Master Thesis. TU Delft Faculty of Technology, Policy and Management

Transcription

1 TU Delft Faculty of Technology, Policy and Management MSc Program Management of Technology Master Thesis A supplier management approach based on carbon performance criteria_ Case study: Royal DSM Master Thesis Committee First supervisor: Dr. Jafar Rezaei TLO section Second supervisor: Dr. Roland Ortt TSE section Chairman: Prof. dr. Lorant Tavasszy TLO section Chrysoula Vana External Supervisor: Dalia Gonciauskaite Sustainability Officer- DSM Sourcing April 2014

2 Abstract During the past decade there has been an increasing interest towards sustainable development and environmental management from corporate organizations, with efforts extending across their value chain to their partners. On the one hand, Life Cycle Assessment (LCA) is the most commonly used tool for measuring the environmental performance of a product, including the raw materials a company buys, and can greatly assist managers in material selection process. On the other hand, assessing a supplier s potential for improvement is also important when an organization seeks to achieve certain environmental targets concerning their supply base, taking into account the limited resources available. In the context of this thesis a methodology for developing a green supplier segmentation tool, incorporating information from LCAs, is proposed as a means to identify and prioritize sustainability related opportunities regarding suppliers. In this methodology, fuzzy Analytic Hierarchy Process (AHP) using fuzzy preference programming (FPP) is utilized in order to solve the Multi-Criteria Decision Making (MCDM) problem of supplier evaluation. Additionally, the methodology is applied in a real world case and the results serve as the basis for further recommendations. Keywords: supplier segmentation, green supplier selection, supplier development, green supplier management, fuzzy AHP, sustainability, corporate carbon footprint 1

3 Acknowledgements This Master thesis report is the final part in the graduation procedure for my master degree program of Management of Technology at TU Delft. This research has been carried out under the section of Transport & Logistics Organization (TLO) and it is the result of a graduate internship project in DSM Sourcing. I would like to acknowledge and extend my genuine gratitude to the following people who have made the completion of this Thesis possible: My first supervisor, dr. Jafar Rezaei for providing guidance, advice and valuable feedback for the successful execution of the project. The Chairman of the thesis committee, Prof. dr.ir. Lorant Tavasszy, and my second supervisor, dr. Roland Ortt for their support and valuable input in assisting me to complete this thesis. Luc van de Walle and Harm Veerkamp for granting me the opportunity to become an active member of their team in DSM and make this internship a true working experience. My company supervisor, Dalia Gonciauskaite, for giving me the ideal combination of freedom and control in approaching the problem under study, her openness to discuss potential issues arisen during the process and her valuable input during feedback meetings. Henk Bosch, Robert Donker and Patrick van Bael as well as the members from purchasing community in DSM who participated in the survey, deeming their contribution as critical for the successful completion of the research. My colleagues in DSM Sourcing who embraced me from the beginning and have always been willing to provide their support. Finally, I would like to express my love and gratitude to my family and friends for their continuous and unconditional support throughout the execution of this Master s programme. 2

4 Summary Increasing environmental concerns have led companies embrace the idea of sustainability as a potential competitive advantage. One of the key themes in sustainability has been carbon emissions or else greenhouse gas (GHG) emissions, responsible for the well-known greenhouse gas effect. Till now, organizations have focused their efforts in controlling emissions from their own production activities; however, great reduction opportunities are being identified in extending these efforts towards their whole value chain. The need for increasing transparency and communication of results in this direction has become even more apparent with the recent development of the first internationally recognized standard for accounting and reporting on company s total carbon footprint attributed to both upstream and downstream activities. In most industries, sourcing of raw materials is identified as the most carbon intensive activity making the role of purchasing function critical in achieving meaningful improvements in the organization s environmental performance. This has also been the case for Royal DSM, an international Dutch company active in Material Sciences and Life Sciences, who hold sustainability as one of their main business drivers. The company has set an ambitious target of reducing emissions attributed to raw materials purchased by 20% by 2020, compared to their baseline scenario, but now the question that rises is how can this be achieved? Despite their commitment, companies often encounter difficulties in understanding and determining how progress can be achieved given limited availability of resources. To provide a solution to this problem the following main research question has been formulated: How can we evaluate the impact and segment suppliers based on their carbon performance, in order to assist managers and decision makers efficiently allocate their resources in carbon reduction practices along their supply chain? The first part of this research question, which is generally referred to as impact assessment stage, aims to provide input in prioritization of action towards suppliers whose improvements can have a meaningful impact to buying firm s performance. In large organizations, the supply base can amount to thousands of suppliers and raw materials procured and it is impossible to dedicate resources to improve all of them. Life Cycle Assessment (LCA) methodology is a widely adopted tool for evaluating environmental impacts; it results in an emission factor that measures the amount of emissions per unit of product throughout the product s different life stages and it can be compared to price; in the same way that suppliers can provide the same product at different prices, the product s real footprint may also vary per supplier. The first major recommendation in this research is the development of the following two dimensional matrix, as a visualization of company s footprint that comes from its supply base, in order to prioritize action (suppliers Si and raw materials Rj are sorted based on their absolute contribution). 3

5 R 1 R 2 R k Total S i S 1 CF 11 CF 12 CF 1k S 2 CF 21 C 22 CF 2k S n CF n1 CF n2 CF nk Total R j Table A: Visualization carbon impact assessment Total CF The table contains carbon emissions per raw material per supplier (CF ij ) and incorporates 2 main advantages: It takes into account the synergistic effects of multiple raw materials sourced from one supplier and multiple suppliers for one raw material. It serves for prioritizing action when looking for the solution to the problem from two different perspectives that demand for different expertise: supplier-based and material-based. The second part of the research question, which aims at the identification of carbon reduction opportunities with regard to suppliers, is mainly answered through supplier management theory and more specifically with the development of a green supplier segmentation methodology, as a means to assess supplier s potential to contribute to buyer s carbon performance goals. Our methodology builds upon Rezaei and Ortt s (2012) segmentation tool which is adapted to segment suppliers based on Criteria assessing supplier s capabilities in terms of reducing the carbon footprint of the raw materials they sell to the specific customer. Criteria assessing supplier s willingness to work either independently or in collaboration with the buyer towards the fulfillment of the buyer s objective which is again the reduction of carbon footprint of the materials bought. The supplier s position in the grid indicates his potential to contribute to the goal. Criteria assessing supplier s green capabilities used and clearly defined in this research include: - Management competencies (senior management support, availability of experienced personnel, network of environmental partners) - Green design (energy efficiency/ems, eco+ product development, transportation) - External recognition 4

6 Willingness (W) - Carbon disclosure - Pollution control Criteria assessing supplier s willingness include: - Commitment to buyer - Commitment to sustainability - Open to info sharing - Open to site evaluation - Trust - Communication richness The final supplier evaluation on these two dimensions, which consists a MCDM problem, was derived by implementing the improved fuzzy AHP using FPP as developed by Rezaei et al. (2013). The criteria described above are listed in order of importance as resulted from the application of the methodology. The second major recommendation in this research is the development of the following green supplier segmentation, in order to customize action based on supplier s potential to contribute to buyer s goal (with impact size integrated as a third dimension). High Impact (I) : Low impact : Medium impact : High impact Low Low Capabilities (C) High Figure A: Extended green supplier segmentation The aforementioned supplier segmentation serves for the distinction among different management strategies regarding suppliers, based on their position in the grid, as described in the following table and provides guidance on next steps and efficient resource allocation towards supplier development and performance improvement. 5

7 Type 2: Low C and High W Invest in improving a strategic sample These suppliers are willing to contribute to the goal but their lack of capabilities illustrates the need for direct involvement and availability of resources which are limited to the buying firm. Decide whether to invest in improving performance or capabilities of these suppliers (see ways to improve supplier s capabilities). Factors to take into consideration when deciding which suppliers to develop, apart from impact category, include the item s strategic importance to the company s business and the expectation for long-term relationship with the supplier. Type 1: Low C and Low W Eliminate high impact suppliers Type 4: High C and High W Bring the topic on the agenda These suppliers are already well informed and advanced in sustainability issues and could timely contribute to the company s goals with low investment efforts. Try to maintain long-term partnerships with these suppliers. Communicate with them to learn the exact effects of their environmental capabilities to your product s footprint and explore together potential improvement areas. Type 3: High C and Low W Focus on relationship management Suppliers in this group probably focus on low cost strategy and have limited environmental knowledge. Take immediate action against high impact suppliers in this category. Start by raising supplier s awareness and communicating the benefits, set higher performance goals and reassess their improvement. If performance is still not tolerable, these suppliers should be either further developed or substituted. These suppliers have the capability of improving their environmental performance themselves. Therefore the challenge lies in information share. Identify root causes for supplier s low score and work on improving and developing the relationship with the supplier (see ways to increase supplier s willingness). Potential issues in buyer-supplier interface are described in table 2.2 Table B: Managerial implications following segmentation Ways to improve supplier s environmental capabilities: Evaluate suppliers Increase supplier performance goals Recognize improvements by performance awards Direct involvement: o Provide equipment and technical support o Provide capital investment o Train suppliers o Exchange personnel o Undertake joint improvement projects 6

8 Ways to increase supplier s willingness: Direct involvement, especially when it includes financial and technical assistance, can also greatly contribute to supplier s willingness to cooperate. Put competitive pressure through multi sourcing Recognize improvements by performance awards Increase volume of current business Express priority considerations for future business Effectively communicate benefits Furthermore, potential improvement projects have been demonstrated revolving around the three main stages that comprise a product s carbon footprint: raw material extraction (translated into investments in R&D and new product development using alternative resources), transportation to supplier s facilities (which makes up a minimal contribution to product s footprint), and supplier s production process (translated into investments in process improvement, efficient energy use and resource management). Finally, the application of the methodology in a real-world case has resulted in the emergence of several points that need to be taken into account in order to increase probabilities of success in achieving the objective. - First of all, the pareto principle applies which indicates that companies should focus their efforts towards a limited number of suppliers through which they can achieve substantial improvements. - Data used for supplier s both impact and potential assessment do not always depict reality, which demonstrates the need for supplier-buyer communication and information exchange in order to be able to identify potential improvement areas. Supplier-specific LCAs instead of using average data can serve as a differentiator among suppliers. - Before communicating expectations to suppliers, companies have first to address several internal issues such as raise awareness of the purchasing community, train their personnel, form cross functional teams made up of experts on environmental impacts, material science and purchasing, and establish a well-organized system for knowledge management which can improve decision making. - When buyer agrees on a project with the supplier, it is important to clearly define details of the agreement and decide on a monitoring system that will allow modifying strategies based on the progress made. 7

9 Contents Abstract... 1 Acknowledgements... 2 Summary... 3 List of figures List of tables Introduction Problem Description Conceptual design Research Objective Research Framework Research questions Technical Research Design Research strategy Research material Practical and scientific contribution Report Structure Literature Review Supplier Selection Supplier Segmentation Supplier Development Environmental Concerns in Purchasing Sustainability and carbon footprint Product carbon footprint Corporate carbon footprint Green supplier selection Green supplier segmentation Conclusion Methodology Analytic Hierarchy Process (AHP) Fuzzy Set Theory (FST) Fuzzy AHP using fuzzy preference programming (FPP) Carbon Impact Assessment Extended green supplier segmentation

10 3.6. Conclusion Application The company Problem Description Application of extended green supplier segmentation methodology Step 1: Assign supplier to a carbon impact category Step 2: Determine criteria and construct hierarchy of the problem Step 3: Assign a score to each supplier with respect to these criteria Step 4: Determine the weights of the criteria Step 5: Determine the final aggregated 2-dimensional score per supplier Step 6: Integrate impact dimension in the extended segmentation grid Conclusion Results and Discussion Results carbon impact assessment Pareto principle Supplier-specific LCAs Results supplier s potential assessment Supplier s potential assessment from buyers Criteria weights Validation Extended green supplier segmentation Supplier management and development Conclusion Conclusion and future research Reflections on research questions Scientific Contributions Assumptions, limitations and future research References

11 List of figures Figure 1.1: DSM Organization Figure 1.2: Research Framework Figure 2.1: Elements of strategic supplier management involved in the research Figure 2.2: Stages in Supplier Selection Process (Cousins, et al., 2007) Figure 2.3: Supplier selection methods (Chen, 2011) Figure 2.4: Kraljic's portfolio segmentation tool (Kraljic, 1983) Figure 2.5: Analysis of supplier's relationships as an extension to Kraljic s model (Olsen & Ellram, 1997) Figure 2.6: Supplier Relationship Map (Tang, 1999) Figure 2.7: A supplier typology for the automotive industry (Kaufman, et al., 2000) Figure 2.8: Supplier segmentation (Svensson, 2004) Figure 2.9: Supplier's potential segmentation tool and its managerial implications (Rezaei & Ortt, 2012) Figure 2.10: Supplier development process (Handfield, et al., 2000) Figure 2.11: From sustainability to low carbon procurement Figure 2.12: Cradle to grave LCA Figure 2.13: Cradle to cradle LCA Figure 2.14: Cradle to gate LCA Figure 2.15: Overview of corporate GHG emissions (WRI/WBCSD, 2013) Figure 2.16: Extended Kraljic's model to incorporate environmental concern (Cousins, et al., 2007) Figure 2.17: Supplier green classification portfolio model and its managerial implications (Zhu, et al., 2010) Figure 3.1: Problem structure in AHP Figure 3.2: The triangular fuzzy membership function (Laarhoven & Pedrycz, 1983) Figure 3.3: The trapezoidal fuzzy membership function (Amindoust, et al., 212) Figure 3.4: Visualization of supplier s carbon impact in the company s footprint Figure 3.5: Supplier's potential segmentation extended to include impact category Figure 4.1: DSM's Global Supplier Sustainability Program (DSM, 2013) Figure 4.2: GHG emissions in DSM's Value Chain (DSM, 2013) Figure 4.3: DSM's carbon impact visualisation with emission factors Figure 4.4: Problem structure_capability assessment Figure 4.5: Problem structure_ willingness assessment Figure 4.6: Extended green supplier segmentation in DSM at corporate level Figure 4.7: Green supplier segmentation (BG1) Figure 4.8: Green supplier segmentation (BG2) Figure 4.9: Green supplier segmentation (BG3) Figure 4.10: Green supplier segmentation (BG4) Figure 4.11: Green supplier segmentation (BG5) Figure 5.1: Information flow in LCA collection

