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A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation

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Abstract

The evaluation of sustainable suppliers is one of the most complex tasks in sustainable supply chain management (SSCM). Classical data envelopment analysis (DEA) and dynamic DEA (DDEA) models are heavily dependent on historical data and do not forecast future efficiencies of decision-making units (DMUs). The primary objective of this paper is to present a new predictive paradigm for ranking sustainable suppliers in SSCM. The proposed model combines goal programming and DDEA in an integrated and seamless paradigm to determine the future efficiencies of DMUs (suppliers). It also shifts the decision maker’s role from monitoring the past to planning the future. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

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Notes

  1. Some of the names and data presented in this study are changed to protect the anonymity of the company.

References

  1. Ageron B, Gunasekaran A, Spalanzani A (2012) Sustainable supply management: an empirical study. Int J Prod Econ 140(1):168–182

    Article  Google Scholar 

  2. Amindoust A, Ahmed S, Saghafinia A, Bahreininejad A (2012) Sustainable supplier selection: a ranking model based on fuzzy inference system. Appl Soft Comput 12(6):1668–1677

    Article  Google Scholar 

  3. Andersen P, Petersen NC (1993) A procedure for ranking efficient units in data envelopment analysis. Manag Sci 39(10):1261–1264

    Article  MATH  Google Scholar 

  4. Azadi M, Farzipoor Saen R (2012) Developing a new chance-constrained DEA model for suppliers selection in the presence of undesirable outputs. Int J Oper Res 13(11):44–66

    Article  MATH  Google Scholar 

  5. Azadi M, Farzipoor Saen R, Tavana M (2012) Supplier selection using chance constrained data envelopment analysis with nondiscretionary factors and stochastic data. Int J Ind Syst Eng 13(2):167–196

    Google Scholar 

  6. Bai C, Sarkis J (2010) Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Prod Econ 124(1):252–264

    Article  Google Scholar 

  7. Beamon BM (2005) Environmental and sustainability ethics in supply chain management. Sci Eng Ethics 11(2):221–234

    Article  Google Scholar 

  8. Carter CR, Jennings MM (2002) Logistics social responsibility: an integrative framework. J Bus Logist 23(1):145–180

    Article  Google Scholar 

  9. Charnes A, Cooper WW, Ferguson RO (1955) Optimal estimation of executive compensation by linear programming. Manag Sci 1(2):138–151

    Article  MathSciNet  MATH  Google Scholar 

  10. Charnes A, Cooper WW (1961) Management models and industrial applications of linear programming. Wiley, New York

    MATH  Google Scholar 

  11. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Article  MathSciNet  MATH  Google Scholar 

  12. Cooper WW, Seiford LM, Tone K (2007) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, 2nd edn. Springer, New York

    MATH  Google Scholar 

  13. Dyllick T, Hockerts K (2002) Beyond the business case for corporate sustainability. Bus Strateg Environ 11(2):130–141

    Article  Google Scholar 

  14. Färe R, Grosskopf S (1996) Productivity and intermediate products: a frontier approach. Econ Lett 50(1):65–70

    Article  MATH  Google Scholar 

  15. Färe R, Grosskopf S, Lovell K, Pasurka C (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71(1):90–98

    Article  Google Scholar 

  16. Färe F, Grosskopf S, Tyteca D (1996) An activity analysis model of the environmental performance of firms—application to fossil-fuel-fired electric utilities. Ecol Econ 18(2):161–175

    Article  Google Scholar 

  17. Farzipoor Saen R (2008) Using super-efficiency analysis for ranking suppliers in the presence of volume discount offers. Int J Phys Distrib Logist Manag 38(8):637–651

    Article  Google Scholar 

  18. Farzipoor Saen R (2009) A decision model for ranking suppliers in the presence of cardinal and ordinal data, weight restrictions, and nondiscretionary factors. Ann Oper Res 172(1):177–192

    Article  MATH  Google Scholar 

  19. Farzipoor Saen R (2010) Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data. Int J Adv Manuf Technol 51(9):1243–1250

    Article  Google Scholar 

  20. Gass SI (1986) A process for determining priorities and weights for large scale linear goal programmes. J Oper Res Soc 37(8):779–785

    Article  MATH  Google Scholar 

  21. Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod 47:345–354

    Article  Google Scholar 

  22. Hatami-Marbini A, Saati S, Tavana M (2010) An ideal-seeking fuzzy data envelopment analysis framework. Appl Soft Comput 10(4):1062–1070

