Lean Supplier Selection: A Data Envelopment Analysis (DEA) Approach

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1 Lean Supplier Selection: A Data Envelopment Analysis (DEA) Approach Masoud Rahiminezhad Galankashi* Department of Material, Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor, Malaysia address: Masoud.rahiminejad@yahoo.com, Rgmasoud2@live.utm.my Muhammad Hisjam Department of Industrial Engineering, Faculty of Engineering Sebelas Maret University Surakarta, Indonesia Syed Ahmad Helmi Department of Material, Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor, Malaysia Abstract The main aim of this study is to propose an incorporated model to evaluate suppliers using a lean manufacturing perspective. Although lean manufacturing has achieved an extensive amount of attention in previous studies, its integration with supplier selection is less investigated. To fill this gap, this study was conducted to systematically propose an integrated approach to select suppliers based on lean related attributes. Particularly, four performance perspectives (cost, quality, lead time and service level) in addition to 16 related metrics were designated as substitutes for leanness. The extracted metrics in addition to supply variety and quality of distribution were used as the inputs and outputs of DEA model, respectively. The proposed model was solved and some managerial implications were then suggested. Keywords Lean Manufacturing; Supplier Selection; Data Envelopment Analysis I. INTRODUCTION Within the new global market, managers have recognized the prominence of choosing the most proper suppliers [1-4]. A proper supplier should supply the requirement of the companies with their preferred quality and in a planned time [5-7]. As the supply chain progresses, companies have more appropriate tools to assess their suppliers [8, 9]. Although the supplier assessment and selection have been deliberated by numerous scholars [8-10], proposing strategic (e.g. lean) supplier selection frameworks and integrating them to mathematical models is less investigated. In addition, over processing (as one of the most important wastes of lean manufacturing) in the assessment of suppliers enforce many expenses to companies. Having this background, suggesting, categorizing, quantifying, and employing the lean related metrics to assess and select the suppliers is a challenge for numerous scholars, practitioners, and managers [11-14]. In spite of the available literature about the significance of assessing suppliers based on adopted strategies, this is less happening in the real world problems [15]. It is partly due to the accessibility and difficulty of available metrics for the aim of supplier assessment. In addition, financial issues are still attaining more concentration compared to balanced approaches [3, 16, 17, 33]. However, considering definite strategic performance metrics for the aim of supplier selection is beneficial [4, 8, 18, 19]. Some reasons of its importance are discussed as follows. In real world problems, managers are desired to have the most proper metrics to assess and select their suppliers. Performance metrics should be classified in definite categories. This assists managers to allocate diverse weights and apply their desires in supplier selection process. Mathematical approaches are specific tools to mix all the supplier evaluation criteria and see the problem in an integrated perspective. Therefore, developing a lean based DEA model to evaluate and select the suppliers is the major concern of this paper. This study attempts to address the subsequent research questions: 1. What are the major perspectives of lean manufacturing to evaluate suppliers? 2. What are the different metrics of lean supplier selection perspectives? 3. How a DEA model can be used to evaluate the performance of suppliers? 985

2 The scope of this study is limited to Iran s semiconductor industry. However, the proposed metrics and the research framework can be cooperative to scholars and practitioners from other case studies who aims to select their supplier based on lean attributes. This research contributes to proposing a new lean-based supplier selection framework for along with addressing a DEA model to put the proposed metrics in practice. It presents a novel idea for the integration of specified metrics of lean supply chain with mathematical models. From the theoretical and methodological standpoint, based on our best of knowledge, this research also contributes to provide a new idea in this area since very few studies have been accompanied before. The rest of this paper is organized as follows. Section 2 is a review of recent works on supplier evaluation and selection. A summary of research methodology is discussed in section 3. Section 4 discusses the DEA model. Section 5 provides the lean supplier selection criteria. Remaining sections are model development, supplier evaluation, results and discussion and conclusion, respectively. II. LITERATURE REVIEW There are three key issues related to the objective of this research, namely lean manufacturing, supplier selection and its approaches. In this section, we elaborate each issue separately. A. Lean Manufacturing (LM) and Lean Supply Chain (LSC) Supply chain is the process of delivering the product to customers from raw material to last stage. Lean manufacturing (LM) is a concept used in many supply chains all around the world [20, 21]. It promises important compensations in terms of enhanced management and supply chain communication and incorporation [22]. LM desires to produce the products based on the exact demand of customer and with little or no waste [23]. Lean concepts, known as waste reduction and continuous improvement, assist managers to seek for excellence. Integrating these two concepts has made a new concept named lean supply chain (LSC) management [24, 25]. B. Supplier Selection Basically, supplier selection is a process with the objective of lessening the initial cluster of potential suppliers to the eventual choices [4, 19]. This process is mainly based on evaluation of suppliers using numerous quantitative and qualitative metrics. Regarding the problem in hand, managers or companies may seek for new suppliers or choosing suppliers from the current pool of suppliers. Irrespective, there is an amount of vagueness in the decision process due to availability of both qualitative and quantitative criteria and numerous decision makers [1, 2, 5, 14]This problem can justify the necessity of more precise approaches such as mathematical modelling. Supplier selection has been discussed by many scholars within the available literature [8, 10, 25]. C. Lean Supplier Management Lean production cannot be understood without a lean supply [5-7]. Lean supply aims to deliver a flow of products from suppliers to the manufacturer with the minimum waste. Figure 1 (adopted from [26]) shows a framework for lean supplier management. Fig. 1 Lean Supplier Selection Framework (adopted from [26]) 986