12 List of tables Table 1.1: Research material and strategy Table 2.1: Types of requests to suppliers (Cousins, et al., 2007) Table 2.2: Supplier selection criteria (Deshmukh & Chaudhari, 2011) Table 2.3: MCDM problem representation Table 2.4: Barriers to supplier development (Cousins, et al., 2007) Table 2.5: Supplier's overall performance evaluation criteria and sub-criteria (Zhu, et al., 2010) Table 2.6: Capabilities criteria for green supplier evaluation Table 2.7: Willingness criteria for green supplier evaluation (Rezaei & Ortt, 2012) Table 3.1: MCDM problem representation Table 3.2: Numerical scale proposed by Saaty Table 3.3: Values of the Random Index (RI) (Saaty, 2001) Table 4.1: Definitions of criteria used to assess green supplier capabilities Table 4.2: Definitions of criteria used to assess supplier's willingness to contribute to carbon reduction goal Table 4.3: Logic behind criteria assessing supplier's potential to contribute to buyer's carbon reduction goals Table 4.4: Linguistic variables in fuzzy AHP Table 4.5: Pairwise comparisons_capabilities (C) main criteria Table 4.6: Pairwise comparisons_ Management competencies (C1) sub-criteria Table 4.7: Pairwise comparisons_ Green design (C2) sub-criteria Table 4.8: Pairwise comparisons_ Willingness (W) criteria Table 4.9: Criteria weights for capabilities Table 4.10: Criteria weights for willingness Table 4.11: Supplier's capability score Table 4.12: Supplier's willingness score Table 4.13: Combined carbon impact and potential assessment results Table 5.1: Pareto efficiency in carbon impact assessment Table 5.2: Summary of criteria global weights Table 5.3: Consistency and λ* values Table 5.4: Supplier distribution in potential grid (DSM corporate) Table 5.5: Managerial implications derived from supplier's green segmentation

13 1. Introduction Sustainability has received a great deal of attention from the media and the business community in the recent years. This is mainly due to the public s growing social and environmental awareness which in turn has lead firms to embrace the strategic importance of environmental management practices for competitive advantage (Yang, et al., 2011). Yet, as a growing number of companies work to become more environmentally and socially sustainable it becomes more apparent that such transformation is challenging. In recent years there has been a considerable shift in thinking with regard to improving not only the environmental performance of company s own processes and production activities but also extending efforts to the entire value chain in which it operates including their supply base (Gavronski, et al., 2011; Govindan, et al., 2013). Up until now, theory in strategic supply management has been strongly based on criteria such as price, quality, flexibility etc. in order to assess supplier s performance. However, with the increased emphasis on environmental issues the need for considering supplier relationships from a strategic and sustainable perspective has become more apparent, increasing the complexity of decisionmaking (Bai & Sarkis, 2010). The main question arising and is worth researching is how can we develop a supplier management approach that will be based on supplier s environmental performance in order to assist managers and decision makers set and prioritize sustainability-related actions with regard to their suppliers. This chapter provides a more thorough description of the problem under study followed by the conceptual and technical research design of this research Problem Description Environmental concerns particularly related to rapid resource depletion and climate change have led to increased pressure on companies to conform to environmental standards and report their releases of pollutants (Gavronski, et al., 2011). The gases contributing to the greenhouse effect, also known as greenhouse gases (GHG), have increased dramatically due to human activities since the beginning of the industrial revolution and have emerged as one of the most important global issues multinationals have to face (IPCC, 2007). Even though firms have attempted to respond to this challenge by trying to develop more eco-friendly products and services, there is still little guidance on how they can reduce their impact. According to Humphreys et al. (2003) a potentially effective way of managing a company s environmental policy is by linking it closely with the activities of the purchasing function. Researchers have suggested that diffusing environmental management techniques along the supply chain can prove to be a beneficial approach in enhancing the environmental performance of an industry (Humphreys, et al., 2003; Lamming & Hampson, 1996). This theory is further reinforced by the development of an internationally recognized industry standard in 2011 by World Business Council for Sustainable Development (WBCSD) and World Resources Institute (WRI) that sets the boundaries and guidelines for companies to transparently disclose emissions along their supply chain and take action towards reducing them. Taking into account that in the majority of cases most of the company s total emissions come from the amount of goods procured, many companies biggest opportunities for environmental improvement lie in strategic sourcing rather than the 12

14 improvement of their own capabilities. But what are the tools a company can use to fulfill this target? On the one hand, there is an extensive range of literature regarding raw material selection. The Life Cycle Assessment (LCA) methodology is the most widely accepted tool for evaluating environmental effects of a product, process or activity throughout its lifetime. The concept emerged in 1960s but still the international LCA community is struggling with issues related to LCA databases, data collection and data quality goals (Roy, et al., 2009). Although LCA methodologies have been improved and standards for estimating the emissions have been revised, further international standardization is required to allow for direct comparison of different case studies. On the other hand, during the last decade many researchers in the field of supplier selection/segmentation and management have started integrating environmental criteria in their models and frameworks developed for supplier assessment (Humphreys, et al., 2003; Tuzkaya, et al., 2009; Rezaei & Ortt, 2012; Zhu, et al., 2010; Hsu, et al., 2013) directly linking sustainability to strategic supplier management. When an organization faces the challenge of reducing the greenhouse gas emissions from the raw materials they buy this is automatically translated into reducing the emissions from their suppliers. This is why LCA quantitative data characterizing the materials should be combined with qualitative information regarding the prospects of achieving the reduction target through a certain supplier. The aim of this thesis is to provide an integrated approach, particularly for international organizations where the complexity of the supply chain is high, as to how to prioritize and customize their efforts and resources towards engaging their suppliers in the improvement of their products carbon footprint. DSM Project Description The research has been carried out in DSM Sourcing as a master internship project. DSM has set a goal to reduce the footprint of the raw materials the company buys by 20% by So far, the base line footprint of the raw materials has been calculated and a process has been established for collecting information from the suppliers. The next step is to define a roadmap for each Business Group, how they could achieve this target. Goals of the project from commissioner s side include: - Benchmark against peers and competitors in terms of carbon footprint reduction targets and roadmaps - Develop a proposal on the structure of the roadmap - Interview internal stakeholders (from business groups, corporate functions, etc) in order to formulate realistic recommendations - Prepare the planning as well as supporting documents to enable DSM to prepare 4 roadmaps by the end of 2013 The characteristics of the company will be further analyzed in chapter 4 but it is worth briefly explaining the organization here to understand the difference between corporate level and business group (BG) level. 13

15 Figure 1.1: DSM Organization As it can be seen in figure 1.1 the organization is made up of 8 business groups (BG) which work as independent entities. BGs are in fact the customers of DSM Sourcing when it comes to strategic sourcing. This situation has several implications for the project which were identified in the initial research phase via unstructured interviews with employees from Sourcing department and after a preliminary screening of the company s records. These include: Not all BGs have the same incentive for pursuing sustainability: In many cases the need for taking action is initiated in companies by an increasing pressure from customers side which can vary per industry and product category. Since product portfolios differ significantly among BGs, analysis may suggest that in certain cases the company might have to start by raising awareness internally before moving towards a supplier management strategy. Critical suppliers/ raw material for one BG might be of secondary importance to another and vice versa: In the case where the same supplier is involved with more than one BG, designing strategies at BG and corporate level differs. Big suppliers at corporate level might be small suppliers at BG level. These are considerations that should be taken into account when deploying strategies for the company as a whole and when designing roadmaps for individual BGs and will be further discussed in this research Conceptual design The conceptual design serves various purposes within a research project the most important of which is steering in the creation of the technical design as well as in the actual implementation of the research project later on (Verschuuren & Doorewaard, 1999). The conceptual design includes: the formulation of a useful, realistic and feasible research objective, the determination of the research framework and the formulation of research questions which will derive useful knowledge to meet the research objective Research Objective The research objective of this thesis is to develop an integrated approach for the development of strategic directions regarding the reduction in greenhouse gas emissions 14

16 from a company s supply chain. This knowledge will be used to develop an assessment map for DSM s supply base and facilitate decision making in how to get from current to desired performance level regarding suppliers environmental performance Research Framework Below follows a schematic representation of the research framework for this project. The project is divided into two phases as illustrated in the figure. The first phase includes gathering all the necessary information in order to build a robust and comprehensive methodology for supplier assessment in terms of environmental performance and further supplier management. The second is made up of the application of this methodology for the purposes of DSM. Figure 1.2: Research Framework A) Theory building: As in any research, a preliminary research on the problem addressed is essential in order to be able to add value to the scientific community by advancing existing theoretical frameworks and not reinventing the wheel. The focus has been in supplier selection, supplier segmentation and supplier development research fields as the key components of strategic supplier management. The impact of green considerations in these fields has also been examined. During this step a major literature gap has been identified which indicated the need for the development of a green supplier segmentation tool to effectively distinguish strategies and define next steps regarding improving suppliers green competencies. Review of theory on corporate sustainability and materials environmental performance has also triggered the idea of extending the selected segmentation tool to include specific quantitative information regarding supplier s contribution to buying firm s supply chain carbon emissions. B) Application: In the application phase we aim to illustrate how the theory developed in part A can be leveraged to deal with a real-world situation. After the application of the proposed methodology, which is mainly comprised of two parts in order to segment suppliers supplier s carbon impact assessment and supplier s potential assessment-, theory 15

17 in supplier development field will prove to be a useful input in making further recommendations Research questions The main research question emerging is: How can we evaluate the impact and segment suppliers based on their carbon performance, in order to assist managers and decision makers efficiently allocate their resources in carbon reduction practices along their supply chain? In order to answer the above research question several core questions together with the corresponding sub-questions have been formed which prove to be useful in fulfilling the objective of the research. RQ1. Why is it important for companies nowadays to account, manage and report on the environmental performance across their whole value chain? RQ2: How can a company assess the contribution of its suppliers to the company s own environmental footprint in order to prioritize action? o What are the current standards and methodologies used for quantifying and reporting the environmental footprint of a product/process? o How can synergistic effects for the same raw material from different suppliers and one supplier providing more than one raw materials be taken into account? RQ3: How can a company assess supplier s potential to contribute to its carbon reduction targets and customize its strategy? o How is potential defined? o What are the metrics that can be used to quantify a supplier s potential? o What is the methodology used to quantify a supplier s potential? o How can impact and potential be combined to improve the decision making process? RQ4: What are the strategic directions a company can take, after the above mentioned supplier assessment, in terms of supplier approach/engagement regarding environmental performance? o What are the possible development actions the company can follow with regard to their suppliers so as to reduce the environmental footprint of the raw materials used? RQ5: How can the results of this research be applied in DSM case? (application) The first question answers to why this research is being conducted and is partially answered also in this chapter (RQ1). Questions 2, 3 and 4 answer to how the problem should be approached and what are the steps one should follow in order to be able to answer the main research question. Following the principle you can only manage what you measure we will present in relative detail how a company can assess the contribution of its suppliers to its overall carbon footprint as a means of prioritizing efforts towards the right suppliers. This is equivalent to companies usually prioritizing in-depth analysis and exploration of cost 16

18 reduction opportunities for suppliers and products that belong in big spend categories (RQ2). However, this information merely indicates where to focus efforts and not what these efforts should include or what the prospects of success are in achieving carbon reductions through a certain supplier. To do so, supplier s potential to contribute to the goal should be investigated (RQ3). Supplier segmentation is an increasingly used tool by companies for designing and customizing supplier management strategies in order to successfully meet their needs. This research will build upon Rezaei & Ortt s (2012) supplier segmentation model, which will also be referred to as supplier s potential segmentation. Their model however will be adjusted to provide a green supplier segmentation including criteria from green supplier selection literature. Supplier evaluation consists a Multi- Criteria Decision Making problem, for which a variety of techniques have been developed to solve. In this research, fuzzy Analytic Hierarchy Process (AHP) methodology has been selected to segment suppliers. Supplier segmentation should then provide the basis for defining appropriate supplier management and development strategies, customized for different supplier segments, in order to meet the objective (RQ4). Finally, the last question (RQ5) refers to the second part of the project, which involves the application of the developed methodology to the case of Royal DSM Technical Research Design As illustrated in the research framework, in section 1.2.2, the project is consisted of two phases: A) Build a framework for suppliers environmental assessment B) Apply the proposed framework for DSM case These two phases demand different research strategies and material as distinguished below Research strategy Phase A: The first phase largely consists of extensive literature review and desk research aiming to develop the approach for the assessment. Building the framework for the identification of reduction opportunities in terms of suppliers GHG emissions consists of three steps corresponding to questions 2, 3, 4 respectively; define supplier s impact on company s reported emissions, assess their potential to contribute to the goal and deploy strategies towards this direction. For all three steps, research will begin with collecting information from documents and people in the corresponding positions (Sustainability Officer, Competence Leader LCA, Purchasing Excellence Advisor), within the company under study, in order to understand the approach in place. Required information includes current sustainability efforts with regard to suppliers as well as existing monitoring and measurement systems for suppliers performance. However, desk research will also be combined, in order to help us identify possible improvements in existing practices and enrichment of the criteria and the metrics used to assess suppliers. Phase B: The second phase consists of the application of the developed model as a case study and involves both the collection of existing quantitative data from internal record systems and online databases for the impact assessment stage as well as the generation of new ones for the potential assessment stage. It is a quantitative research during which 17

19 questionnaires will be distributed to buyers across the company to assess a strategic sample of suppliers and fuzzy AHP methodology will be applied to derive final scores. This process, which will be based on the theory developed in phase A, will ultimately lead to a supplier segmentation which will be further used to make suggestions on next steps Research material A combination of resources and accessing techniques need to be leveraged in order to meet the objective of the project. The most common sources of knowledge and data include people, the media (printed and electronic), documents (consultant s records, annual reports) and the literature. Access instruments include questioning, observation, measurement tools, content analysis and search methods (Verschuuren & Doorewaard, 1999). For this research several resources will be consulted, a method known as triangulation. Source triangulation can further enhance the validity of the study by overcorrecting the disadvantages of the different sources. In the following table (1.1) the sources as well as the accessing methods for each phase of the project are presented. Phase A Qualitative - Desk research Phase B Research Strategy Sources Access technique Quantitative - Case study Table 1.1: Research material and strategy Decision makers Face-to-face preliminary unstructured interviews Databases: scientific journals Search method Electronically Literature Search method to select the appropriate literature to fulfill research objective within time constraints Databases: Electronically purchasing record and LCA databases Experts on Questionnaire sustainability/ followed by purchasing structured interviews 1.4. Practical and scientific contribution It can be concluded that this project, though initially designed as practice-oriented to serve a particular purpose in a private organization, it could directly contribute to the development of a theoretical body of knowledge, for the scientific community, in the field of corporate sustainability and green supplier management; literature on green material selection and green supplier selection will be combined in an integrated assessment tool to improve decision-making. This research constitutes the first attempt in developing a pure green supplier segmentation methodology, rather than integrating sustainability into supplier evaluation, and it will also be tested using a real world case. It is also among the first studies to use MCDM for supplier segmentation. 18

