Theoretical Framework of DEA in Sustainable Suppliers Selection: History
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Katerina Fotova Čiković
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Ivana Martinčević
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Supply chains and their management and governance are a complex system that strives to minimize costs and maximize the level of service, and its focus today should primarily be on the selection of sustainable (green) suppliers. Sustainable (green) suppliers improve and assist sustainable business models in the field of supply chain management. With an emphasis on sustainability and environmental care, the selection of green suppliers should be a central component and goal in supply chain management.

  • data envelopment analysis (DEA)
  • sustainable supplier selection
  • supply chain

1. Introduction

Purchasing and supply chain management have a key role in creating a company’s competitive edge. Moreover, purchasing represents “a strategic role in supply chain management for a firm and is the driver of competitive advantage” [1]. Therefore, the selection of suppliers in any business is one of the vital decision-making processes that have a major impact on the business’ viability and sustainability. “Supplier selection is an important decision-making problem which involves many quantitative and qualitative factors incorporating vagueness and imprecision” [2]. The importance of a supplier selection whose actions affect the performance and productivity of the supply chain is a process that requires quality assessment, evaluation, and, ultimately, the selection of suppliers that will contribute to an increase in the overall efficiency of the supply chain, reduced procurement costs, as well as an increase in market competitiveness [3].
However, the changes in the societies, as well as the increasing importance of corporative social responsibility, have imposed even greater importance on the selection and cooperation with sustainable, eco, and green suppliers.
The concept of sustainable development, which is connected to the selection of sustainable suppliers, emphasizes the need and necessity of the company to change the current business, values, rules, attitudes at all levels, activities, and business processes. Economic development and growth and the process of globalization and internationalization of society and markets cannot be stopped, but it is necessary to think in the direction of ensuring a healthy, sustainable, and green society, and a quality economy that will have less harmful effects on the environment. Therefore, it is no wonder that the selection of sustainable suppliers that ensures sustainable development is one of the key elements needed to formulate and implement development policies in the world. The key factor for a successful supply chain is the selection of sustainable suppliers but also the selection of the best sustainable strategy in the supply chain. Application of the DEA model in the process of selecting sustainable suppliers and the best sustainable strategy in the supply chain contributes to increasing the effectiveness and efficiency of the entire supply chain. It also contributes to the sustainability concept in the supply chain design [4].
This encapsulates the reason why DEA methodology has been of interest for researchers regarding the process of selection of sustainable suppliers. DEA is the leading non-parametric approach for measuring the relative efficiency and benchmarking of peer decision-making units (DMUs). Ever since its introduction in 1978 by the seminal paper of Charnes, Cooper, and Rhodes [5][6], it has been widely recognized as one of the most effective methodologies for measuring efficiency and performance. Moreover, there are some specific areas in which DEA has been most applied [7].

2. Theoretical Framework

2.1. Sustainable/Green Suppliers

Supply chains enable and represent the flow of goods, services, and information without which it is impossible to imagine the normal functioning of the market. They include and are part of the primary, secondary, and tertiary sectors and cover the entire process and flow of production [8]. Supply chains create added value in the business process, which is why it is necessary to manage the supply chain in order to create added value constantly. The supply chain management process includes an “integrated process that includes planning and managing all stakeholder selection activities, procurement of materials, transformation of materials into the final product, as well as related logistics activities within the entire chain” [9] (p. 187). In order to examine the efficiency of the supply chain, the “Game-theoretical Design Technique” approach was tested. The design approach allows easier access to business decisions within the supply chain when the supply chain is exposed to some uncertain parameters and ensures agile, cooperative, and resource-efficient design of multi-stage supply chains [10].
The supply chain is evolving rapidly, and while this development follows the technology development, it also intends to ensure sustainable development. Many equate sustainable development with economic and social growth. Sustainable development is the aspiration of society to achieve sustainable economic growth to the extent that it will meet the needs of present and future generations. It is necessary to preserve production capacity for the long term while achieving social goals such as increasing real income per capita, improving hygiene and nutrition, educational achievement, access to resources, equitable distribution of wealth, and increasing freedom [11][12][13][14]. The key goal of any organization is to ensure sustainability and create sustainable business models. Such an approach and business model rely primarily on the selection of sustainable and green suppliers that will ensure the path to sustainable development. Supply chains and their management and governance are a complex system that strives to minimize costs and maximize the level of service, and its focus today should primarily be on the selection of sustainable (green) suppliers.
Sustainable (green) suppliers improve and assist sustainable business models in the field of supply chain management. With an emphasis on sustainability and environmental care, the selection of green suppliers should be a central component and goal in supply chain management [15]. Suppliers and supplier relationship management create several benefits for the company—from creating long-term and loyal partners to creating greater visibility through better communication with them. Choosing a reliable business partner is the task of the management of companies that want to achieve long-term competitiveness and sustainability in the market. Sustainable (green) supplier selection and assessment are the most significant and complex challenges for supply chain managers [16][17]. Supplier selection is “intrinsically related to the Multi-Criteria Decision Making (MCDM) problem” [18]. Moreover, the process of selecting a supplier represents a “key competence in the sourcing function” [19]. Today, everything is intensified through environmental (green) factors that have, i.e., should have an impact on the selection of reliable and green suppliers. The question of how to identify and select sustainable suppliers can be answered through a system of weights that determine environmental factors as important decision factors when selecting suppliers by changing the DEA methodology [20]. One of the problems that arises in the business environment today is the selection of suppliers, primarily the selection of sustainable suppliers. Quality and proper selection of suppliers enable and facilitate the cross-efficiency fuzzy DEA technique. This method evaluates the efficiency of the supply chain and all its members (primarily in this—suppliers) with the correct ranking which then allows the correct selection of suppliers and later reflects on the overall efficiency, costs, and delivery time within the supply chain [21].
Choosing sustainable suppliers means choosing business partners with the most beneficial monetary value on the one hand and the least harmful impact on society and the environment on the other [22]. Assessing the sustainability and performance of the supplier and the supply chain itself requires a selection of optimal tools and methods for the selection process, where DEA is an important tool for measuring the performance of sustainable suppliers and sustainable supply chains [23]. That the DEA methodology and the evaluation index method are an objective and quantitative tool for evaluating and selecting sustainable (green) suppliers is a fact confirmed by numerous researchers through their empirical research [23][24][25][26][27][28][29][30].
However, considering the importance of suppliers in the strategy framework of supply chains, it is rather surprising that “the sourcing function has not been subject to more focused research on the development of adequate decision support tools” [19].