3 D. Supplier Selection Tools Supplier selection can be done using different approaches. These approaches can consider both quantitative and qualitative metrics. Multi-criteria decision making approaches (e.g. AHP, ANP and etc.), mathematical models (e.g. DEA), group decision making (e.g. NGT) are some of the approaches used to select the suppliers [27]. E. Need For Integrated Approach Concisely, an effective approach to develop lean-related specific measures for supplier selection has not been developed, especially using mathematical models that can combine all the measures together to put them in practice. In addition, the availability of numerous measures in the literature makes the supplier selection process very confusing and can impose many expenses to the company. A specialized supplier selection framework is required to use specific lean related metrics for the aim of assessing suppliers and incorporate it with a mathematical model to put all the proposed metrics in practice. Trying to fill the gap in the literature, this study developed an integrated lean-based DEA model for assessing and selecting suppliers by to assist managers and researchers to proficiently make their supplier selection decision. Based on best of our knowledge, this paper seems to be one of the preliminary studies on suggesting specific performance metrics for lean-based supplier evaluation and incorporating them to a mathematical model for the aim of selecting suppliers in semiconductor industry. III. RESEARCH METHODOLOGY In the first phase of this research, a literature review was conducted to determine the major perspectives of lean supply chains. Related metrics of each perspective were then established using a systematic literature review (SLR) approach. minal Group Technique (NGT) was then deployed to determine and categorize the most critical criteria of lean supplier selection. Experts were asked to consider all lean related aspects in addition to the characteristics of semiconductor industries during the process of categorizing lean-related metrics into its major perspectives. The proposed metrics were used as the input of DEA model in addition to supply variety and quality of distribution as the output. Finally, using the GAMS software, the model was solved and the result were discussed. The conceptual framework of this research is shown in Figure 2. Idea and Problem Statement Determining the Most Important Perspectives of Lean Supply Chains Cost Feedback to Managers Quality Service Level Lean Supplier Selection Criteria Supplier I Is the managerial implication satisfy managers desires? Lead T im e Data Collection Managerial Implication Supplier II Supplier III Stage Two: Determining Desired criteria and supplier evaluation Supplier IV Constructing DEA Models in GAMS Analysis of Results Stage One: Problem Statement and Analysis criteria Solving the Model Stage Three: Modelling Managerial Implication Fig. 2 Conceptual Framework 987

4 DATA ENVELOPMENT ANALYSIS (DEA) IV. DEA is a nonparametric technique in operations research and employed to measure the efficiency of decision making units (DMUs). Based on the [28] ideas, the study "Measuring the efficiency of decision making units" by [29] employed linear programming (LP) to approximate an experiential production technology frontier. Since then, a great quantity of journal articles and books focused on DEA or using DEA on numerous research areas. V. LEAN SUPPLIER SELECTION CRITERIA Based on [30], the major perspectives of lean supply chain lie on cost, quality, lead time and service level. Having this idea in hand, a systematic literature review (SLR) was conducted to extract the most repetitive metrics used to select suppliers in each perspective. A nominal group technique (NGT) was then used to summarize the extracted metrics. Table 1 shows the proposed metrics to be used for the aim of lean supplier selection. Table 1 Lean Supplier Selection Criteria Indicator 1 C1 2 C2 3 4 Perspectives Measure Sample References Price [4, 7, 16, 18] Technical capability [2, 5] C3 Quantity Discounts [6, 7, 15] C4 Production Capacity [4, 16, 19] 5 C5 Quality of product [9, 12] 6 C6 Reputation [5, 6] 7 C7 Changing Policy [4, 7, 16, 19] 8 C8 Waste Level [25] 9 C9 Delivery time [4, 16, 19] 10 C10 Delivery reliability [4, 7, 16, 18] 11 C11 Location [2, 5] 12 C12 Information sharing [5, 6] 13 C13 After sale service [12, 27] 14 C14 Free delivery [17] 15 C15 Penalties [2, 3, 5, 13] 16 C16 Innovation Level [6, 7, 15] Cost Quality Lead time Service Level VI. MODEL DEVELOPMENT DEA is a linear mathematical process for a frontier investigation of inputs and outputs. DEA allocates a score of 1 to a DMU once comparison with other DMUs do not offer inefficiency evidence. It allocates a less than one score to inefficient units. A less than one score shows that a linear mixture of other DMUs can make the same outputs with smaller inputs. DEA considers that the inputs and outputs are truly defined. Typically, as the quantity of inputs and outputs upsurge, many DMUs tend to become efficient. Conversely, with few inputs and outputs, more DMUs tend to be similar. In any research, it is significant to concentrate on appropriately determining inputs and outputs. Friedman and Sinuany-Stern [31] have suggested an instruction for DEA. According to this research, the quantity of DMUs must be bigger than the triple of the entire quantity of input and output factors (3(m + s) < n) [32]. To solve this issue, we developed different models based on each perspective. Model inputs are lean supplier selection criteria and the outputs are supply variety and quality of distribution. The related equations are as follows: 988