20 The practical relevance of the project is evident: the results of this research can on the one hand provide incentives for more companies to pursue sustainable growth practices along their supply chain while on the other hand assist already committed executive members in the decision-making process of pursuing alternative sustainability actions in terms of their suppliers Report Structure The rest of the research is structured in the following way: Chapter 2: Literature review Present theory in corpoarte carbon footprint and supplier management related to supplier selection, supplier segmentation, supplier development, the integration of environmental concerns in purchasing and the evolution of green supplier management (RQ1, RQ2, RQ3) Chapter 3: Methodology Develop a green supplier segmentation tool based on the theory described in chapter 2 (RQ3) Chapter 4: Application Company description, problem description and application of methodology developed in chapter 3 (RQ5) Chapter 5: Results and Discussion Discuss results from application and make recommendations based on supplier development theory (RQ4) Chapter 6: Conclusions and future research Revise research questions and main findings and make suggestions for further research 19

21 2. Literature Review The evolution of purchasing from a clerical function to strategic supply management is well documented in literature. Firms began to appreciate the potential contribution of purchasing in realizing strategic goals in 1980s when Porter (1980) emphasized the importance of the function in his five forces model of competitive advantage. According to Cousins et al. (2007) drivers for this change include technological, political, social and economic pressures. In this chapter, theory on strategic sourcing will be presented which will serve as the basis for dealing with the research problem. The study will elaborate around three main critical components in strategic supplier management that have attracted wide attention from researchers throughout the years: supplier selection, supplier segmentation and supplier development, as depicted in figure 2.1. This research will mainly focus in the segmentation component to develop a new approach in green supplier management. Supplier Selection/ Evaluation Supplier Management Supplier Developm ent Supplier Segmenta tion Figure 2.1: Elements of strategic supplier management involved in the research Supplier Management: Managing the relationships with the suppliers over time. It can be defined as the communication, evaluation and relationship building efforts involving suppliers (Rezaei & Ortt, 2012). Supplier Evaluation/Selection: Defining a number of qualitative and quantitative criteria that will be used to select the most appropriate supplier. Supplier Segmentation: Further classification of selected suppliers in different segments to be able to develop suitable strategies for different types of suppliers and adopt a more strategic approach to supplier relationship management (Rezaei & Ortt, 2013). Supplier Development: Any set of activities undertaken by a buying firm in coordination with a supplying firm to identify, measure and improve supplier performance and facilitate the continuous improvement of the overall values of 20

22 goods and services supplied to the buying company s business unit (Krause, et al., 1998). Sections focus in reviewing literature related to these elements for traditional supplier management. In section 2.4, the introduction of environmental concerns in purchasing is integrated and these elements are examined from a green perspective to serve the purposes of this research Supplier Selection According to Cousins et al. (2007) strategic supplier selection involves four main stages as depicted in figure Initial supplier qualification 2. Agree measurement criteria 3. Obtain relevant information 4. Make selection Figure 2.2: Stages in Supplier Selection Process (Cousins, et al., 2007) Stage 1_Initial supplier qualification: Supplier qualification is concerned with supplier s capabilities as a whole and aims at a first screening of potential suppliers to identify those who can meet the minimum product and process standards to be eligible for later selection. This stage helps organizations reduce the pool of potential suppliers to a more manageable number before committing time to a detailed evaluation. Collecting information for qualification usually starts with a survey request sent to supplier. There are three main types of request documents that can be issued when an organization seeks information from suppliers; request for quotation (RFQ), request for proposal (RFP) and request for information (RFI) (Cousins, et al., 2007). The definitions and use of these requests are described in table

23 Type of request Definition Use Request for Quotation (RFQ) Buyer makes the specifications available to other firms and requests for price and availability Request for Proposal (RFP) Request for Information (RFI) Stage 2_Agree measurement criteria: Buyer requires complete or partial design input from the supplier and requests for designs, price and availability Buyer wishes to collect more information regarding a product or supplier. It may lead to issuing an RFQ or RFP. Table 2.1: Types of requests to suppliers (Cousins, et al., 2007) If the monetary value of the item is high and the firm has no existing supplier Negotiation rather than competitive bidding Besides price other issues become important such as capability to innovate and R&D strength If the buyer has insufficient knowledge relating to a market or product to issue an RFP or RFQ. In the second stage a set of relevant and appropriate criteria need to be established against which suppliers will be assessed. Literature is abundant in the field of supplier selection and many researchers have developed their own assessment frameworks using a variety of measurement criteria. Three important contributions have been identified in reviewing supplier selection literature from 1996 till Dickson (1966) was the first to conduct a supplier selection research based on a questionnaire sent to 273 purchasing agents and managers and identified 23 supplier selection criteria which he ranked in terms of their importance. Weber et al. (1991) reviewed 74 articles published since Dickson (1966) up to 1991 concerning supplier selection criteria and methods whereas Deshmukh & Chaudhari (2011) continued their work by covering 49 articles issued from 1992 to These criteria as well as the ranking of their importance for the respective time-periods are depicted in table

24 Criteria Deshmukh& Chaudhari rank (2011) Weber et al. rank (1991) Dickson rank (1966) Net Price Quality Delivery Production Facility & Capacity Technical Capability Financial Position Geographical Location Management and Organization Performance History Operating Controls Communication System Reputation and Position in Industry Repair Service Packaging Ability Training Aids Procedural Compliance Labor Relations Record Warranties and Claims Policies Attitude Reciprocal Arrangement Impression Desire for Business Amount of Past Business Table 2.2: Supplier selection criteria (Deshmukh & Chaudhari, 2011) It can be observed from the table that throughout the years, price has emerged as the primary criterion in supplier selection whereas quality and delivery performance have been traditionally among the top 3 criteria. An additional remark is the increased importance of supplier s geographical location attributed to the emergence and wide adoption of JIT systems in organizations (Weber, et al., 1991). Also, in an era where information exchange and relationship characteristics have become a critical factor of success for businesses, building an effective communication system with suppliers has drawn considerable attention by practitioners (Deshmukh & Chaudhari, 2011). It can generally be claimed that despite the dominance of price criterion in supplier selection, the most significant trend in the field has been the tendency to move towards a total cost approach instead of a single criterion approach (Cousins, et al., 2007). Stage 3_Obtain relevant information: The third stage of supplier selection as described by Cousins et al. (2007) is to obtain the information needed to be able to compare suppliers across criteria. They distinguish among three main sources of information 23

25 Information from suppliers: As in the case of initial supplier qualification, buyers often receive and evaluate detailed information directly from potential suppliers which may come from requests for quotes (RFQ), requests for proposals (RFP) or request for information (RFI). These request types should not be confused as they are utilized in different circumstances (table 2.1). Supplier visits: Visits to supplier s site are on the one hand a type of socialization mechanism that enhances understanding and inter-firm relationships (Cousins & Menguc, 2006). It also provides the most complete way for skilled and experienced cross-functional teams from buyer s side to ensure an accurate evaluation of the supplier on different topics such as global capacity, logistical networks, supply management practices, process and technology capability (Monczka, et al., 2009). On the other hand supplier s willingness to allow for such visits is also an indication of supplier s transparency and openness and has often been used by researchers as a measurement criterion in supplier assessment (Kannan & Tan, 2002). Supplier performance measures: These are applicable to existing or incumbent suppliers that can be evaluated against past or current performance. Typically they include more subjective, non-financial measures such as the level and degree of information sharing, the number of buyer-vendor cost-saving initiatives and extent of mutual assistance in problem-solving efforts (Cousins, et al., 2007). An additional source of information is the use of third-party information available through the web that can also serve as a timely and effective way to gain insight into potential suppliers. (Monczka, et al., 2009) Stage 4_Make Selection Supplier selection is a Multi-Criteria Decision Making (MCDM) problem which includes a trade-off among a set of qualitative and quantitative factors in order to choose the best alternative available. MCDM is widely applied to address an extensive range of decision analysis problems aiding to reduce complexity and organize thinking in a systematic process (Cousins, et al., 2007). In short, the method involves evaluating and ranking a set of alternatives A i (i= 1, 2,, n) against a number of quantitative as well as qualitative criteria C j (j=1, 2,, k) in order to enhance decision making process. A typical representation of a MCDM problem is depicted in figure 2.4 where, a ij indicates the performance of alternative A i relative to criterion C j and w j (j= 1, 2,, k) are the weights that decision maker attributes to the respective criteria. Criteria C 1 C 2 C k Alternatives w 1 w 2 w n A 1 a 11 a 12 a 1n A 2 a 21 a 22 a 2n A n a 11 a 11 a 11 Table 2.3: MCDM problem representation A variety of both qualitative and quantitative models have been developed to assist decision-makers in making the final selection between potential suppliers (Cousins, et al., 24

26 2007) and researchers reviewing studies in supplier selection have proposed different categorizations of these methods. For example, the works of (Weber, et al., 1991; Deshmukh & Chaudhari, 2011) provide an extended literature overview of the methods used in final supplier selection stage distinguishing among three general categories: (1) Linear weighting models, (2) Mathematical Programming models, and (3) Statistical/probabilistic approaches. Other studies that aimed at reviewing decision making methods in supplier evaluation and selection include (Degraeve, et al., 2000; Boer, et al., 2001; Ho, et al., 2010). Degraeve et al. (2000) and Boer et al. (2001) distinguished among decision methods for the pre-qualification phase- including categorical methods, data envelopment analysis (DEA), cluster analysis (CA) and case-based reasoning (CBR) - and the final selection phase- including: (1) Rating and linear weighting methods, (2) Total cost approaches, (3) Mathematical programming models, (4) Statistical approaches and (5) Artificial intelligence-based models. Ho et al. (2010) distinguished among eight individual approaches and their hybrids as it is often the case that researchers combine more than one methods to improve the robustness of their results. Individual approaches include (1) Data envelopment analysis (DEA), (2) Mathematical Programming, (3) Analytic Hierarchy Process (AHP), (4) Case-based reasoning (CBR), (5) Analytic network process (ANP), (6) Fuzzy set theory (FST), (7) Simple multiattribute rating technique (SMART) and (8) Genetic Algorithm (GA). Finally, Chen (2011) summarized supplier selection methods identified by the above-mentioned researchers into two clusters of single models and combined models as illustrated in figure 2.3. Figure 2.3: Supplier selection methods (Chen, 2011) 25

27 Mathematical programming models such as linear programming (LP), goal programming (GP) and DEA are frequently encountered in literature (Boer, et al., 2001; Ho, et al., 2010). Such models aim to optimize supplier selection in order to maximize an objective function that can be either a single or multiple criteria (Boer, et al., 2001; Deshmukh & Chaudhari, 2011). Although mathematical programming models can provide some quite robust results, their greatest disadvantage lies in their inability to handle qualitative data. Linear weighting models on the other hand, such as AHP and ANP, are also extensively used by researchers as they overcome the above mentioned limitation of mathematical programming models. These models follow a slightly different approach as they aim to place a weight on each criterion and provide a total score for each vendor by summing up the vendor s performance on the criteria multiplied by the respective weight (Weber, et al., 1991; Degraeve, et al., 2000). ANP is a generalized form of the widely used multi-criteria decision making technique of AHP, which also takes into account interactions among the various factors however, it involves more complicated calculations (Zhu, et al., 2010). The greatest advantage of AHP relative to other methods is its simplicity and ease of use. It provides an easily understandable and defensible approach to practitioners while at the same time it is capable of coping with both qualitative and quantitative criteria (Govindan, et al., 2013). The integration of fuzzy set theory (FST) in AHP is often used to further enhance the ability of the method to handle impreciseness in human thinking. Fuzzy AHP is a synthetic extension of classical AHP that takes into consideration the fuzziness of the decision maker when mapping his/her perception to crisp numbers. AHP methodology and FST are extensively described in chapter 3 as they comprise part of the methodology proposed for green supplier segmentation Supplier Segmentation Supplier segmentation takes place after the selection/ evaluation phase (Rezaei & Ortt, 2012). Parasuraman (1980) was the first to introduce the concept of vendor segmentation. Although market/customer segmentation has been a tool widely applied by organizations since the 1950s in order to identify customer sub-groups with different characteristics, needs and requirements and adapt their strategy to maximize benefits, this approach had never been applied before to the other side of the supply chain to distinguish among vendor characteristics (Parasuraman, 1980; Day, et al., 2010). Parasuraman (1980) suggested that conventional vendor evaluation techniques developed till then had primarily focused on selecting a vendor based on an overall performance on several standard criteria. These techniques omitted opportunities to identify vendors who would potentially be more appropriate for a market segment than the resulted best-performing one. To overcome this inefficiency of supplier selection models, Parasuraman (1980) proposed a stepwise procedure for the identification of supplier segments in correspondence to customer segments as a means of identifying vendors potentially more suitable than current ones. These steps are described as follows: Step 1: Identification of key features of customer segments Step 2: Identification of critical vendor characteristics Step 3: Selection of relevant dimensions for vendor segmentation Step 4: Identification of vendor segments 26

28 Although Parasuraman was the first to suggest that segmentation should also be used on the supply-side he did not specify which decision variables should be used to distinguish among different segments; his work was limited to describing the process on how to identify these variables and form the segments (Rezaei & Ortt, 2012). Kraljic s portfolio matrix The greatest contribution on this field was done by Kraljic (1983) when he introduced a comprehensive portfolio method to help companies deal with supply risk and disruptions by integrating purchasing function as a key element in supply management. In his model, Kraljic considered two dimensions against which the materials purchased by the company should be assessed and classified: profit impact (y-axis) and supply risk (x-axis). Profit impact can be defined as the strategic importance of purchasing in terms of the value added by product line, the percentage of raw material in total costs and their impact on profitability. Supply risk refers to the complexity of the supply market, supply scarcity, monopoly or oligopoly conditions, entry barriers, material substitution possibilities, pace of technological advancements and logistics costs. The result is a 2x2 matrix distinguishing among four product categories each of which require a distinctive approach toward supplier management: Strategic items (high supply risk, high profit impact): These are items that demand established global suppliers with key performance criterion being long-term availability of the product. Bottleneck items (high supply risk, low profit impact): These are items that demand global, predominantly new suppliers with new technology with key performance criteria being cost management and reliable short-term sourcing. Leverage items (low supply risk, high profit impact): These are items that demand multiple suppliers locally positioned with key performance criteria being cost/price and material flow management. Non-critical items (low supply risk, low profit impact): These are items that demand established local suppliers with key performance criterion being functional efficiency. Kraljic s approach and its strategic implications is summarized in figure