    Article  Google Scholar 

  23. Hsu CW, Hu AH (2007) Green supply chain management in the electronic industry. Int J Environ Sci Technol 5(2):205–216

    Article  Google Scholar 

  24. Jahanshahloo GR, Hosseinzadeh Lotfi F, Khanmohammadi M, Kazemimanesh M, Rezaie V (2010) Ranking of units by positive ideal DMU with common weights. Expert Syst Appl 37(12):7483–7488

    Article  Google Scholar 

  25. Jahanshahloo GR, Hosseinzadeh Lotfi F, Shoja N, Tohidi G, Razavyan S (2005) Undesirable inputs and outputs in DEA models. Appl Math Comput 169(2):917–925

    MathSciNet  MATH  Google Scholar 

  26. Kahraman C, Cebeci U, Ulukan Z (2003) Multi-criteria supplier selection using fuzzy AHP. Logist Inf Manag Syst 16(6):382–394

    Article  Google Scholar 

  27. Korhonen PJ, Luptacik M (2004) Eco-efficiency analysis of power plants: an extension of data envelopment analysis. Eur J Oper Res 154(2):437–446

    Article  MATH  Google Scholar 

  28. Kumar S (2006) Environmentally sensitive productivity growth: a global analysis using Malmquist–Luenberger index. Ecol Econ 56(2):280–293

    Article  Google Scholar 

  29. Kumar A, Jain V, Kumar SA (2014) A comprehensive environment friendly approach for supplier selection. Omega 42(1):109–123

    Article  MathSciNet  Google Scholar 

  30. Li L, Li M, Wu C (2013) Production efficiency evaluation of energy companies based on the improved super-efficiency data envelopment analysis considering undesirable outputs. Math Comput Model 58(5–6):1057–1067

    Article  Google Scholar 

  31. Liang L, Wu D, Hua Z (2004) MES-DEA modeling for analyzing anti-industrial pollution efficiency and its application in Anhui province of China. Int J Glob Energy Issues 22(2–4):88–98

    Article  Google Scholar 

  32. Liao CN, Kao HP (2011) An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Syst Appl 38(9):10803–10811

    Article  Google Scholar 

  33. Likert R (1932) A technique for the measurement of attitudes. Arch Psychol 22(140):1–55

    Google Scholar 

  34. Muralidharan C, Anantharaman N, Deshmukh SG (2002) A multi-criteria group decision-making model for supplier rating. J Supply Chain Manag 38(4):22–33

    Article  Google Scholar 

  35. Murphy PR, Poist RF (2003) Green perspectives and practices: a comparative logistics study. Supply Chain Manag Int J 8(2):122–131

    Article  Google Scholar 

  36. Noorizadeh A, Mahdiloo M, Farzipoor Saen R (2011) Supplier selection in the presence of dual-role factors, nondiscretionary inputs, and weight restrictions. Int J Product Qual Manag 8(2):134–152

    Article  Google Scholar 

  37. Önüt S, Kara SS, Işik E (2009) Long term supplier selection using a combined fuzzy MCDM approach: a case study for a telecommunication company. Expert Syst Appl 36(2):3887–3895

    Article  Google Scholar 

  38. Pittman R (1983) Multilateral productivity comparisons with undesirable outputs. Econ J 93(372):883–891

    Article  Google Scholar 

  39. Ratan SRA, Sekhari A, Rahman M (2010) Sustainable supply chain management: state-of-the-art. In: International conference on software, knowledge, information management and applications, Paro, Bhutan

  40. Romero C (2004) A general structure of achievement function for a goal programming model. Eur J Oper Res 153(3):675–686

    Article  MathSciNet  MATH  Google Scholar 

  41. Sarkis J, Talluri S (2004) Evaluating and selecting e-commerce software and communication systems for a supply chain. Eur J Oper Res 159(2):318–329

    Article  MATH  Google Scholar 

  42. Seiford LM, Zhu J (2002) Modeling undesirable factors in efficiency evaluation. Eur J Oper Res 142(1):16–20

    Article  MATH  Google Scholar 

  43. Sengupta JK (1995) Dynamics of data envelopment analysis: theory of systems efficiency. Kluwer Academic Publishers, Dordrecht

    Book  MATH  Google Scholar 

  44. Sexton TR, Silkman RH, Hogan AJ (1986) Data envelopment analysis: critique and extensions, vol 32, no 1. In: Silkman RH (ed) Measuring efficiency: an assessment of data envelopment analysis. Jossey-Bass, San Francisco, pp 73–105