2.2. Data Envelopment Analysis (DEA)

The DEA is a linear mathematical programming technique that is used for the evaluation of the performance (i.e., the relative efficiencies) of a group of complex entities referred to as Decision-Making Units (DMUs) [31]. DEA is one of the most widely applied non-parametric methodologies ever since its introduction in 1978 by Charnes, Cooper, and Rhodes (1978) [5][6] that has grown into a powerful mathematical and linear programming technique. Namely, according to [7], there are five areas in which DEA has been most applied, and these are Agriculture, Banking, Supply Chain, Transportation, and Public Policy. Research conducted in the field of supply chain related to planning and resource management in intermodal terminals proves that the application of the DEA model allows planning decisions under conflicting requirements (the DEA method provides data on the number, capacity, and allocation of resources to address increasing flows) [32]. Moreover, the positive implication regarding the usage of the DEA method in the supply chain is used “to determine the efficiency of the rescheduled timetable (in terms of reduction of delays and maximization of robustness) and to rank alternatives according to their efficiency values [33] (p. 256).
DEA is a non-statistical and non-parametric approach that “makes no assumptions regarding the distribution of inefficiencies or the functional form of the production function (although it does impose some technical restrictions such as monotonicity and convexity)” [34], which represents one of its main advantages over parametric methodologies. It is a “data-oriented“ method that converts multiple inputs to multiple outputs when evaluating peer units—DMUs [35]. DEA focuses on the extreme observations, which is its main distinction from the parametric methodologies that “focus on average tendencies and deviations from it”. Moreover, DEA can employ multiple inputs and outputs, while parametric methodologies can only employ one output, which represents one of their biggest limitations [36]. This makes DEA “an excellent data-oriented efficiency analysis method” when using multiple inputs and outputs and “a useful performance evaluation and decision-making tool” [37].
DEA identifies the relative efficient DMUs in the observed sample that shape the efficiency frontier that measures the inefficiency of inputs or outputs of the other DMUs in the sample by comparing them and benchmarking with the relative efficient DMUs. Thus, DEA is also an econometric frontier method. The DMUs that lie on the frontier have the best relative efficiency, whereas the ones that are inefficient lie below the efficiency frontier [38]. Moreover, the results from the DEA vary from 0 to 1 (or 0 to 100%), with 1 being relatively efficient and a result below 1 being relatively inefficient. Thus, this approach enables a simple comparison of the DMUs in the sample [39][40].
Two basic DEA models are named in honor of their founders (CCR—Charnes, Cooper, and Rhodes and BCC—Banker, Charnes, and Cooper [5][6]). The CCR model employs a constant return to scale (CRS) assumption, i.e., “the output variables increase proportionally with input variables” [41], whereas the BCC DEA model employs a variable returns-to-scale (VRS) assumption, assuming that the proportional change in inputs does not necessarily lead to a proportional change in the outputs. The CCR model is graphically represented as a straight line, whereas the BCC model is represented by a convex hull [42].
However, it should be kept in mind that DEA is a methodology that explores the relative (and not absolute) efficiency within the analyzed sample of peer decision-making units [43]. The limitations and downsides of DEA are often scholarly researched, but most agree that “its advantages outweigh its limitations” and DEA should be considered “a significant diagnostic tool” [31].