29 Figure 2.4: Kraljic's portfolio segmentation tool (Kraljic, 1983) The evolution of supplier segmentation frameworks Over the years, the Kraljic matrix has become the standard in the field of purchasing portfolio models and inspired many practitioners and researchers gain a better insight of the possibilities of a portfolio approach for purchasing purposes (Gelderman & Weele, 2005). Despite its popularity, Kraljic s approach has also been the subject of severe criticism with respect to the following issues (Gelderman & Weele, 2005): The selection of segmenting variables The operationalization of dimensions The negligence of supplier s side and supplier-buyer relationship characteristics The lines of demarcation (what is the exact difference between high and low) In an effort to overcome these inefficiencies and exploit the potential benefits of supplier segmentation in decision making, research in the field across industries has generated a variety of tools using different segmentation variables and assessment criteria (Olsen & Ellram, 1997; Kaufman, et al., 2000; Bensaou, 1999; Tang, 1999; Dyer, et al., 1998; Weele, 2005; Hallikas, et al., 2005; Caniels & Gelderman, 2007; Svensson, 2004). For example, Olsen and Ellram (1997) suggested that a second portfolio matrix next to Kraljic s should also be developed to analyze the supplier-buyer relationships by categorizing suppliers under the dimensions of relative supplier attractiveness and the strength of the relationship (figure 2.5). Dyer et al. (1998) distinguished among only two categories of appropriate supplier relationship strategies namely strategic partnerships and durable arm s-length relationships based on product characteristics and supplier s management practices. Tang (1999), highlighting the importance of supplier-buyer relationship in strategic decisionmaking suggested selecting an appropriate supplier relationship strategy based on two determining factors: strategic importance of the part to the buyer and buyer s bargaining power (figure 2.6). Kaufman et al. (2000) proposed a supplier typology using the dimensions of technology and collaboration therefore focusing on supplier s technological capability and supplier-buyer relationship to determine an appropriate strategy per category (figure 2.7). Svensson (2004) used the dimensions of supplier s commitment to buyer and commodity s importance to buyer to distinguish among four 28

30 groups of suppliers in the automotive industry (figure 2.8). For a detailed overview of the various approaches developed for the purposes of supplier categorization and their assessment dimensions and bases till 2008 refer to Day et al. (2010). Figure 2.5: Analysis of supplier's relationships as an extension to Kraljic s model (Olsen & Ellram, 1997) Strategic importance of part to buyer Low Buyer s bargaining power Low High Exclusive Vendor supplier High Partner Preferred supplier Figure 2.6: Supplier Relationship Map (Tang, 1999) Technology Low High Low Commodity supplier Technology specialist Collaboration High Collaboration specialist Problemsolving supplier Figure 2.7: A supplier typology for the automotive industry (Kaufman, et al., 2000) Commodity s importance High Supplier s commitment High Low Business Family partner Low Friendly Transactional Figure 2.8: Supplier segmentation (Svensson, 2004) 29

31 The main problem with these segmentation tools is that the segmentation variables used vary significantly among them and a buyer who adopts one of the above mentioned tools can never be sure that he does not miss any critical variables when assessing the supply base (Rezaei & Ortt, 2012). Day et al. (2010) claimed that in all models reviewed in their research, there was limited understanding about why certain bases are used, what the most appropriate object of classification is, how bases can be evaluated together, and their consequent relationship to supply strategy and value creation. This is also the reason why companies nowadays usually combine more than one segmentation tools to be able to effectively distinguish among their suppliers. Rezaei & Ortt s supplier s potential matrix A new approach to supplier segmentation was introduced by Rezaei and Ortt (2012) which aimed to fulfill the need for a unifying conceptual framework that would include all important variables from previously proposed methods under two overarching dimensions: supplier capabilities and supplier willingness. They define supplier capabilities and supplier willingness as follows: Supplier s capabilities are complex bundles of skills and accumulated knowledge, exercised through organizational processes that enable firms to co- ordinate activities and make use of their assets in different business functions that are important for the buyer. Supplier s willingness is confidence, commitment and motivation to engage in a (long-term) relationship with the buyer. The combination of supplier s capabilities and supplier s willingness indicates supplier s position in the grid which is translated into supplier s potential to meet the buying firm s expectations. Supplier potential is defined as the buyer s perception of supplier capabilities and supplier willingness to engage and maintain a partnership to achieve mutual objectives. In their work Rezaei and Ortt (2012) have established a relatively complete set of variables for supplier evaluation, derived from the literature on supplier selection and buyer-supplier relationships, which they enlisted under the dimensions of capabilities and willingness. The main advantage of the proposed model is that this list of criteria can be customized to include variables from the supplier selection literature that measure a supplier s potential to contribute to buyer s specific strategic goals. This is particularly important for the problem under study as conventional criteria for supplier assessment such as price, delivery time and product quality would not add much value to achieving sustainability-related goals. Rezaei & Ortt s (2012) supplier segmentation tool together with its managerial implications is depicted in figure

32 Figure 2.9: Supplier's potential segmentation tool and its managerial implications (Rezaei & Ortt, 2012) Despite their differences and variety, the primary purpose of all supplier segmentation models, encountered in literature, is to provide an effective supporting tool for decision makers in determining appropriate supplier management strategies. These may include supplier substitution, maintaining arms-length relationships with the suppliers or investing in supplier development. While the first two of the possible strategies are quite straightforward in what they suggest, supplier development is a rather broad concept, a research field itself, which is worthwhile to further explore Supplier Development Firms are increasingly focusing on the performance capabilities and responsiveness of their supply base to withstand competition and those that encounter shortcomings in supplier performance or capabilities can (1) invest personnel, time and resources to increase performance (2) manufacture the purchased product in-house (3) search for an alternative supplier or (4) a combination of the above (Krause, 1999). However, threats to exclude suppliers from your chain, while they may have short term benefits, they can jeopardize market conditions in the longer term (Krause & Ellram, 1997). Rather than electing to insource the component or switch to another supplier, many buyers are now actively intervening in the activities of their suppliers in order to generate the required performance improvements (Cousins, et al., 2007; Monczka, et al., 1993). According to Krause et al. (2000) there are four main supplier development strategies: 1. Competitive pressure: Firms make use of multiple sourcing strategies to develop competitive pressure to their suppliers. The firm can then distribute the volume of 31

33 business across the supply base, ensuring that the best-performing supplier is rewarded with the higher volumes. This motivates other suppliers to improve their performance while maintaining pressure on the primary supplier to ensure their performance does not deteriorate. 2. Evaluation and certification systems: Routine supplier evaluation and feedback ensures that the suppliers are aware of their performance and the customer firm s expectation of their performance, and that they are provided with directions for improvement. Firms use formal evaluation systems and supplier certification programs to communicate these expectations, as well as motivate suppliers to improve performance. 3. Incentives: To motivate suppliers, the buyer firm may provide a range of incentives for improvements. These may include increased volume of current business, priority consideration for future business, sharing of achieved cost savings and recognizing supplier s improvements through awards. 4. Direct involvement: Capital and equipment investments in suppliers Partial acquisition of supplier firm Investment of human and organizational resources to develop supplier performance, also referred to as operational knowledge transfer activities (OKTA) (Modi & Mabert, 2007). These include training of supplier s engineers, on-site visits or mutual process adaptations designed to enhance the relationship. Research by Krause et al. (2000) suggests that the most effective strategy is one of direct involvement whereas strategies of supplier incentives and supplier evaluation and assessment provide a facilitating role in driving the success of the direct involvement effort. There is also a distinction in literature regarding development activities aiming at improving supplier s operational performance and those aiming at supplier s capability development. The first include short-term programmes where buyer works side-by-side with the supplier to improve specific dimensions and supplier s product lines that will contribute to achieving buyer s goals. This approach might give the buying firm a competitive advantage in short time but it fails to teach the supplier the underlying problem-solving techniques. The latter, on the other hand, focus on building the supplier s capability for improvement from within the organization by transferring to suppliers in-house capabilities and knowledge. Despite the fact that this approach might be more difficult to achieve, performance improvements can be greater over time (Cousins, et al., 2007). The supplier development process Handfield et al. (2000) defined a 7-step process in supplier development as depicted in figure

34 Step 1: Identify critical commoditi es Step 2: Identify critical suppliers Step 3: Form a cross functional team Step 4: Meet with supplier top manageme nt Step 5: Identify key projects Step 6: Define details of agreement Step 7: Monitor status and modify strategies Figure 2.10: Supplier development process (Handfield, et al., 2000) Steps 1 & 2: Identify critical commodities and critical suppliers Since resources for supplier development are limited, buying company should identify a strategic subgroup of commodities and suppliers that are deemed critical for the realization of its goal. Segmentation has a lot to offer in this stage. Pareto analysis is also a common approach as 20% of suppliers can often be responsible for 80% of the poor performance indicating that there is great improvement potential within a limited number of suppliers. (Handfield, et al., 2000) Step 3: Form a cross functional team Gaining internal consensus and support for the initiative and developing an internal cross-functional team is a critical step before approaching the supplier to ask for his cooperation and commitment. This is particularly important when a matrix organizational structure is in place, as is in our case study, where organizational complexity and internal interest conflicts can have a negative impact on the success of the project and leave suppliers in frustration. Step 4: Meet with supplier top management Top management involvement and commitment to the project has been identified as critical element in successful supplier development efforts in literature (Modi & Mabert, 2007; Humphreys, et al., 2004; Krause & Ellram, 1997). This applies to both supplier s and buying firm s management. Top management can push aside political barriers, assign resources and provide the drive to make change happen (Cousins, et al., 2007). While some suppliers may be open about the need for change, others may show resistance to accept that the process can be performed better as mandated by customer. Lack of trust is also often identified as the greatest barrier in sharing information and being open about their plants and operations, fearing for spillovers or increasing buyer s bargaining power (Handfield, et al., 2000). One effective way of getting 33

35 the supplier s top management support is to show how supplier development would lead to greater profits or better quality (Cousins, et al., 2007). Steps 5, 6 & 7: Project identification, agreement and monitoring In their study, Handfield et al. (2000) found that most organizations were able to deploy successfully the first three or four of these steps, yet were less successful with the final steps aimed at sustaining the supplier development effort. Identifying key projects that can yield high benefits to both parties, determining which costs to share and which to bear, as well as agreeing on established metrics and timelines that provide a basis for follow-up and joint problem solving, are issues that should be clearly addressed in initial supplier-buyer communications. Reviewing the progress of the program is also important in being able to take correcting action if necessary. Table 2.4 summarizes the most important barriers to supplier development as described by Cousins et al. (2007): Area Main Issues Buyer-specific Lack of top management commitment Small purchases spread across many suppliers Low importance of supplier Ambitious and unrealistic expectations Supplier-specific Lack of top management commitment Lack of buying firm s power creates reluctance for participation in an initiative Insufficient human resources Insufficient technical capabilities Buyer-supplier interface Lack of mutual trust Ineffective communication of potential benefits Insufficient inducements to the supplier Supplier is reluctant to share cost/process information Poor cultural alignment Table 2.4: Barriers to supplier development (Cousins, et al., 2007) In order to successfully implement supplier development activities buyer should be aware of these potential shortcomings and prepared to deal with them Environmental Concerns in Purchasing Sustainability and carbon footprint Sustainability: In 1987, the United Nations introduced the most widely recognised definition of sustainable development as the development that meets the needs of the present without compromising the ability of future generations to meet their own needs (UN, 1987). The idea of sustainability covers three areas: economy, society and environment. 34

36 Carbon Footprint: The total greenhouse gas (GHG) emissions caused directly and indirectly by an individual organization, event or product, and it is expressed as a carbon dioxide equivalent (CO 2 e) (Carbon Trust, 2010). In fact low carbon emissions are an element of wider environmental concerns which in turn are part of the wider sustainability agenda as illustrated in figure Other environmental concerns include water availability and quality, ecosystem preservation, deforestation or the release of toxic chemicals. Sustainability Ecconomic Environmental Social Water Footprint Carbon Emissions Toxic Chemicals Direct (Scope 1) Indirect (Scope 2+Scope3) Figure 2.11: From sustainability to low carbon procurement The 6 greenhouse gases (GHG) that mostly contribute to global warming phenomenon were defined in Kyoto Protocol Japan 1997, and are therefore also referred to as Kyoto gases (United Nations, 1998). These include carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF 6 ). In 2011 a revised edition by the UNFCCC introduced nitrogen trifluoride (NF 3 ) as the seventh global warming potential gas (UNFCC, 2011). The most intensive GHG that comes from human activities, particularly from energy generation and the burning of fossil fuels, is carbon dioxide (CO 2 ). Carbon dioxide equivalent (CO 2 e) is the unit of measurement which allows different GHGs to be compared on a like for a like basis relative to one unit of CO 2. CO 2 e emissions are calculated by multiplying the emissions of each of the seven GHGs by its 100-year global warming potential (GWP) (Carbon Trust, 2010). For example, 1 kg of methane (CH 4 ) traps an amount of heat in the atmosphere 25 times higher than 1 kg of CO 2 whereas nitrous oxide (NO 2 ) has a much higher GWP value of 298. The standard GWP value for carbon (CO 2 ) equals to 1 (IPCC, 2007) Product carbon footprint Lifecycle analysis (LCA) is an increasingly important tool for environmental policy in the industry (Franklin, 1995; Ayres, 1995). LCA considers the entire life cycle of a product, from raw material extraction and acquisition, to use and end-of-life treatment/disposal, and it is the tool used to quantify a product s carbon or in the extended version environmental- 35

37 footprint (Franklin, 1995). If the most environmentally harmful of these stages can be determined, then impact on the environment can be efficiently reduced by focusing on making changes for that particular phase. The result of an LCA is an emission factor expressed in kg CO 2 e per unit of product, process or activity. Depending on the extent to which they cover a product s life cycle there are different types of LCAs which include: Cradle-to-grave LCA: A full LCA from raw material extraction to disposal phase without material re-use. Transportation Raw material extraction Production Use Disposal Figure 2.12: Cradle to grave LCA Cradle-to-cradle LCA: Cradle-to-cradle is a specific kind of cradle-to-grave LCA, where the end-of-life disposal step for the product is a recycling process. It is a method used to minimize the environmental impact of products by employing sustainable production, operation, and disposal practices (William McDonough, 2002). The term C2C is widely used among organizations to describe and communicate their efforts towards new product development with reduced environmental impact. Manufacture Extraction of raw material Use Recycle Figure 2.13: Cradle to cradle LCA Cradle-to-gate LCA: Cradle-to-gate is an assessment of a partial product life cycle from resource extraction (cradle) to the factory gate (i.e., before it is transported to 36

