IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v309y2022i1d10.1007_s10479-021-04271-1.html
   My bibliography  Save this article

An ex-ante DEA method for representing contextual uncertainties and stakeholder risk preferences

Author

Listed:
  • Taylan G. Topcu

    (The George Washington University)

  • Konstantinos Triantis

    (Virginia Polytechnic Institute and State University)

Abstract

Data envelopment analysis (DEA) is an analytical method for measuring, benchmarking, and improving efficiency performance. While DEA has traditionally been a deterministic ex-post approach, when operational data are not available, it can be positioned to represent an ex-ante perspective, if it is feasible to simulate production behaviors. However, there are two limitations of ex-ante DEA. First, is the representation of uncertainties associated with the contextual conditions and their impact on production. Second, is the inability to incorporate risk preferences that relate to the organizations’ attitude towards production uncertainty. This paper addresses these two gaps through a method that integrates Monte-Carlo simulations, the Subjective Expected Utility Theory, and ex-ante DEA. Our method allows to explicitly model and incorporate the uncertainties associated with the contextual variables and the production behavior, while facilitating the representation of risk preferences. We demonstrate our method through a case study that is framed around a hypothetical community evaluating Micro-Grid (MG) design alternatives. We utilize the National Renewable Energy Lab data to model the stakeholder energy demand and environmental uncertainties; and use the engineering specifications for the MG technology to populate the production possibility set. We then investigate the distribution of efficiency scores by using various stakeholder risk attitudes and find that DEA rankings are insensitive to risk preferences. The proposed method and the documented risk insensitivity are significant contributions as they allow to reposition ex-ante DEA as a nonparametric alternative to multi-criteria decision-making, which can be generalized across different application areas, including uncommon DEA applications such as engineering.