5 t ur yrjo Maximize h0 = r =m1 vx i =1 i ijo t ur yrjo S.T. r =m1 1 vi xijo i =1 (1) j=1, 2.., n (2) ur ε (3) vr ε (4) Where ur = the weight for output r, vr = the weight for input i, yrj = the amount of output r of DMU j, xij = the amount of input i of DMU j, t= the number of outputs, m= the number of inputs, n= the number of DMUs and number. ε = a small positive VII. EVALUATION OF SUPPLIERS Suppliers Outputs Quality of distribution DMUs Supply variety Inputs Table 1 We used different perspectives in order to evaluate the performance of each supplier. The first perspective is related to cost (with C1-C4 indicators as input and supply variety and quality of distribution as outputs). The second perspective evaluated the suppliers performance with regard to quality (with C5-C8 indicators as input and supply variety and quality of distribution as outputs). Third perspective focused on lead time (with C9-C12 indicators as input and supply variety and quality of distribution as outputs). Finally, last perspective evaluated suppliers based on service level (with C13-C16 indicators as input and supply variety and quality of distribution as outputs). Figure 3 shows a conceptual framework of suppliers evaluation. Fig.3 Suppliers evaluation conceptual framework VIII. RESULTS AND DISCUSSION All perspectives were used in the model and solved using GAMS software shown in Table 2. The results for all four suppliers are categorized based on each perspective. 989

6 Table 2 DEA Results Supplier Perspectives Efficiency S1 Dominant S2 Cost S3 Weight= S4 S S2 Quality S3 Weight=0.25 S S S2 Lead time S3 Weight=0.25 S S S2 S3 S4 Service Level Weight=0.25 As shown in Table 2, the efficiency of each supplier is categorized based on our four lean related performance perspectives. We have used the weights of each perspective to integrate the scores of each supplier. To have a balanced selection, we have assigned 0.25 to each perspective. As a managerial implication, other studies can use different weights to increase or decrease the effect of each perspective. Following shows the total index of each supplier using a balanced approach (0.25 for each perspective). Based on the eq. 9, suppliers are ranked as S2, S1, S3 and S4 respectively. S1= 0.25 ( ) = S2= 0.25 ( ) = S3= 0.25 ( ) = S4= 0.25 ( ) = Therefore, S2 > S1 > S3 > S4 IX. (5) (6) (7) (8) (9) CONCLUSION AND FUTURE RESEARCH The lack of an effective approach to develop lean-related specific measures for supplier selection in addition to the availability of numerous measures in the literature and many confusing issues of real world problems motivated us to do this study. This study developed an integrated lean-based DEA model for assessing and selecting suppliers to assist managers and researchers to proficiently make their supplier selection decision. Four performance perspectives (cost, quality, lead time and service level) in addition to 16 related metrics were designated as substitutes for leanness. The extracted metrics in addition to supply variety and quality of distribution were used as the inputs and outputs of DEA models respectively. The proposed model was solved using GAMS software and some managerial implications were then suggested. As a direction for future research, the proposed metrics can be evaluated to be used in other industries. In addition, more complex models (e.g. Fuzzy DEA) can be proposed and solved by other OR softwares. 990