38 the consumer). The use phase and disposal phase of the product are omitted in this case (Plastics Division of the American Chemistry Council, 2011). Raw material extraction Transportation to supplier s facilities Supplier s production activities Transportation to customer Figure 2.14: Cradle to gate LCA Gate-to-gate: Gate-to-gate is a partial LCA looking at only one value-added process in the entire production chain. Gate-to-gate modules may also later be linked in their appropriate production chain to form a complete cradle-to-gate evaluation (Jimenez-Gonzalez, et al., 2000). The development of lifecycle methodologies has its roots back in late 1960 s and early 1970 s. Environmental concerns at that time were characterized by a particular concern for resource depletion and inspired by the experience of the first oil crisis in 1973 (Hauschild, et al., 2005). Up until mid-90s, although significant progress had been made towards standardization of LCA methodology, results from life cycle inventory analysis of the same product from different researchers varied significantly (Keoleien, 1993; Ayres, 1995). Inadequate data and information has been identified as a primary obstacle for LCA. Collection of data for the environmental exchanges between processes in the product system and the environment is normally the most labor-intensive part of doing an LCA. To assist the inventory analysis, data has been collected in life cycle databases in a unit process form which allows them to be used as building blocks in different life cycle models (Burgess & Brennan, 2001). In 1996, the International Standards Organisation (ISO) initiated a global standardization process for LCA. Four standards were developed for LCA and its main phases and issued in the period from in ISO 1400 series of standards for Environmental Management (ISO ). These standards provide minimum requirements for the performance of life cycle assessment and define the framework for LCA (ISO, 2006). The standardization harmonized the use of the methodology and increased the credibility of the results. At the turn of the millennium LCA was widespread used among industries both as a marketing tool and a decision support tool in environmental management (Hauschild, et al., 2005). ISO 1400 series standards were revised in Corporate carbon footprint Corporate carbon footprint: Measures GHG emissions from all the activities across the organization, including energy used in buildings, industrial processes and company vehicles. Carbon Management: The measurement and management of emissions of carbon dioxide (CO 2 ) and of the other 6 greenhouse gases covered by the Kyoto Protocol (Chan, 2009). Activities include deciding measurement boundaries, identifying emissions sources, selecting methodologies, setting targets, defining priorities, 37

39 developing systems and procedures and engaging human resources and suppliers (Chan, 2009). Low Carbon Procurement (LCP): The process whereby organizations seek to procure goods, services, works and utilities with a reduced carbon footprint throughout their life cycle and/or leading to the reduction of the overall organizational carbon footprint when considering its direct and indirect emissions (Correia, et al., 2013). With the quest for sustainability and carbon management a company s responsibility is extended to cover not only its own processes but also activities triggered by the company s demands to its supply chain partners both upstream and downstream (Hauschild, et al., 2005). Following the principle you can only manage what has been measured, calculating your organizational carbon footprint is the first step towards reducing it. The first internationally recognized carbon management standard was the Greenhouse Gas Protocol developed in 2004 by World Resources Institute and World Business Council for Sustainable Development (WRI/WBCSD, 2004) as a response to the need for consistency in how organizations accounted and reported emissions. The categorization distinguished among 3 scopes of carbon emissions: Scope 1: Direct GHG emissions that occur from operations that are owned or controlled by the reporting company. Scope 2: Indirect emissions from the generation of purchased or acquired energy such as electricity, steam, heating or cooling, consumed by the reporting company. Scope 3: All other indirect emissions that occur in the value chain of the reporting company including both upstream and downstream emissions. At that time scope 3 (indirect) emissions were an optional reporting category consisted of six sub-categories employee business travel, waste disposal, contract or owned vehicles, outsourced activities, product use and production of purchased materials. In 2011 the new international GHG Protocol Accounting and Reporting for Product Life Cycle, and Corporate Value Chain Emissions were published with the aim to bring better clarity, support and methodological rigor to the difficult calculation of Scope 3 emissions (WRI/WBCSD, 2011). The protocol then distinguished clearly among upstream and downstream emissions forming 15 different emission categories. The 15 categories in scope 3 are intended to provide companies with a systematic framework to measure, manage, and reduce emissions across their value chain. The categories are designed to be mutually exclusive to avoid a company double counting emissions among categories. In 2013 a revised edition included the newly introduced NF 3 GHG and a more detailed methodology regarding the calculations for scope 3 emissions. An overview of a company s GHG emissions is depicted in figure

40 Figure 2.15: Overview of corporate GHG emissions (WRI/WBCSD, 2013) Scope 3 emissions are the most difficult for an organization to quantify since data collection is dependent on multiple third parties. From a procurement point of view, scope 3 emissions are highly relevant as they are caused directly somewhere in the supply chain (Correia, et al., 2013). What is even more impressive is the fact that these emissions often constitute the majority of the reporting company s carbon footprint. This is why Low Carbon Procurement is an important activity for a company that pursues sustainable growth Green supplier selection Recognizing the increased pressure posed to companies due to a growing environmental concern from governments but also the public, Lamming & Hampson (1996) were the first to suggest that environmental practices should be linked to supply chain management practices such as supplier assessment, total quality management and collaborative supply strategies. A year later, Noci (1997) suggested a model to support the decision-maker in the selection of the most effective supplier from an environmental viewpoint. His framework was consisted of four main criteria and 13 sub-criteria: Green competencies (availability of clean technologies, type of material used in supplied component, capacity to respond in time) Current environmental efficiency (air emissions, solid wastes, waste water, energy consumption) Green image (customer s purchase retention, type of relationship with stakeholders, market share related to green customers) Net life cycle cost (cost of supplied component, cost for component disposal, investments in improving environmental performance) 39

41 Since then, several researchers have started developing their own green vendor assessment systems by using a variety of criteria and methods to evaluate suppliers. Handfield et al. (2002) conducted a Delphi group study to develop a rational framework of the many different environmental performance indicators they had identified and came up with 10 top criteria for supplier environmental performance. Humphrey et al. (2003) developed a framework for incorporating environmental criteria into supplier selection process distinguishing between two types of criteria; quantitative and qualitative. The framework consisted of seven environmental categories (costs from improvement, cost from pollutant effect, management competencies, green image, design for environment, environmental management system and environmental competencies) made up of thirty variables. Tuzkaya et al. (2009) built a framework that would evaluate suppliers against 6 main criteria (green process management, pollution control, environmental and legislation management, green product, green image and environmental costs) and 31 sub-criteria. Lee et al. (2009) developed two sets of criteria and sub-criteria: one for the evaluation of conventional suppliers and one for the evaluation of green suppliers illustrating how assessment criteria should be adjusted to serve a particular goal. Govindan et al. (2013)developed a supplier assessment framework based on the three dimensions of sustainability (economic, environmental and social). By combining the studies of several previous researchers they used a manageable number of four main environmental criteria (pollution production, resource consumption, eco-design and environmental management system). In their model they have also introduced clear definitions of these 4 criteria which are derived by grouping criteria from previous literature. Pollution production: Average volume of air emission pollutant, waste water, solid waste and harmful materials releases per day during measurement period Resource consumption: Resource consumption in terms of raw material, energy and water during measurement period. Eco-design: design of products for reduced consumption of material/energy, design of products for reuse, recycle, recovery of material, design of products to avoid or reduce use of hazardous materials Environmental management system: Environmental certifications like ISO 14000, environmental policies, planning of environmental objectives, checking and control of environmental activities Hsu et al. (2013) focused on supplier selection criteria for carbon management issues rather than broader environmental criteria as in the previous studies. In their work they identified 13 relevant criteria which they categorized under three dimensions; planning, implementation and management. The most important contribution in reviewing developed approaches in the field can be found in the work of Govindan et al. (2013) who structurally reviewed literature in green supplier evaluation and selection from international scientific journals and conference proceedings, published from 1997 to In their work they document the results from 33 papers indicating in detail the industry in which the research was applied, the criteria and sub-criteria used in each framework and the final choice methodology. In total more than 100 evaluating criteria and sub-criteria were recorded. It is important to mention here that 40

42 one main issue in current literature is that, with the exception of (Govindan, et al., 2013), there is lack of clear definitions to these criteria leading to ambiguity in what they actually mean. According to Govindan et al. (2013) building accepted definitions and characteristics of these criteria before their implementation in decision models is necessary for the acceptance of the methodologies and for the decisions that derive from them. Another important observation is that all the frameworks developed for green supplier evaluation neglect the impact of supplier-buyer relationship. There is only one reference in the recently developed framework by Hsu et al. (2013) who use supplier collaboration as a criterion under the implementation dimension. A summary of the most frequently used environmental criteria as derived from the abovementioned literature is depicted in table 2.6. MCDM in green supplier selection Just like Ho et al. (2010) reviewed traditional supplier selection literature, Govindan et al. (2013) distinguished among the same 7 different methodological approaches and their hybrids in green supplier selection literature (they excluded SMART).The most widely used MCDM approach when developing a green vendor assessment framework was found to be AHP (Noci, 1997; Handfield, et al., 2002; Lee, et al., 2009; Grisi, et al., 2010; Lu, et al., 2007) followed by ANP (Buyukozkan & Cifci, 2012; 2011; Hsu & Hu, 2009). For both methods extended versions to utilize fuzzy sets are included. The rest of the approaches were encountered in the remaining 23 articles either individually or integrated in a hybrid method developed to deal with MCDM in green supplier evaluation Green supplier segmentation Despite the increased popularity of incorporating sustainability criteria in supplier selection, developing a green supplier segmentation tool has received little attention in literature. Cousins et al. (2007) proposed extending Kraljic s model to add a third dimension for environmental costs as a method to integrate environmental concerns with regular commercial analysis. However, they did not specify which measurement variables should be used to account for this new dimension. Their approach is limited to introducing the importance of developing a green supplier segmentation rather than actually developing one. 41

43 Figure 2.16: Extended Kraljic's model to incorporate environmental concern (Cousins, et al., 2007) Zhu et al. (2010) were the first to propose a comprehensive portfolio-based analysis for green supplier management and their classification model together with its managerial implications is depicted in figure In their model they used the classification dimensions of supplier s relative power and supplier s overall performance to distinguish among four groups of suppliers that require a distinctive approach towards supplier management. Supplier s relative power can be measured based on 4 factors used to describe buyer s relative dependence on the supplier. These include: logistical indispensability, need for supplier s technological expertise, availability of alternative suppliers and buyer s switching costs (Zhu, et al., 2010). In order to measure supplier s overall performance, Zhu et al. (2010) suggest the use of criteria from both traditional and green supplier selection literature. In this way, performance dimension is made up of 14 assessment variables divided in three clusters as shown in table 2.5 with the majority of them representing conventional criteria. Strategic performance measures Cost Quality Time Flexibility Process management Innovativeness Organizational factors Culture Technology Relationship Environmental factors Pollution control Pollution prevention Environmental management system Resource consumption Pollution production Table 2.5: Supplier's overall performance evaluation criteria and sub-criteria (Zhu, et al., 2010) 42

44 Figure 2.17: Supplier green classification portfolio model and its managerial implications (Zhu, et al., 2010) In order to solve the MCDM classification problem, Zhu et al. (2010) implemented ANP methodology using an illustrative example. However their model was not tested in a real world situation. What is more, it can be claimed that their model does not target to an exclusive green segmentation tool. Apart from the use of a relative large number of criteria not related to sustainability aspirations under the overall performance dimension, the dimension of supplier s relative power is solely based on general factors characterizing supplier s dependence on buyer and it shows little to no specificity to sustainability concerns. This work is the first attempt to develop a pure green supplier segmentation tool and apply it in a real world situation that can assist managers decide upon different management approaches towards their supply base with the specific goal to improve their environmental performance. To do so, the model developed by Rezaei & Ortt (2012) as presented in section 2.2 has been selected to serve as the basis for segmenting suppliers using fuzzy AHP methodology. The main reason for this choice is the flexibility their model offers in customizing the list of criteria to include variables that measure a supplier s potential to contribute to buyer s specific strategic goals. Rezaei & Ortt (2012) included several criteria measuring supplier s environmental performance in their list, but their research was limited to three relevant papers (Noci, 1997; Handfield, et al., 2002; Humphreys, et al., 2003). It can also be claimed that these criteria have received relative low importance in subsequent implementation of 43

45 their model and once again conventional criteria have been chosen to assess suppliers such as price, delivery, quality, reserve capacity, geographical location and financial position (Rezaei & Ortt, 2013). In our model we look for: Criteria assessing supplier s capabilities in terms of reducing the carbon footprint of the raw materials they sell to the specific customer. Criteria assessing supplier s willingness to work either independently or in collaboration with the buyer towards the fulfillment of the buyer s objective which is again the reduction of carbon footprint of the materials bought. For this reason measurement criteria adopted by researchers in the field of green supplier selection will be used to customize and enrich the set of capabilities enlisted by Rezaei & Ortt (2012). Table 2.6 summarizes the most frequently encountered criteria in green supplier selection literature that can be enlisted under the capability dimension. The table includes only two variables from the conventional supplier evaluation literature, which are also adapted to represent environmental concerns. Choi and Hartley (1996) included performance awards in their list of supplier selection criteria. Even though at that time they referred to company s financial performance, supplier s environmental performance awards are an indication not only of supplier s environmental efforts but of actual achievements. The same holds for supplier s reputation (Kannan & Tan, 2002) if we limit the application of the concept to the environmental aspect. Table 2.7 describes the criteria that can influence a supplier s willingness to engage in carbon reductions either on his own initiative or in collaboration with the buyer. These criteria are extracted from Rezaei & Ortt s (2012) list as they are applicable to both conventional and green supplier evaluation. Work from Prahinski and Benton (2004) who elaborated on buyer-supplier communication types has also been included. Effective inter-organizational communication has been heavily emphasized for effective supplier development (Modi & Mabert, 2007)and is characterized by four main facets: frequency, direction, content and modality (Prahinski & Benton, 2004). 44

46 Variables of supplier s capabilities in terms of reducing the carbon footprint of their products 1 Technology capability/ Availability of clean technology (Noci, 1997) (Humphreys, et al., 2003) (Lee, et al., 2009), (Zhu, et al., 2010) 2 Green packaging (Handfield, et al., 2002) (Lee, et al., 2009) (Wong, et al., 2012) 3 Green product design/ Design for environment (Humphreys, et al., 2003) (Lee, et al., 2009) (Tuzkaya, et al., 2009) (Wong, et al., 2012) (Govindan, et al., 2013) 4 Resource consumption (raw material, energy, water) (Noci, 1997) (Humphreys, et al., 2003) (Lee, et al., 2009) (Zhu, et al., 2010), (Wong, et al., 2012) (Govindan, et al., 2013) 5 Pollution control/reduction capability (Noci, 1997) (Humphreys, et al., 2003) (Tuzkaya, et al., 2009) (Lee, et al., 2009) (Zhu, et al., 2010) (Wong, et al., 2012) (Govindan, et al., 2013) 6 Use of environmentally friendly material (Noci, 1997) (Humphreys, et al., 2003) (Tuzkaya, et al., 2009) (Lee, et al., 2009) (Wong, et al., 2012) 7 Green Image (Noci, 1997) (Humphreys, et al., 2003) (Tuzkaya, et al., 2009) (Lee, et al., 2009) 8 Public disclosure of environmental record (Handfield, et al., 2002) (Hsu, et al., 2013) 9 EMS, ISO (Handfield, et al., 2002) (Humphreys, et al., 2003) (Lee, et al., 2009) (Tuzkaya, et al., 2009) (Zhu, et al., 2010) (Govindan, et al., 2013) 10 Management competencies (support, partners, training) (Humphreys, et al., 2003) (Tuzkaya, et al., 2009) (Hsu, et al., 2013) 11 Performance awards (Choi & Hartley, 1996) 12 Reputation of supplier (Kannan & Tan, 2002) Table 2.6: Capabilities criteria for green supplier evaluation 45