This entry is adapted from the peer-reviewed paper 10.3390/su14116672

References

  1. Dutta, P.; Jaikumar, B.; Arora, M.S. Applications of data envelopment analysis in supplier selection between 2000 and 2020: A literature review. Ann. Oper. Res. 2021, 1–56.
  2. Bao, X.; Li, F. A methodology for supplier selection under the curse of dimensionality problem based on fuzzy quality function deployment and interval data envelopment analysis. PLoS ONE 2021, 16, e0253917.
  3. Roostaee, R.; Izadikhah, M.; Lotfi, F.H.; Rostamy-Malkhalifeh, M. A Multi-Criteria Intuitionistic Fuzzy Group Decision Mak-ing Method for Supplier Selection with VIKOR Method. Int. J. Fuzzy Syst. 2012, 2, 1–17.
  4. Moghaddas, Z.; Tosarkani, B.M.; Yousefi, S. A Developed Data Envelopment Analysis Model for Efficient Sustainable Supply Chain Network Design. Sustainability 2022, 14, 262.
  5. Charnes, A.; Cooper, W.; Rhodes, E. Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 1978, 2, 429–444.
  6. Cooper, W.W.; Seiford, L.M.; Tone, K. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, 2nd ed.; Springer: New York, NY, USA, 2007.
  7. Emrouznejad, A.; Yang, G.L. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Econ. Plan. Sci. 2018, 61, 4–8.
  8. Crkvenčić, M.; Buntak, K.; Krpan, L. Upravljanje Lancima Opskrbe; Sveučilište Sjever: Koprivnica, Croatia, 2018.
  9. Zelenika, R.; Plic Skender, H. Upravljanje Logističkim Mrežama; Ekonomski Fakultet Sveučilišta u Rijeci: Rijeka, Croatia, 2007.
  10. Cavone, G.; Dotoli, M.; Epicoco, N.; Morelli, D.; Seatzu, C. A Game-theoretical Design Technique for Multi-Stage Supply Chains under Uncertainty. In Proceedings of the 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, 20–24 August 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 528–533.
  11. Borozan, Đ. Makroekonomija; Ekonomski Fakultet u Osijeku: Osijek, Croatia, 2006.
  12. Pravdić, V. Sustainability and Sustainable Development: The Use in Policies and the Ongoing Debate on These Terms. Croa-Tian Int. Relat. Rev. 2001, 7, 93–100.
  13. Solow, R.M. An almost practical step towards sustainability. Resour. Policy 1993, 19, 162–172.
  14. Pearce, D.W.; Barbier, E.; Markandya, A. Sustainable Development: Economics and Environment in the Third World; Elgar, E., Hants, G.B., Brookfield, V.T., Eds.; Routledge: Abingdon, UK, 1990.
  15. Amindoust, A.; Shamsuddin, A.; Saghafinia, A. Using Data Envelopment Analysis for Green Supplier Selection in Manufac-turing under Vague Environment. Adv. Mater. Res. 2012, 622–623, 1682–1685.
  16. Mahdiloo, M.; Saen, R.F.; Lee, K.-H. Technical, environmental and eco-efficiency measurement for supplier selection: An extension and application of data envelopment analysis. Int. J. Prod. Econ. 2015, 168, 279–289.
  17. Shabanpoura, H.; Fathi, A.; Yousefi, S.; Saen, R.F. Ranking sustainable suppliers using congestion approach of data envelop-ment analysis. J. Clean. Prod. 2019, 240, 118190.
  18. Wang, C.-N.; Tsai, H.-T.; Ho, T.-P.; Nguyen, V.-T.; Huang, Y.-F. Multi-Criteria Decision Making (MCDM) Model for Supplier Evaluation and Selection for Oil Production Projects in Vietnam. Processes 2020, 8, 134.
  19. Hatami-Marbini, A.; Hekmat, S.; Agrell, P.J. A strategy-based framework for supplier selection: A grey PCA-DEA approach. Oper. Res. Int. J. 2022, 22, 263–297.
  20. Dobos, I.; Vörösmarty, G. Green supplier selection and evaluation using DEA-type composite indicators. Int. J. Prod. Econ. 2014, 157, 273–278.
  21. Cavone, G.; Dotoli, M.; Epicoco, N.; Morelli, D.; Seatzu, C. Design of Modern Supply Chain Networks Using Fuzzy Bargaining Game and Data Envelopment Analysis. IEEE Trans. Autom. Sci. Eng. 2020, 17, 1221–1236.
  22. Moheb-Alizadeh, H.; Handfield, R. Sustainable supplier selection and order allocation: A novel multi-objective pro-gramming model with a hybrid solution approach. Comput. Ind. Eng. 2019, 129, 192–209.
