S.S., Forst, F.G. and Zydiak, J.L.. Vendor Selection with Price Breaks. European Journal of Operational Research, 1993, 70: [2] Karpak, B., Kumcu, E. and Kasuganti, R.. An Application of Visual Interactive Goal Programming: A Case in Vendor Selection Decisions. Journal of Multi-criteria Decision Analysis, 1999, 8: [3] Lee, E.K., Ha,S. and Kim, S.K.. Supplier Selection and Management System Considering Relationships in Supply Chain Management. IEEE Transactions on Engineering Management, 2001, 48: [4] M.R. Leenders, H.E. Fearon, A.E. Flynn and P.F. Johnson. Purchasing and Supply Management, 11th edition. McGraw-Hill, NY, [5] C. Muralidharan, N. Anantharaman and S.G. Deshmukh. A Multi-criteria Group Decision Making Model for Supplier Rating. Journal of Supply Chain Management, 2002, 38 (4): [6] Chan, F.T.S.. Interactive Selection Model for Supplier Selection Process: An Analytical Hierarchy Process Approach. International Journal of Production Research, 2003, 41: [7] Muralidhara, C., Anantharaman, N. and Deshmukh, S.G.. A Multi-criteria Group Decision Making Model for Supplier Rating. Journal of Supply Chain Management, 2002, 38: [8] Chan, F.T.S. and Qi, H.J.. A Fuzzy Basis Channel-spanning Performance Measurement Method for Supply Chain Management. Journal of Engineering Manufacture, 2002, 216: (Edited by Jimmy and Gavin) 80