47 Variables of supplier s willingness to engage in carbon reductions from the raw materials 1 Inter organizational communication (Prahinski & Benton, 2004) 2 Buyer-supplier relationship closeness (Choi & Hartley, 1996) (Kaufman, et al., 2000) 3 Supplier s commitment to buyer/dependency (Kaufman, et al., 2000) (Prahinski & Benton, 2004) (Hallikas, et al., 2005) 4 Effort in eliminating waste (Kannan & Tan, 2002) 5 Commitment to continuous process/product improvement (Kannan & Tan, 2002) (Svensson, 2004) (Urgal-Gonzalez & Garcia- Vasquez, 2007) 6 Willingness to invest in specific tech (Urgal-Gonzalez & Garcia- Vasquez, 2007) 7 Long term relationship (Choi & Hartley, 1996) 8 Communication openness (Choi & Hartley, 1996) (Smeltzer, 1997) (Kannan & Tan, 2002) 9 Open to site evaluation (Kannan & Tan, 2002) 10 Willingness to share info ideas (Smeltzer, 1997) (Kannan & Tan, 2002) 11 Honest and frequent communication (Kannan & Tan, 2002) Table 2.7: Willingness criteria for green supplier evaluation (Rezaei & Ortt, 2012) In chapter 4, where the proposed model is applied in a real world case, it is made explicit how criteria from tables 2.6 and 2.7, used for assessing suppliers, are related to the sustainability goal. The proposed differentiated management approaches for different types of suppliers are presented in chapter Conclusion Since environmental concerns have been extended to cover an organization s total value chain, and with the majority of the problem often lying in sourcing of raw materials, theory on strategic purchasing and supplier management can be leveraged to develop a green supplier management approach that can assist organizations in their efforts towards improving their environmental performance via their suppliers. Supplier management is mainly consisted of three basic elements: supplier selection/evaluation, supplier segmentation and supplier development which form a cyclical process. Although research in green supplier selection has attracted an increasing interest in the past decade the same does not hold in green supplier segmentation. Rezaei and Ortt s (2012) potential segmentation has been selected as the most appropriate basis, to serve the green objective, due to its flexibility in customizing the list of criteria to include variables that measure a supplier s capability and willingness to contribute to buyer s specific strategic goals. Capability criteria can be adopted from green supplier selection literature whereas willingness criteria may remain in accordance to the authors initial list as they can be expressed in a way to be applicable in both traditional and green segmentation. After this segmentation is developed, the next step should be to describe appropriate management strategies based on supplier development theory for the different groups of suppliers resulting in the development of an integrated green supplier management approach, focusing on carbon-performance criteria. 46

48 3. Methodology This chapter provides the description of the methodology used in order to answer the main research question based on the theory developed in chapter 2. In order to assist managers in their efforts to efficiently manage suppliers in terms of their environmental performance, theory in carbon footprint measurement, supplier segmentation, green supplier selection and supplier development will be leveraged to provide a management framework. The result is an extended green version of Rezaei & Ortt s (2012) potential segmentation tool. Criteria will be adapted to represent green supplier capabilities and willingness to contribute to buyer s specific objective and supplier s carbon impact category will be integrated as a third dimension. This will allow the distinction among more groups of suppliers allowing to decision makers not only to customize further action based on their suppliers position in the grid but also prioritize efforts based on their size. As illustrated in chapter 2 supplier selection can be formulated as a Multi Criteria Decision Making (MCDM) problem where a set of alternatives A i (i= 1, 2,, n) are evaluated against a number of quantitative as well as qualitative criteria C j (j=1, 2,, k) with different weights w i assigned to them. Criteria C 1 C 2 C k Alternatives w 1 w 2 w n A 1 a 11 a 12 a 1n A 2 a 21 a 22 a 2n A n a 11 a 11 a 11 Table 3.1: MCDM problem representation Although the implementation of MCDM in supplier evaluation is abundant in literature, with researchers applying a variety of techniques and models to solve the problem and make the final selection (figure 2.3), the same does not hold in the supplier segmentation field. Rezaei & Ortt (2012; 2013) proposed two methodological approaches, a fuzzy-rule based system and a DEA-like linear programming model, to segment suppliers. In a subsequent study they applied a fuzzy preference relations-based AHP as a more robust mathematical procedure to derive final supplier s scores (Rezaei & Ortt, 2013). Although not applied using real-world data, Zhu et al. (2010) implemented ANP methodology in an illustrative example, in order to solve the MCDM classification problem in their green supplier segmentation model. In chapter 2 we saw that the most widely MCDM approach used when developing a green vendor assessment framework is the Analytic Hierarchy Process method (AHP) including its combinations with Fuzzy Set Theory (FST) (Govindan, et al., 2013). Its popularity is mainly due to the fact that it is easy for managers and decision-makers to understand it and apply it, its capability to handle both qualitative as well as quantitative data and its ability to handle impreciseness in human judgment when combining theory on fuzzy logic. In this research, fuzzy Analytic Hierarchy Process (FAHP) is also applied to segment suppliers and extensively described in this chapter. Sections 3.1 and 3.2 provide the basis of AHP and FST while section 3.3 builds upon the fuzzy AHP that will be used in this research. Section

49 describes the methodology followed in order to assign suppliers to a carbon impact category and finally in section 3.5 an overall stepwise approach is proposed for the implementation Analytic Hierarchy Process (AHP) Analytic Hierarchy Process was first introduced by Thomas Saaty (1977) as an effective tool to deal with complex decision making. The process starts by describing the problem in a hierarchical structure including in the highest level an overall (quantifiable) goal further decomposed in criteria and sub-criteria whereas in the lowest level alternative solutions to attain the goal are found. The approach is applicable in situations where decision-makers (DM) and experts are available. The DM needs to define the goal and can distinguish alternative solutions to attain it whereas experts are required to evaluate the alternative solutions based on criteria (Rezaei, et al., 2013). A typical problem structuring when using AHP is depicted in figure 3.2, in the end of this section. After structuring the problem, the next step in AHP is to compute the weights for the different criteria. To do so, pairwise comparison matrices are constructed to assess how they contribute to the goal, starting from the first level of criteria and continuing to lower levels, comparing criteria on the same level under the same nod. Each matrix A is a real matrix where n is the number of evaluation criteria considered. Elements in the matrix (a ij ) represent the importance of criterion i th relative to criterion j th whereas they satisfy the following constraints: a ij *a ji = 1 and a ii = 1. Based on the above the pairwise comparison matrices have the following format: A = a ij = - for a ij > 1, criterion i is considered more important than j - for a ij < 1, criterion i is considered less important than j - for a ij = 1, criterion i is considered equally important to j Then, DM needs to evaluate the criteria in terms to their relative importance. Saaty (1977) proposed a numerical scale from 1 to 9 to assess the relative importance between two criteria as follows: Value of a ij Interpretation 1 i and j are equally important 3 i is slightly more important than j 5 i is more important than j 7 i is strongly more important than j 9 i is absolutely more important than j 2, 4, 6, 8 intermediate values Table 3.2: Numerical scale proposed by Saaty 48

50 Once the pairwise comparison matrices are built it is possible to derive the criteria weight vector ŵ = (w 1, w 2,.., w n ) T by applying a mathematical process, for example calculate the eigenvector of matrix A (Saaty, 1998), use the Least-Square Method (LSM) (Chu, et al., 1979) or a fuzzy preference programming method (Mikhailov, 2000). The resulting criteria weight vector should fulfill the requirement:. When more than one layer is involved, this method results in the calculation of local priorities (w i ). Global priority vector (ŵ i ), against which alternatives should be evaluated, are obtained at the lowest level of sub-criteria for all main criteria clusters by consecutively multiplying local priorities. An additional step includes the comparison of alternative solutions by experts using the lowest level of sub-criteria per main-criteria category. Finally the MCDM problem as depicted in figure 3.1 is structured and solved, where weights w i represent global priority vector ŵ i. Based on their final score, that is the result of a weighted average, the alternative solutions are ordered in terms of their ability to attain the goal. Goal Level 0 Local weights Criterion 1 Criterion 2 Level 1 Local weights Local weights Sub criterion 1.1 Sub criterion 1.2 Sub criterion 1.3 Sub criterion 2.1 Sub criterion 2.2 Level 2 Global weights Alternative A1 Alternative A2 Alternative A3 Alternative A4 Consistency Figure 3.1: Problem structure in AHP When many pairwise comparisons are performed some inconsistencies may arise. AHP allows checking the consistency of pairwise comparisons and acts as a feedback mechanism 49

51 for decision makers to review and revise the judgment (Saaty, 1977). This integrated verification operation is the main differentiator among AHP and other MCDM approaches (Govindan, et al., 2013). Saaty (1977) has proposed a consistency index (CI), which is related to the eigenvalue method applied in matrix A, for which: where n: dimension of the matrix and λ max = maximal eigenvalue. If CI/RI < 0.1, the pairwise comparison matrix is characterized by an acceptable level of consistency. RI is a random index (the average CI of 500 randomly filled matrices), the values of which have been predefined by Saaty (Saaty, 2001) for problems with n 10 as indicated in table 3.3. n RI Table 3.3: Values of the Random Index (RI) (Saaty, 2001) Despite its widespread adoption AHP has also received some considerable criticism mainly regarding the consistency and realism of its results. The most challenging step of AHP methodology involves quantifying the expert judgment using crisp values as shown in table 3.1. This makes the methodology inefficient when it comes to dealing with vague and imprecise knowledge (Rezaei, et al., 2013). To overcome this inefficiency, Laarhoven & Pedrycz ( (1983) proposed the integration of Fuzzy Set Theory (FST) in AHP and the use of Triangular Fuzzy Numbers (TFNs) as a means to handle the ambiguity in existing human judgments and improve the realism of decision maker s perception Fuzzy Set Theory (FST) Fuzzy Set Theory (FST) was first introduced by Zadeh (1965) in an effort to accommodate fuzziness contained in human language, judgment and evaluation and it has been widely integrated by researchers with other MCDM techniques. Definition 1: A fuzzy set is a class of objects with a continuum of membership grades, where the membership grade can be taken as an intermediate value between 0 and 1. A fuzzy subset A of a universal set X is defined by a membership function f A (x) which maps each element x in X to a real number [0,1] (Govindan, et al., 2013). There are two types of fuzzy numbers commonly used in FST namely: triangular fuzzy numbers and trapezoidal fuzzy numbers. Definition 2: A fuzzy number N on ( ) is defined to be triangular (TFN) if its membership function μ N (x): [0,1] is equal to: ( ) { (1) 50

52 where l and u are the lower and upper bound of the support of N, respectively, and m stands for the modal value (l m u). This triangular fuzzy number can be noted by the triple (l, m, u). Figure 3.2: The triangular fuzzy membership function (Laarhoven & Pedrycz, 1983) The operational laws of two TFNs N 1 = (l 1, m 1, u 1 ) and N 2 = (l 2, m 2, u 2 ) are as follows. Fuzzy number addition : N 1 N 2 = (l 1, m 1, u 1 ) (l 2, m 2, u 2 ) = (l 1 + l 2, m 1 + m 2, u 1 + u 2 ) Fuzzy number multiplication : N1 N2 = (l 1, m 1, u 1 ) (l 2, m 2, u 2 ) (l 1 l 2, m 1 m 2, u 1 u 2 ) Fuzzy number division (/): N 1 (/) N 2 = (l 1, m 1, u 1 ) (/) (l 2, m 2, u 2 ) ( l 1 /u 2, m 1 /m 2, u 1 /l 2 ) Definition 3: A fuzzy number N on ( ) is defined to be trapezoidal if its membership function μ w (x): [0,1] is equal to (Amindoust, et al., 2012): ( ) (2) { where a and d are the upper and lower bound of the support w, respectively, and [b,c] stands for the modal range. The trapezoidal fuzzy number can be noted as (a, b, c, d). 51

53 Figure 3.3: The trapezoidal fuzzy membership function (Amindoust, et al., 2012) Triangular fuzzy numbers are easier for decision makers to use and calculate relative to trapezoidal and most frequently encountered in academic research. The integration of FST in AHP suggests the replacement of crisp values, as proposed by Saaty (1980), with fuzzy numbers to account for the vagueness in expert judgments. However, it does not deal with the problem of inconsistency and keeping a good rank preservation and precision in weights when evaluating alternatives (Mikhailov, 2000). As already mentioned, in the application of AHP researchers have used a variety of more sophisticated methods to get a ranking of priorities from a pairwise matrix in an effort to increase the performance of the model and overcome consistency issues. A well-recognized approach is Mikhailov s (2000) who used fuzzy preference programming (FPP) to derive the priority vector in a fuzzy AHP and solved several shortcomings related to inconsistency in previous AHP. Rezaei et al. (2013) proposed an improved version of Mikhailov s model that allowed taking into account non-linearity and skewness of reciprocal numbers. The fuzzy AHP that will be applied in this research is based on these two contributions and is extensively presented in the next section. Definition 4: A fuzzy positive reciprocal matrix Ã= [ã ij ] is consistent if and only if ã ik ã kj ã ij where the fuzzy positive matrix Ã= [ã ij ] is reciprocal if and only if ã ji = ã -1 ij and ã ii = 1 i (Buckley, 1985) Fuzzy AHP using fuzzy preference programming (FPP) In this approach, the aim is to determine the relative weight of the criteria w = (w 1, w 2,, w n ) T such that the ratios w i /w j are approximately within the scopes of the pair wise judgment ã ij, or equivalently (Mikhailov, 2000): l ij u ij (3) However, for each i and j, there may be many w i and w j that satisfy inequality (3) and different ratios w i /w j, providing different DM s satisfaction, can be measured by a membership function which Mikhailov and Tsventinov (2004) describe as follows: 52

54 ( ) (4) This membership function can take the following values: { ( ) ( ) (5) ( ) (6) The FPP aims at finding the optimal crisp priority vector w* of the fuzzy feasible area P on the (n-1) dimensional simplex Q n-1 with the following membership function: { (7) ( ) { ( ) (8) Based on the fact that this membership function is a convex set, Mikhailov and Tsvetinov (2004) claimed that there is always a priority vector w* Q n-1 that has an optimum degree of membership: ( ) { ( ) (9) Finally, in order to solve the fuzzy prioritization problem, Mikhailov and Tsvetinov (2004) make use of the maximin decision rule, known from the game theory. The maximin prioritization problem can be represented in the following way: maximize λ subject to ( ) (10) Taking into consideration the form of the membership function describing DM s satisfaction for different combinations of w i /w j (equation 4), the maximin problem (equation 10) is transformed into a bilinear program. 53