7 [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] REFERENCES Choi, T.Y. and J.L. Hartley, An exploration of supplier selection practices across the supply chain. Journal of operations management, (4): p De Boer, L., E. Labro, and P. Morlacchi, A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, (2): p Ellram, L.M., The supplier selection decision in strategic partnerships. Journal of Supply Chain Management, (4): p. 8. Ghodsypour, S.H. and C. O'Brien, A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International journal of production economics, : p Dargi, A., et al., Supplier selection: A fuzzy-anp approach. Procedia Computer Science, : p Galankashi, M.R., et al., Prioritizing Green Supplier Selection Criteria Using Fuzzy Analytical Network Process. Procedia CIRP, : p Galankashi, M.R., et al., Supplier Selection for Electrical Manufacturing Companies Based on Different Supply Chain Strategies. Electrical Engineering, Sarkis, J. and S. Talluri, A model for strategic supplier selection. Journal of supply chain management, (1): p. 18. Verma, R. and M.E. Pullman, An analysis of the supplier selection process. Omega, (6): p Kannan, V.R. and K. Choon Tan, Attitudes of US and European managers to supplier selection and assessment and implications for business performance. Benchmarking: An International Journal, (5): p Zhu, Q. and Y. Geng, Integrating environmental issues into supplier selection and management. Greener Management International, (35): p Spekman, R.E., Strategic supplier selection: understanding long-term buyer relationships. Business horizons, (4): p Dulmin, R. and V. Mininno, Supplier selection using a multi-criteria decision aid method. Journal of Purchasing and Supply Management, (4): p Boran, F.E., et al., A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, (8): p Galankashi, M.R., Memari, A., Anjomshoae, A., Mara'am, A., Helmi, S.A., Selection of Supply Chain Performance Measurement Frameworks in Electrical Supply Chains. Ghodsypour, S.H. and C. O brien, The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International journal of production economics, (1): p Bhutta, K.S. and F. Huq, Supplier selection problem: a comparison of the total cost of ownership and analytic hierarchy process approaches. Supply Chain Management: An International Journal, (3): p Ho, W., X. Xu, and P.K. Dey, Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, (1): p Gunasekaran, A., C. Patel, and E. Tirtiroglu, Performance measures and metrics in a supply chain environment. International journal of operations & production Management, (1/2): p Schonberger, R.J., Best practices in lean six sigma process improvement. 2008: John Wiley & Sons. Womack, J.P., D.T. Jones, and D. Roos, Machine that changed the world. 1990: Simon and Schuster. Scherrer-Rathje, M., T.A. Boyle, and P. Deflorin, Lean, take two! Reflections from the second attempt at lean implementation. Business Horizons, (1): p Shah, R. and P.T. Ward, Lean manufacturing: context, practice bundles, and performance. Journal of operations management, (2): p Husby, P. and D. Swartwood, Fix your supply chain: how to create a sustainable lean improvement roadmap. 2009: CRC Press. Manrodt, K.B., K.L. VITASEK, and R.H. Thompson, The lean journey. Logistics management, (4). Guo, Y. and Z. Xu, A Model of Lean Supplier Management Based on the Lean Production, in Research and Practical Issues of Enterprise Information Systems II. 2008, Springer. p Shyur, H.-J. and H.-S. Shih, A hybrid MCDM model for strategic vendor selection. Mathematical and Computer Modelling, (7): p Farrell, M.J., The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 1957: p Charnes, A., W.W. Cooper, and E. Rhodes, Measuring the efficiency of decision making units. European journal of operational research, (6): p Agarwal, A., R. Shankar, and M. Tiwari, Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach. European Journal of Operational Research, (1): p Friedman, L. and Z. Sinuany-Stern, Scaling units via the canonical correlation analysis in the DEA context. European Journal of Operational Research, (3): p Rezaee, M.J., Using Shapley value in multi-objective data envelopment analysis: Power plants evaluation with multiple frontiers. International Journal of Electrical Power & Energy Systems, : p

8 [33] Galankashi, M.R., Fallahiarezoudar, E., Moazzami, A. Yousof, NM., Performance evaluation of a petrol station queuing system: A simulation-based design of experiments study. Advances in Engineering Software, : p BIOGRAPHY Masoud Rahiminezhad Galankashi eceived his B.Sc and M.Sc in the field of industrial engineering from QIAU and UTM, respectively. He is currently working on his doctorate degree at UTM in the field of industrial engineering focusing on Supply Chain Management (SCM).His research interests are SCM, simulation, MCDM/MADM, mathematical modelling, lean manufacturing and DOE. Mohammad Hisjam is an assistant professor of industrial engineering. His major research area is supply chain management. Syed Ahmad helmi is an assistant professor of industrial engineering. His major research area is system dynamic. 992