    Google Scholar 

  45. Stewart TJ (2010) Goal directed benchmarking for organizational efficiency. Omega 38(6):534–539

    Article  Google Scholar 

  46. Sueyoshi T, Goto M (2010) Should the US clean air act include CO2 emission control? Examination by data envelopment analysis. Energy Policy 38(10):5902–5911

    Article  Google Scholar 

  47. Sueyoshi T, Goto M (2011) Methodological comparison between two unified (operational and environmental) efficiency measurements for environmental assessment. Eur J Oper Res 210(3):684–693

    Article  MATH  Google Scholar 

  48. Sun J, Wu J, Guo D (2013) Performance ranking of units considering ideal and anti-ideal DMU with common weights. Appl Math Model 37(9):6301–6310

    Article  MathSciNet  Google Scholar 

  49. Tone K, Tsutsui M (2010) Dynamic DEA: a slacks-based measure approach. Omega 38(3–4):145–156

    Article  MATH  Google Scholar 

  50. Tone K, Tsutsui M (2014) Dynamic DEA with network structure: a slacks-based measure approach. Omega 42(1):124–131

    Article  MATH  Google Scholar 

  51. Tseng M-L, Chiang JH, Lan LW (2009) Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral. Comput Ind Eng 57(1):330–340

    Article  Google Scholar 

  52. Wang Y-M, Chin K-S, Luo Y (2011) Cross-efficiency evaluation based on ideal and anti-ideal decision making units. Expert Syst Appl 38(8):10312–10319

    Article  Google Scholar 

  53. Wang Y-M, Luo Y (2006) DEA efficiency assessment using ideal and anti-ideal decision making units. Appl Math Comput 173(2):902–915

    MathSciNet  MATH  Google Scholar 

  54. Wang NS, Yi RH, Wang W (2008) Evaluating the performances of decision-making units based on interval efficiencies. J Comput Appl Math 216(2):328–343

    Article  MathSciNet  MATH  Google Scholar 

  55. Weber A, Current J, Desai A (2000) An optimization approach to determining the number of vendors to employ. Int J Supply Chain Manag 5(2):90–98

    Article  Google Scholar 

  56. Wen L, Xu L, Wang R (2013) Sustainable supplier evaluation based on intuitionistic fuzzy sets group decision methods. J Inf Comput Sci 10(10):3209–3220

    Article  Google Scholar 

  57. White BJ (1996) Developing products and their rhetoric from a single hierarchical model. Proc Annu Conf Soc Tech Commun 43:223–224

    Google Scholar 

  58. Wierzbicki AP (1999) Reference point approaches. In: Gal T, Stewart TJ, Hanne T (eds) Multicriteria decision making: advances in MCDM models, algorithms, theory, and applications, Chapter 9. Kluwer Academic Publishers, Boston

    Google Scholar 

  59. Yahya S, Kingsman B (1999) Vendor rating for an entrepreneur development programme: a case study using the analytic hierarchy process method. J Oper Res Soc 50(9):916–930

    Article  MATH  Google Scholar 

  60. Yaisawarng S, Klein J (1994) The effects of sulfur dioxide controls on productivity change in the US electric power industry. Rev Econ Stat 76(3):447–460

    Article  Google Scholar 

  61. Yousefi S, Shabanpour H, Farzipoor Saen R, Faramarzi GR (2014) Making an ideal decision making unit using virtual network data envelopment analysis approach. Int J Bus Perform Manag 15(4):316–328

    Article  Google Scholar 

  62. Zouggari A, Benyoucef L (2012) Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem. Eng Appl Artif Intell 25(3):507–519

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the anonymous reviewers and the editor for their insightful comments and suggestions.

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Authors and Affiliations

  1. Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA, 19141, USA

    Madjid Tavana

  2. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, 33098, Paderborn, Germany

    Madjid Tavana

  3. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran

    Hadi Shabanpour

  4. Department of Industrial Management, Faculty of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran

    Saeed Yousefi & Reza Farzipoor Saen

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  1. Madjid Tavana
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  2. Hadi Shabanpour
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  4. Reza Farzipoor Saen
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Correspondence to Madjid Tavana.

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Tavana, M., Shabanpour, H., Yousefi, S. et al. A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation. Neural Comput & Applic 28, 3683–3696 (2017). https://doi.org/10.1007/s00521-016-2274-z

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  • DOI: https://doi.org/10.1007/s00521-016-2274-z

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