55 Improved fuzzy AHP using FPP The membership function described in equation 4 is linearly increasing over the interval (-, m ij ) and linearly decreasing over the interval (m ij, ). However, this is not the case for reciprocal fuzzy numbers. Although, Mikhailov s method guarantees the preservation of preference intensities and provides a well interpretative consistency index it fails to deal with the skewness and non-linearity of reciprocal fuzzy numbers. This problem was overcome by Rezaei at al. (2013) who proposed an improvement in Mikhailov s model in order to be applicable to both types of triangular fuzzy numbers. Type 1 TFNs: (1,.., 9) where the satisfaction of decision maker is represented by the membership function in equation 4. Type 2 TFNs: (1/9,..,1) where the satisfaction of decision maker is represented by the membership function in equation 11. ( ) (11) { Using this addition and based on the same logic as followed by Mikhailov and Tsvetinov (2004) the improved fuzzy AHP problem using fuzzy preference programming (FPP) can be expressed in the following way (Rezaei, et al., 2013): max λ s.t ( ) ( ) for Type 1 fuzzy numbers (12) ( ) ( ) for Type 2 fuzzy numbers i=1,...,n-1 j=2,,n j>i k=1,, n Equation 12: FPP to derive optimal crisp priority vector The above problem description consists a non-linear programming problem that results in the optimal priority vector w* and a consistency index λ*. For λ* 0 the pairwise comparisons can be considered consistent whereas λ*< 0 indicates that comparisons are strongly inconsistent. 54

56 Based on the above the proposed fuzzy AHP methodology to derive criteria weights in this research is described in the following 4 steps. Step 1: Establish the hierarchy In this step we hierarchically structure the problem as depicted in figure 3.1 to include the main goal, criteria, sub-criteria and alternatives. Step 2: Sort criteria in terms of order of preference/importance to decision maker This is an additional step that is proposed in this research when applying the model. The main aim of this step is to guarantee that the order of importance of the criteria as derived by the mathematical process applied later is consistent with the actual order of preference in decision-maker s perception. It serves as an automatic mechanism to prevent preference inconsistencies during comparisons, especially when a large number of criteria are involved. Step 3: Construct pairwise comparison matrices Following step 2, criteria are sorted in pair-wise comparison matrices in terms of their importance to the decision maker. Linguistic variables are used to determine relative importance of the criteria in n(n-1)/2 comparisons and are then translated to Type 1 TFNs (I, m, u) as depicted in table 4.3. Step 4: Derive crisp priority vector In this final step the priority vector is derived by solving the non-linear problem described in equation 12 only for Type 1 fuzzy numbers due to the addition of step Carbon Impact Assessment In this section a 4 step methodology is described in order to assign a supplier to an impact category related to the size of his contribution to the company s carbon footprint and extend the supplier segmentation tool. These steps, which are quite straightforward and include simple calculations, are based on the theory developed in section Calculate supplier s total carbon footprint as follows: (13) ( ) ( ) It therefore follows that: (15) 2. Sort suppliers (and raw materials) based on the size of their footprint. (14) 55

57 A good allocation of carbon emissions that come from a company s supply base provides a solid starting point in understanding which suppliers/materials are the major contributors and where the greatest improvement margins lie. A proposed visualization of carbon emissions from a company s purchased goods is depicted in figure 3.4. R 1 R 2 R k Total S i S 1 CF 11 CF 12 CF 1k S 2 CF 21 C 22 CF 2k S n CF n1 CF n2 CF nk Total R j - S 1 : supplier with the highest carbon impact (sorted) - R 1 : raw material with the highest carbon impact (sorted). Total CF Figure 3.4: Visualization of supplier s carbon impact in the company s footprint The table contains CO 2 e emissions per raw material per supplier (CF ij ) and incorporates 2 main advantages: It takes into account the synergistic effects of multiple raw materials sourced from one supplier and multiple suppliers for one raw material. It serves for prioritizing action when looking for the solution to the problem from two different perspectives: supplier-based and material-based. o Supplier-based approach (rows): When a supplier is highly contributing to the company s footprint as a result of the materials he provides, questions arise regarding supplier s resource and energy efficiency in his facilities. Investing in process improvement in this case could positively affect the footprint of multiple raw materials purchased by the reporting company resulting in a greater carbon reduction than focusing on one material. Knowledge on supplier s characteristics and supplier-buyer relationship in this case is the foundation for further discussions. o Material-based approach (columns): In this case questions arise related to the potential of product improvement or substitution. This time, knowledge on the characteristics of the product and its importance to business is required and demands the involvement of people with technical expertise for the identification of alternative solutions. Of course, in the case where multiple suppliers are involved in the production of the material the need to 56

58 identify who does so with the lowest footprint shifts again the attention to supplier-rather than product- evaluation. The two approaches are not independent of each other; what differs is how the table is interpreted from people in different functions across the organization. People in R&D and Production can come up with different solutions than people in Purchasing. Communication and knowledge share among these departments can further improve decision-making. 3. Calculate suppliers cumulative relative frequency (contribution) to the organization s total footprint. ( ) (17) represents supplier s relative contribution to the total footprint and ( ) The following requirement should be met: (19) 4. Divide suppliers into impact categories based on their contribution. For example, we can distinguish among three impact categories as follows: o High impact: 50% o Medium impact: 50 < 80% o Low impact: 80% < 100% As in the case of assessing supplier s capabilities and willingness, with the purpose to distinguish among four different groups that demand different management approaches, it is also recommended to group suppliers depending on the size of their impact (nominal scale) rather than maintain the ratio scale used for the assessment. The suggestion mainly concerns the visualization of results in a way that decision-makers can quickly interpret. The order of supplier s impact should also be made visible (ordinal scale) as a note accompanying the graph and this will be illustrated in the application of the methodology. Of course, supplier s impact is already expressed in step 2 and steps 3-4 can be omitted in cases where a small and manageable number of suppliers are involved. The thresholds suggested in this research are relatively objective. The 80% cut-off point is initiated by the Pareto Principle, also known as the Rule of 80/20, which suggests that in many circumstances 80% of the contribution comes from 20% of the items (Sanders, 1992). For more detail, a third category is introduced that involves suppliers responsible for half of the total contribution. In general, the larger the number of suppliers more categories with different thresholds can be introduced in order to create manageable groups for prioritization of action. 57

59 Willingness (W) 3.5. Extended green supplier segmentation In this section a 6 step methodology is proposed in order to build a green supplier segmentation tool based on the theory described so far. These steps include: 1. Calculate supplier s carbon footprint and assign him to an impact category (section 3.4). 2. Determine a set of criteria that measure supplier s capabilities and willingness to contribute to the objective as derived from the literature and summarized in tables (2.6) and (2.7), or adapt them in consultation with the decision makers. Clearly define the criteria. 3. Assess supplier s performance by experts (buyers) with respect to each criterion. 4. Determine the weights of the criteria by applying the fuzzy AHP methodology (section 3.3, steps 1-4). 5. Determine the final aggregated 2-dimensional score as the resulting weighted average of steps 3 and Map suppliers in the extended segmentation grid incorporating impact dimension (C, W, I) as depicted in figure 3.5. High Impact (I) : Low impact : Medium impact : High impact Low Low Capabilities (C) High Figure 3.5: Supplier's potential segmentation extended to include impact category 3.6. Conclusion In this chapter we described step by step the process for developing a green supplier segmentation with the purpose to decide upon further management strategies that will assist a company achieve its carbon reduction targets relevant to its supply base. Building upon Rezaei and Ortt s (2012) segmentation, we have selected to apply the improved AHP using FPP (Rezaei, et al., 2013), in order to evaluate suppliers, against a set of criteria representing green capabilities and willingness to contribute to buyer s environmental aspirations. Reasons for this choice include the model s ability to handle both qualitative and quantitative criteria, its ability to deal with fuzziness in human thinking, the fact that it takes into account non-linearity and skewness of reciprocal numbers and the fact that it allows to 58

60 check for consistency in results. A modification to the methodology has been suggested which includes predetermining the order of preference of the criteria by decision makers, as a means to preserve consistency during pairwise comparisons and reduce complexity for decision makers. Furthermore, supplier s impact has been added as a third dimension in the segmentation, distinguishing among high, medium and low impact suppliers as a means of prioritizing action when a large number of suppliers are involved in the company s chain. Supplier s impact assessment has resulted in a 2-dimensional matrix which provides information on suppliers, as well as materials carbon contribution to the company s emissions. Although this research focuses on developing a supplier management approach, the results from impact assessment could also be used for developing a material-based approach; however, material assessment demands for more technical expertise. Therefore, collaboration among departments that can provide input on both the dimensions of the matrix can improve decision making. 59

61 4. Application 4.1. The company Royal DSM is a global science-based company active in health, nutrition and materials. The company delivers innovative solutions to markets worldwide like food and dietary supplements, personal care, feed pharmaceuticals, medical devices, automotive, paints, electrical and electronics, life protection, alternative energy and bio-based materials. The company is listed on NYSE Euronext. It employs more than people around the world and has annual net sales of around 9 billion (DSM, 2013). Royal DSM is made up of 8 Business Groups (BGs), four of them active in Material Sciences and the rest in Life Sciences, all of which act as independent units (figure 1.1). Purchasing organization in DSM Purchasing is a globally-operating functional group that consists of a central DSM Sourcing unit and Business Group Purchasing departments, under the leadership of the Chief Purchasing Officer. This matrix organization aims for world-wide collaboration between all purchasing employees across the company. There is also a distinction made among two different spend areas, namely: Chemicals and Utilities (Direct): Raw materials purchased and further processed to provide the company s products. Indirect Goods and Services: All other goods purchased that are not directly processed into products but are necessary to the company s operations, such as facility goods and services (FG&S), physical distribution (PD) and technical goods and services (TG&S). This research has been conducted in the central Sourcing department and it is limited to suppliers of raw materials (direct spend category). Sustainability in DSM Sustainability consists one of the company s growth drivers together with high growth economies, innovation and acquisitions/partnerships. Following the definition published from United Nations in 1987, the company creates sustainable value around three dimensions: people, planet, profit (3Ps). With the belief that sustainability will be the key differentiator value driver in the coming decades, the concept is is an integral part of the company s operations, strategic actions and decisions across all functions including purchasing. DSM takes sustainability explicitly into account in the selection, evaluation and development of suppliers and has lunched Global Supplier Sustainability Program (Global SSP) as a means of achieving its environmental ambition targets. 60

62 Figure 4.1: DSM's Global Supplier Sustainability Program (DSM, 2013) This program covers both local and global suppliers and is comprised of two main elements: compliance and supplier solutions. It involves a three step approach: First, a Supplier Code of Conduct sets sustainability guidelines for suppliers Second, supplier self-assessment questionnaires allow them to measure their activities and sort suppliers into risk categories Third, supplier audits are used for large or high-risk suppliers based on spend, country, cluster/category type, security of supply and specific business needs. These audits focus on identifying opportunities for joint improvement plans. Also, since 2012, DSM strives for full adherence to the Greenhouse Gas Protocol, as defined in 2011 under the international Corporate Accounting and Reporting Standard, Revised Edition. This means that the company reports not only emissions that come directly from its own production processes but also emissions coming from the value chains in which it operates Problem Description As mentioned in chapter 2, the GHG protocol distinguishes among 15 different categories of scope 3 emissions. However, sector specific guidance is also developed by Working Groups in WBCSD and an example includes guidance for the chemical sector accounting and reporting (WBCSD Chemicals, 2012). According to the guidance only 11 categories are considered relevant to the chemical sector and five of them have been identified as material for the company based on their business impact and societal impact, in addition to the estimated size and the availability of data. As can be seen in figure 4.2, company s footprint from its own operations and facilities (scope 1) and from purchased electricity (scope 2) account only for 4.2 million tons of CO 2 61

63 equivalent. Purchased goods and services on the other hand have by far the largest impact on organization s total carbon footprint. Figure 4.2: GHG emissions in DSM's Value Chain (DSM, 2013) In 2010 the company calculated the baseline carbon footprint of the raw materials purchased, which was at that time 10 million tons, and set a reduction ambition level of 20% by In 2012, when the company first reported according to the scope 3 GHG standard, GHG emissions from raw materials purchased amounted to 11.8 million tons. This can be partially attributed to the great number of acquisitions the company went through during the past 3 years but still efforts need to be intensified to achieve the target set. 62

64 4.2. Application of extended green supplier segmentation methodology In section 3.5 an overall methodology comprised of 6 steps is described in order to derive a green supplier segmentation that will act as a supporting tool for decision makers involved in green supplier management. Sections provide a detailed description of how the methodology was applied in DSM, what have been the resources and access methods used as well as the results from the analysis. Interpretation of the tool and its managerial implications will be discussed in chapter Step 1: Assign supplier to a carbon impact category The methodology described in section 3.4 is implemented to allocate emissions to suppliers and assign them to an impact category of low, medium, high. Calculating carbon emissions requires the use of two types of data: activity data and emission factors. Activity data is a quantitative measure of a level of activity that results in GHG emissions (e.g. kg purchased, km driven, hours spent etc.). The mass of raw materials purchased and the corresponding suppliers are extracted from the company s database for the period June 2012-May 2013 and can be considered to be highly accurate and company-specific. The dataset included approximately 230 raw materials provided by 242 suppliers. Emission factor is a factor that converts activity data into GHG emissions data (e.g. kg CO 2 e emitted per liter of fuel consumed or per kg of material produced etc.) (WRI/WBCSD, 2013). It is the result of a cradle-to- supplier s gate LCA as mentioned in chapter 2 and should vary when changes occur in the system. However, due to the time-consuming process of carrying out a detailed LCA, emission factors are derived from available databases that describe industry averages. This approach has certain limitations which will be discussed in chapter 5. Based on this, the following table format has been prepared for the company s various business groups as well as for the organization as a whole. This table aims to assist the company prioritize its action following either a supplier or a material approach as mentioned in chapter 3 and further analyzed in chapter 5. Figure 4.3: DSM's carbon impact visualisation with emission factors 63

65 Supplier's capability to reduce product's carbon footprint Three impact categories have been defined to distinguish among suppliers and the final results are shown in table 4.12: High impact suppliers (suppliers that belong in the range 0-50% contribution): 10 suppliers Medium impact suppliers (50%-80%): 22 suppliers Low impact suppliers (80%-100%): 210 suppliers Step 2: Determine criteria and construct hierarchy of the problem In consultation with the company supervisor, criteria have been limited to the ones that purchasing directors would be both able and willing to answer (not too long, not too detailed). The framework for supplier s potential assessment is depicted in figures 4.3 and 4.4 for capability and willingness respectively. They represent the hierarchy of the problem with its goal, criteria and sub-criteria. Personel Management Competencies External recognition Senior mgt support Network Carbon disclosure Pollution control Green Design Transportation New eco+ product development Energy efficiency/ems Figure 4.4: Problem structure_capability assessment 64