Suggested Citation

  • Taylan G. Topcu & Konstantinos Triantis, 2022. "An ex-ante DEA method for representing contextual uncertainties and stakeholder risk preferences," Annals of Operations Research, Springer, vol. 309(1), pages 395-423, February.
    • Handle: RePEc:spr:annopr:v:309:y:2022:i:1:d:10.1007_s10479-021-04271-1
    DOI: 10.1007/s10479-021-04271-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04271-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-021-04271-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Taylan G. Topcu & Konstantinos Triantis & Richard Malak & Paul Collopy, 2020. "An interdisciplinary strategy to advance systems engineering theory: The case of abstraction and elaboration," Systems Engineering, John Wiley & Sons, vol. 23(6), pages 673-683, November.
    2. Ruggiero, John, 1998. "Non-discretionary inputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 111(3), pages 461-469, December.
    3. Robert G. Chambers & Atakelty Hailu & John Quiggin, 2011. "Event‐specific data envelopment models and efficiency analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(1), pages 90-106, January.
    4. Toloo, Mehdi, 2009. "On classifying inputs and outputs in DEA: A revised model," European Journal of Operational Research, Elsevier, vol. 198(1), pages 358-360, October.
    5. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    6. Céline Nauges & Christopher J. O'Donnell & John Quiggin, 2011. "Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 38(4), pages 449-467, October.
    7. Yang, Hongliang & Pollitt, Michael, 2009. "Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1095-1105, September.
    8. Sriram Shankar & John Quiggin, 2013. "Production under uncertainty: a simulation study," Journal of Productivity Analysis, Springer, vol. 39(3), pages 207-215, June.
    9. C. J. O'Donnell & W. E. Griffiths, 2006. "Estimating State-Contingent Production Frontiers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 249-266.
    10. Noah Kaufman, 2014. "Why is risk aversion unaccounted for in environmental policy evaluations?," Climatic Change, Springer, vol. 125(2), pages 127-135, July.
    11. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    12. Wang, Ying-Ming & Chin, Kwai-Sang, 2011. "The use of OWA operator weights for cross-efficiency aggregation," Omega, Elsevier, vol. 39(5), pages 493-503, October.
    13. Zoe Szajnfarber & Paul T. Grogan & Jitesh H. Panchal & Erica L. Gralla, 2020. "A call for consensus on the use of representative model worlds in systems engineering and design," Systems Engineering, John Wiley & Sons, vol. 23(4), pages 436-442, July.
    14. Greenberg, Michael & Mantell, Nancy & Lahr, Michael & Felder, Frank & Zimmerman, Rae, 2007. "Short and intermediate economic impacts of a terrorist-initiated loss of electric power: Case study of New Jersey," Energy Policy, Elsevier, vol. 35(1), pages 722-733, January.
    15. Moschini, Giancarlo & Hennessy, David A., 2001. "Uncertainty, risk aversion, and risk management for agricultural producers," Handbook of Agricultural Economics, in: B. L. Gardner & G. C. Rausser (ed.), Handbook of Agricultural Economics, edition 1, volume 1, chapter 2, pages 88-153, Elsevier.
    16. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    17. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    18. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    19. Serra, Teresa & Oude Lansink, Alfons, 2014. "Measuring the impacts of production risk on technical efficiency: A state-contingent conditional order-m approach," European Journal of Operational Research, Elsevier, vol. 239(1), pages 237-242.
    20. Herrera-Restrepo, Oscar & Triantis, Konstantinos & Trainor, Joseph & Murray-Tuite, Pamela & Edara, Praveen, 2016. "A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach," Omega, Elsevier, vol. 60(C), pages 45-59.
    21. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    22. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    23. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521785235, January.
    24. Christopher O’Donnell & Robert Chambers & John Quiggin, 2010. "Efficiency analysis in the presence of uncertainty," Journal of Productivity Analysis, Springer, vol. 33(1), pages 1-17, February.
    25. Aldy, Joseph Edgar & Leiserowitz, Anthony A & Kotchen, Matthew J, 2012. "Willingness to Pay and Political Support for a U.S. National Clean Energy Standard," Scholarly Articles 8832942, Harvard Kennedy School of Government.
    26. Sueyoshi, Toshiyuki, 2000. "Stochastic DEA for restructure strategy: an application to a Japanese petroleum company," Omega, Elsevier, vol. 28(4), pages 385-398, August.
    27. Paradi, Joseph C. & Sherman, H. David, 2014. "Seeking Greater Practitioner and Managerial Use of DEA for Benchmarking," Data Envelopment Analysis Journal, now publishers, vol. 1(1), pages 29-55, October.
    28. O. B. Olesen & N. C. Petersen, 1995. "Chance Constrained Efficiency Evaluation," Management Science, INFORMS, vol. 41(3), pages 442-457, March.
    29. Topcu, Taylan G. & Triantis, Konstantinos & Roets, Bart, 2019. "Estimation of the workload boundary in socio-technical infrastructure management systems: The case of Belgian railroads," European Journal of Operational Research, Elsevier, vol. 278(1), pages 314-329.
    30. Kumbhakar, Subal C & Ghosh, Soumendra & McGuckin, J Thomas, 1991. "A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 279-286, July.
    31. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    32. Battese, George E. & Corra, Greg S., 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 1-11, December.
    33. Fare, Rolf, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    34. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    35. Zhao, Y. & Triantis, K. & Murray-Tuite, P. & Edara, P., 2011. "Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1140-1159.
    36. Cook, Wade D. & Zhu, Joe, 2007. "Classifying inputs and outputs in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 180(2), pages 692-699, July.
    37. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    38. Soshinskaya, Mariya & Crijns-Graus, Wina H.J. & Guerrero, Josep M. & Vasquez, Juan C., 2014. "Microgrids: Experiences, barriers and success factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 659-672.
    39. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    40. William Cooper & Zhimin Huang & Vedran Lelas & Susan Li & Ole Olesen, 1998. "Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA," Journal of Productivity Analysis, Springer, vol. 9(1), pages 53-79, January.
    41. Joseph E. Aldy & Matthew J. Kotchen & Anthony A. Leiserowitz, 2012. "Willingness to pay and political support for a US national clean energy standard," Nature Climate Change, Nature, vol. 2(8), pages 596-599, August.