  23. Bai, C.; Sarkis, J. Determining and applying sustainable supplier key performance indicators. Supply Chain Manag. 2014, 19, 275–291.
  24. Shi, P.; Yan, B.; Shi, S.; Ke, C. A decision support system to select suppliers for a sustainable supply chain based on a systematic DEA approach. Inf. Technol. Manag. 2015, 16, 39–49.
  25. Jauhar, S.K.; Pant, M.; Nagar, M.C. Differential evolution for sustainable supplier selection in pulp and paper industry: A DEA based approach. Comput. Methods Mater. Sci. 2015, 15, 118–126.
  26. Kumar, A.; Jain, V.; Kumar, S.; Chandra, C. Green supplier selection: A new genetic/immune strategy with industrial applica-tion. Enterp. Inf. Syst. 2016, 10, 911–943.
  27. Jain, V.; Kumar, S.; Kumar, A.; Chandra, C. An integrated buyer initiated decision-making process for green supplier selection. J. Manuf. Syst. 2016, 41, 256–265.
  28. Zarbakhshnia, N.; Jaghdani, T.J. Sustainable supplier evaluation and selection with a novel two-stage DEA model in the presence of uncontrollable inputs and undesirable outputs: A plastic case study. Int. J. Adv. Manuf. Technol. 2018, 97, 2933–2945.
  29. Boudaghi, E.; Saen, R.F. Developing a novel model of data envelopment analysis–discriminant analysis for predicting group membership of suppliers in sustainable supply chain. Comput. Oper. Res. 2018, 89, 348–359.
  30. Sharafi, H.; Soltanifar, M.; Hosseinzadeh Lotfi, F. Selecting a green supplier utilizing the new fuzzy voting model and the fuzzy combinative distance-based assessment method. EURO J. Decis. Processes 2022, 10, 100010.
  31. Fotova Čikovič, K.; Lozić, J. Application of Data Envelopment Analysis (DEA) in Information and Communication Technol-ogies. Teh. Glas. 2022, 16, 129–134.
  32. Cavone, G.; Dotoli, M.; Epicoco, N.; Seatzu, C. Intermodal terminal planning by Petri Nets and Data Envelopment Analysis. Control. Eng. Pract. 2017, 69, 9–22.
  33. Cavone, G.; Dotoli, M.; Epicoco, N.; Seatzu, C. A decision making procedure for robust train rescheduling based on mixed inte-ger linear programming and Data Envelopment Analysis. Appl. Math. Model. 2017, 52, 255–273.
  34. Obadić, A.; Aristovnik, A. Relative Efficiency of Higher Education in Croatia and Slovenia: An International Comparison. Amfiteatru Econ. J. 2011, 13, 362–376.
  35. Cvetkoska, V. Data Envelopment Analysis Approach and Its Application in Information and Communication Technologies. In Proceedings of the International Conference on Information and Communication Technologies for Sustainable Agriproduction and Environment (HAICTA 2011), Skiathos, Greece, 8–11 September 2011.
  36. Cvetkoska, V.; Fotova Čiković, K. Efficiency Analysis of Macedonian and Croatian Banking Sectors with DEA. Econ. Bus. Dev. 2021, 2, 1–19.
  37. Amirteimoori, A.; Zadmirzaei, M.; Hassanzadeh, F. Developing a new integrated artificial immune system and fuzzy non-discretionary DEA approach. Soft Comput. 2021, 25, 8109–8127.
  38. Jorda, P.; Cascajo, R.; Monzón, A. Analysis of the Technical Efficiency of Urban Bus Services in Spain Based on SBM Models. ISRN Civ. Eng. 2012, 2012, 984758.
  39. Škrinjarić, T. Evaluation of environmentally conscious tourism industry: Case of Croatian counties. Tour. Int. Interdiscip. J. 2018, 66, 254–268.
  40. Fotova Čiković, K.; Lozić, J.; Milković, M. Applications of data envelopment analysis (DEA) in empirical studies regarding the Croatian tourism. Tour. Int. Interdiscip. J. 2022, accepted.
  41. Cooper, W.W.; Seiford, L.M.; Tone, K. Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References; Springer: New York, NY, USA, 2006.
  42. Hodžić, S.; Jurlina Alibegović, D. The efficiency of regional government expenditure in smart tourist destination: The case of Croatia. Tour. South East Eur. 2019, 5, 307–318.
  43. Giustiniani, A.; Ross, K. Bank competition and efficiency in the FYR Macedonia. South-East. Eur. J. Econ. 2008, 2, 145–167.
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