66 Supplier's willingness to work towards this direction Oppenness to site evaluation Oppenness to info sharing Communication oppenness/richness Trust Commitment to buyer Commitment to sustainability Figure 4.5: Problem structure_ willingness assessment As Govindan et al. (2013) indicate it is critical to clearly define these criteria before asking experts to assess alternatives against them. Tables 4.1 and 4.2 provide the definitions of the criteria used in this research and which in several cases describe a combination of criteria and sub-criteria encountered in the literature and summarized in tables 2.6 and 2.7. For example external recognition as a capability describes a combination of performance awards and reputation criteria as identified in table 2.4. Commitment to sustainability is a more general willingness criterion that is measured based on supplier s efforts in eliminating waste, commitment to continuous process/product improvement with regard to sustainability and willingness to invest in specific technologies which in our case are characterized as clean technologies. These three components are derived from table

67 Criteria Sub-criteria Definition C1: Management competencies C11: Senior management support C2: Green Design C21: Energy efficiency/ems C3: External recognition C4: Carbon disclosure Degree to which senior management supports and encourages advancements and investments in reducing the environmental impact from the processes in place and seeks for collaboration opportunities with partners alongside the supply (value) chain of his products. C12: Personnel Degree to which the supplier has experienced and trained personnel in monitoring, managing and communicating the environmental performance of company's operations and this personnel can easily be tracked from supply chain partners. C13: Network Degree to which supplier participates in global organizations for sustainable development and collaborates with NGOs, external consultants to tackle environmental challenges (e.g. WBCSD, WRI, Greenpeace, external consultants specializing in improving a company's footprint). Degree to which supplier invests in improving energy efficiency of his operations either by reducing the amount of energy consumed or by turning to alternative types of energy for his operations. C22: Eco+ Degree to which supplier invests in new product development with reduced environmental footprint (eg. Biobased). C23: Transport and packaging Degree to which the supplier invests in optimizing transportation with a consideration towards the environment and use environmentally friendly product package. Degree to which supplier has a good reputation in effective environmental management and has received performance awards from recognized institutions (e.g. DJSI, VBDO, Awards, etc). Degree to which supplier publicly discloses the environmental performance from his operations (eg. Sustainability report, Carbon Disclosure Program) and how detailed and transparent the reporting is. C5: Pollution control Degree of supplier's capability in reducing pollution from his activities throughout the course of time Table 4.1: Definitions of criteria used to assess green supplier capabilities 66

68 Criteria Definition W1: Commitment to Degree to which DSM constitutes an important customer for the buyer supplier and supplier tries to stand up to DSM's expectations. W2: Commitment to Degree to which sustainability constitutes an important aspect in sustainability supplier's strategic development (supplier's efforts to reduce the environmental footprint of his operations through product/process improvement, waste elimination and new cleaner technology ). W3: Open to info Degree to which the supplier would be willing to share information sharing related to the environmental performance of his operations and the carbon footprint of his products. W4: Open to site 1: Supplier has refused audit 2: Supplier has not been asked but would evaluation probably deny site evaluation 3: Supplier has not been asked but there are chances for the supplier to accept 4: Supplier would most probably show a favourable attitude towards the evaluation 5. Supplier has been audited or has declared his oppenness to site evaluation. W5: Trust Degree to which DSM can rely on supplier's input, self-assessment and fulfillment of their expectations. W6: Communication a. Degree to which the supplier tries to discuss DSM's evaluation of his richness performance so as to clarify expectations and exploit improvement opportunities. b. Medium and content richness. Medium refers to the method used to transmit information (least rich: electronic data transfer, rich: teleconferences, richest: face to face meetings). Content refers to the type of information transmitted (least rich: purely transactional or richer: more strategic). Table 4.2: Definitions of criteria used to assess supplier's willingness to contribute to carbon reduction goal Logic behind criteria selection and categorization under capability and willingness dimensions While some of the criteria used to assess suppliers are already enlisted in Rezaei and Ortt s (2012) framework many of them have been derived from green supplier selection literature and hereby enlisted under the dimensions of capability and willingness. It is important to demonstrate why the selected criteria constitute an indication of supplier s potential to contribute to buyer s specific goal which is exclusively related to the reduction of carbon emissions in his supply chain. Tables 4.3 provides the logic behind selection and categorization of criteria under the two dimensions. Buyers can assess suppliers against these criteria based on information that is publicly available on the internet, information directly provided by the supplier or their past experience with supplier. In this research, attention has been given to include criteria not too technical and specific, which buyers would have little chances of knowing, but limit the assessment to those that can be relatively easily accessed by buyers. 67

69 Criteria C1: Management competencies Logic Indication of the degree to which supplier has integrated sustainability in his strategy. Suppliers scoring high in this criterion show higher chances for effective carbon management in their operations, therefore producing products with a lower footprint. C2: Green Design This criterion is relatively more difficult for buyers to assess than the rest. However, known investments from supplier s side to improve environmental performance of his operations are expected to yield results. Sub-criteria used to measure supplier s green design capability are limited to the ones that directly affect carbon footprint of products supplied, from cradle to gate. C3: External recognition C4: Carbon disclosure C5: Pollution control W1: Commitment to buyer W2: Commitment to sustainability W3: Open to info sharing W4: Open to site evaluation Suppliers who score high in this criterion have already been positively assessed for their environmental performance by renowned institutions, making clear that they take efficient action towards sustainable development. This information is usually easy to find as companies are benefited from publishing relevant achievements. The participation of companies in carbon disclosure programs indicates that there is an existing built-in monitoring and reporting system which increases the chances of the supplier having in place also a managing system to improve his performance. This information can also be easily retrieved usually from a company s annual report. A supplier showing a continuous improvement in terms of his environmental performance throughout the years is more likely to produce the same product with a continuously improved footprint. Indication of supplier s willingness to invest resources in order to meet buyer s objectives. This is a general criterion assessing supplier-buyer relationship which however, also demonstrates supplier s reaction in a potential demand from buyer s side related to sustainability. Indication of supplier s willingness to improve the environmental performance of his operations even on his own initiative. Suppliers that score high can be more receptive in discussions related to sustainability and show more willingness in undergoing identified improvements. Information on environmental performance is not something that suppliers are always willing to share. For example, energy consumption in a supplier s facilities is also a strong indication of production costs and suppliers might fear for price pressures during negotiations. However, if this or other relevant information does not become available to the buying company these suppliers have little to contribute to company s carbon management efforts. A supplier who is open to buyer s visits to his facilities can greatly facilitate the assessment process of current environmental performance and the identification of improvement areas in the supply chain. W5: Trust Trust plays a major role in supplier-buyer relationship, especially in this case where the exchange of sensitive information is required in order to attain the goal. W6: This is a general criterion assessing current supplier-buyer communication Communication regardless if it is related to sustainability issues. It can however still serve richness as an indication of the degree of easiness in integrating sustainability among other issues in supplier s agenda. Table 4.3: Logic behind criteria assessing supplier's potential to contribute to buyer's carbon reduction goals 68

70 Step 3: Assign a score to each supplier with respect to these criteria Due to the excessive number of suppliers in the company s value chain priority has been given to assess high contributors as identified by the carbon impact assessment. To do so, buyers from the various business groups (BG) had to be contacted. Customized excel files were prepared each time, before contacting the interviewee, that included the names of the suppliers that are relevant to the particular BG. In the case where multiple BGs were involved with a supplier the assessment was done by the BG bearing most of the supplier s impact. A buyer from each BG was initially contacted and provided information on which suppliers is he/she in charge of and what other buyers should be contacted for the rest. Purchasing directors and buyers were initially contacted via in which they found information about the project and the questionnaire. According to their geographical position a meeting was arranged for the assessment either face to face or via online communication system. In total 20 buyers participated in the survey which resulted in the assessment of 50 suppliers. It must be noted here that the questionnaire intended to measure in a scale from 1 to 5 buyer s perception on how they think the suppliers perform rather than actual supplier performance. The results of this process are shown in tables 4.11 and Step 4: Determine the weights of the criteria Construct pairwise comparison matrices: Three experts on sustainability issues were gathered together to conduct the pairwise comparisons with regard to the different criteria. Experts were first asked to reach a consensus and rank the criteria in terms of their importance/contribution to the goal to be able to maintain consistency. Then, the pairwise comparisons followed using linguistic variables which were later translated into triangular fuzzy numbers as described in table 4.4. Linguistic Variables Triangular fuzzy numbers Just Equal JE (1,1,1) Weakly more important W (1,2,3) Weakly to moderately more WM (2,3,4) important Moderately more important M (3,4,5) Moderately to strongly MS (4,5,6) more important Strongly more important S (5,6,7) Strongly to extremely more SE (6,7,8) important Extremely more important E (7,8,9) Absolutely more important A (8,9,9) Table 4.4: Linguistic variables in fuzzy AHP 69

71 This also resulted in using only Type 1 fuzzy numbers (1-9) for the analysis. The outcome of this process is described in tables The tables illustrate the second phase of the process after sorting the criteria based on experts order preference. Management competencies Green Design External recognition Carbon disclosure Pollution control Management competencies (1,1,1) (1,2,3) (3,4,5) (4,5,6) (7,8,9) Green Design (1,1,1) (2,3,4) (3,4,5) (6,7,8) External recognition (1,1,1) (1,2,3) (2,3,4) Carbon disclosure (1,1,1) (2,3,4) Pollution control (1,1,1) Table 4.5: Pairwise comparisons_capabilities (C) main criteria Senior mgt support Personnel Network Senior mgt support (1,1,1) (2,3,4) (7,8,9) Personnel (1,1,1) (4,5,6) Network (1,1,1) Table 4.6: Pairwise comparisons_ Management competencies (C1) sub-criteria Energy New eco+ product Transportation Energy (1,1,1) (1,2,3) (7,8,9) New eco+ product (1,1,1) (4,5,6) Transportation (1,1,1) Table 4.7: Pairwise comparisons_ Green design (C2) sub-criteria Commitment to buyer Commitment to sustainability Open to info sharing Open to site evaluation Trust Communication richness Commitment to buyer Commitment to sustainability Open to info sharing Open to site evaluation Trust Communication richness (1,1,1) (1,2,3) (2,3,4) (3,4,5) (4,5,6) (6,7,8) (1,1,1) (1,2,3) (2,3,4) (3,4,5) (4,5,6) Table 4.8: Pairwise comparisons_ Willingness (W) criteria (1,1,1) (1,2,3) (2,3,4) (3,4,5) (1,1,1) (1,2,3) (2,3,4) (1,1,1) (1,2,3) (1,1,1) 70

72 Criteria Derive crisp priority vector: In this step the priority vector is derived by solving the non-linear problem described in equation 12 only for Type 1 fuzzy numbers. To do so, excel solver has been used. The resulting priority vector for capability and willingness criteria is illustrated in tables 4.9 and 4.10 respectively. λ= 0 C1: Management competencies Criteria weights Sub-criteria λ 1 =0,854 λ 2 =0,742 Sub-criteria weights Local weights C11: Senior mgt support C12: Personnel C13: Network C2: Green Design C21: Energy efficiency/ems C22: Eco C23: Transport and packaging C3: External recognition C4: Carbon disclosure C5: Pollution control Table 4.9: Criteria weights for capabilities Criteria Criteria Weights λ= 0,317 W1: Commitment to buyer W2: Commitment to sustainability W3: Open to info sharing W4: Open to site evaluation W5: Trust W6: Communication richness Table 4.10: Criteria weights for willingness Step 5: Determine the final aggregated 2-dimensional score per supplier In this step suppliers receive an aggregated score for their capability and willingness to contribute to the company s goal by applying a weighted average method using the results from steps 3 and 4. Supplier s final aggregated scores are shown in tables 4.11 and Weights Supplier C11 C12 C13 C21 C22 C23 C3 C4 C5 Capab ility , , , ,81 71

73 , , , , , , , , , , , , , , , , , , , , , , , , , , , ,5 3, , , , , , , , , , , , , , , , , , ,21 Table 4.11: Supplier's capability score 72

74 Weights Supplier W1 W2 W3 W4 W5 W6 1 Willingness , , ,5 3, , , ,5 3, ,5 4, , , ,5 3, ,5 2, ,5 2, , , ,5 3, , , , , , ,5 4, , ,5 3, ,5 4, ,5 3, , , , , , , , , , , , , ,5 2, , , , , , ,5 3,80 1 Communication richness is the average result of two sub-questions in the questionnaire. 73

75 , , , ,5 4, , , ,85 Table 4.12: Supplier's willingness score Step 6: Integrate impact dimension in the extended segmentation grid Apart from their performance in capability and willingness criteria, suppliers have also been assessed based on their contribution to the company s carbon footprint in step 1. This information is integrated in the segmentation tool as a third dimension. Table 4.13 provides the final outcome of the combined analysis which is visualized in figure 4.6. Supplier Impact category Capability Willingness 1 High 3,37 4,69 2 High 3,82 4,96 3 High 3,66 3,05 4 High 3,81 4,16 5 High 4,26 4,21 6 High 3,57 3,98 7 High 3,10 4,72 8 High 3,69 2,76 9 High 4,51 4,36 10 Medium 2,34 3,98 11 Medium 2,07 2,45 12 Medium 3,28 2,82 13 Medium 3,44 4,38 14 Medium 3,41 4,69 15 Medium 4,16 3,87 16 Medium 3,89 4,52 17 Medium 3,44 2,13 18 Low 3,19 4,69 19 Low 3,71 4,96 20 Low 2,07 1,14 21 Low 3,31 4,61 22 Low 2,73 2,63 23 Low 3,61 3,36 24 Low 3,73 4,29 25 Low 3,16 3,09 26 Low 3,66 4,70 27 Low 3,20 3,76 28 Low 2,11 1,42 29 Low 4,58 3,69 30 Low 2,88 2,20 31 Low 4,57 4,70 32 Low 3,58 4,18 33 Low 3,20 4,21 74

76 34 Low 3,05 3,06 35 Low 2,86 3,48 36 Low 3,04 2,33 37 Low 3,44 2,98 38 Low 4,76 5,01 39 Low 3,05 3,22 40 Low 2,27 2,49 41 Low 2,63 1,87 42 Low 2,88 3,85 43 Low 3,69 3,80 44 Low 2,63 1,87 45 Low 2,63 2,49 46 Low 2,69 3,41 47 Low 3,73 4,72 48 Low 2,71 2,49 49 Low 3,17 3,88 50 Low 3,21 2,85 Table 4.13: Combined carbon impact and potential assessment results Figure 4.6: Extended green supplier segmentation in DSM at corporate level 75