    42. Shin, Jungwoo & Woo, JongRoul & Huh, Sung-Yoon & Lee, Jongsu & Jeong, Gicheol, 2014. "Analyzing public preferences and increasing acceptability for the Renewable Portfolio Standard in Korea," Energy Economics, Elsevier, vol. 42(C), pages 17-26.
    43. Wei, Guiwu & Chen, Jian & Wang, Jiamin, 2014. "Stochastic efficiency analysis with a reliability consideration," Omega, Elsevier, vol. 48(C), pages 1-9.
    44. Ustun, Taha Selim & Ozansoy, Cagil & Zayegh, Aladin, 2011. "Recent developments in microgrids and example cases around the world—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 4030-4041.
    45. Färe, Rolf & Grosskopf, Shawna, 2013. "DEA, directional distance functions and positive, affine data transformation," Omega, Elsevier, vol. 41(1), pages 28-30.
    46. Just, Richard E. & Pope, Rulon D., 2001. "The agricultural producer: Theory and statistical measurement," Handbook of Agricultural Economics, in: B. L. Gardner & G. C. Rausser (ed.), Handbook of Agricultural Economics, edition 1, volume 1, chapter 12, pages 629-741, Elsevier.
    47. A. Charnes & W. W. Cooper, 1963. "Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints," Operations Research, INFORMS, vol. 11(1), pages 18-39, February.
    48. Christopher W. Karvetski & James H. Lambert, 2012. "Evaluating deep uncertainties in strategic priority‐setting with an application to facility energy investments," Systems Engineering, John Wiley & Sons, vol. 15(4), pages 483-493, December.
    49. Awerbuch, Shimon, 2000. "Investing in photovoltaics: risk, accounting and the value of new technology," Energy Policy, Elsevier, vol. 28(14), pages 1023-1035, November.
    50. Yin, Pengzhen & Sun, Jiasen & Chu, Junfei & Liang, Liang, 2016. "Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspectiveAuthor-Name: Wu, Jie," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1047-1062.
    51. Mozumder, Pallab & Vásquez, William F. & Marathe, Achla, 2011. "Consumers' preference for renewable energy in the southwest USA," Energy Economics, Elsevier, vol. 33(6), pages 1119-1126.
    52. George E. Battese & Greg S. Corra, 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 169-179, December.
    53. Sadjadi, S.J. & Omrani, H., 2008. "Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies," Energy Policy, Elsevier, vol. 36(11), pages 4247-4254, November.
    54. Keeney, Ralph L., 1996. "Value-focused thinking: Identifying decision opportunities and creating alternatives," European Journal of Operational Research, Elsevier, vol. 92(3), pages 537-549, August.
    55. Gao, Jianwei & Li, Ming & Liu, Huihui, 2015. "Generalized ordered weighted utility averaging-hyperbolic absolute risk aversion operators and their applications to group decision-making," European Journal of Operational Research, Elsevier, vol. 243(1), pages 258-270.
    56. Hamilton, Michelle C. & Lambert, James H. & Connelly, Elizabeth B. & Barker, Kash, 2016. "Resilience analytics with disruption of preferences and lifecycle cost analysis for energy microgrids," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 11-21.
    57. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    58. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    59. Gao, Jianwei & Li, Ming & Liu, Huihui, 2015. "Generalized ordered weighted utility proportional averaging-hyperbolic absolute risk aversion operators and their applications to group decision-making," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 114-132.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Macedo, Pedro & Scotto, Manuel, 2014. "Cross-entropy estimation in technical efficiency analysis," Journal of Mathematical Economics, Elsevier, vol. 54(C), pages 124-130.
    2. Olesen, Ole B. & Petersen, Niels Christian, 2016. "Stochastic Data Envelopment Analysis—A review," European Journal of Operational Research, Elsevier, vol. 251(1), pages 2-21.
    3. Arbelo-Pérez, Marta & Arbelo, Antonio & Pérez-Gómez, Pilar, 2017. "Impact of quality on estimations of hotel efficiency," Tourism Management, Elsevier, vol. 61(C), pages 200-208.
    4. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    5. Martín Rossi, 2015. "The Econometrics Approach to the Measurement of Efficiency: A Survey," Working Papers 117, Universidad de San Andres, Departamento de Economia, revised Feb 2015.
    6. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.
    7. repec:aer:wpaper:324 is not listed on IDEAS
    8. Quintano, Claudio & Mazzocchi, Paolo & Rocca, Antonella, 2021. "Evaluation of the eco-efficiency of territorial districts with seaport economic activities," Utilities Policy, Elsevier, vol. 71(C).
    9. Lovell, Knox, 2001. "Future Research Opportunities in Efficiency and Productivity Analysis," Efficiency Series Papers 2001/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    10. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    11. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.
    12. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    13. Cordero Ferrera, Jose Manuel & Alonso Morán, Edurne & Nuño Solís, Roberto & Orueta, Juan F. & Souto Arce, Regina, 2013. "Efficiency assessment of primary care providers: A conditional nonparametric approach," MPRA Paper 51926, University Library of Munich, Germany.
    14. Sun, Qinghe & Chen, Li & Meng, Qiang, 2022. "Evaluating port efficiency dynamics: A risk-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 333-347.
    15. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    16. García-Alonso, Carlos R. & Salvador-Carulla, Luis & Fernández-Rodríguez, Vicente, 2015. "Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areasAuthor-Name: Torres-Jiménez, Mercedes," European Journal of Operational Research, Elsevier, vol. 242(2), pages 525-535.
    17. C. Lovell & Shawna Grosskopf & Eduardo Ley & Jesús Pastor & Diego Prior & Philippe Eeckaut, 1994. "Linear programming approaches to the measurement and analysis of productive efficiency," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 2(2), pages 175-248, December.
    18. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    19. Francisca Pacheco & Rafael Sánchez & Mauricio G. Villena, 2021. "Estimating local government efficiency using a panel data parametric approach: the case of Chilean municipalities," Applied Economics, Taylor & Francis Journals, vol. 53(3), pages 292-314, January.
    20. Yin, Pengzhen & Sun, Jiasen & Chu, Junfei & Liang, Liang, 2016. "Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspectiveAuthor-Name: Wu, Jie," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1047-1062.
    21. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:309:y:2022:i:1:d:10.1007_s10479-021-04271-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.