IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v234y2014i1p241-252.html
   My bibliography  Save this article

A new multicriteria approach for the analysis of efficiency in the Spanish olive oil sector by modelling decision maker preferences

Author

Listed:
  • Alcaide-López-de-Pablo, David
  • Dios-Palomares, Rafaela
  • Prieto, Ángel M.

Abstract

The efficiency in production is often analysed as technical efficiency using the production frontier function. Efficiency scores are usually based on distance computations to the frontier in an m+s-dimensional space, where m inputs produce s outputs. In addition, efficiency improvements consider the total consumption of each input. However, in many cases, the “consumption” of each input can be divided into input-consumption sections (ICSs), and trade-off among the ICSs is possible. This share framework can be used for computing efficiency. This analysis provides information about both the total optimal consumption of each input, as does data envelopment analysis, and the most efficient allocation of the “consumption” among the ICSs. This paper studies technical efficiency using this approach and applies it to the olive oil sector in Andalusia (Spain). A non-parametrical methodology is presented, and an input-oriented Multi-Criteria Linear Programming model (MLP) is proposed. The analysis is developed at global, input and ICSs levels, defining the extent of satisfaction achieved at all these levels for each company, in accordance with their own preferences. The companies’ preferences are modelled with their utility function and their set of weights. MLP offers more detailed information to assist decision makers than other models previously proposed in the literature. In addition to this application, it is concluded that there is room for improvement in the olive oil sector, particularly in the management of the skilled labour. Additionally, the solutions with two opposite scenarios indicate that the model is suitable for the intended decision making process.

Suggested Citation

  • Alcaide-López-de-Pablo, David & Dios-Palomares, Rafaela & Prieto, Ángel M., 2014. "A new multicriteria approach for the analysis of efficiency in the Spanish olive oil sector by modelling decision maker preferences," European Journal of Operational Research, Elsevier, vol. 234(1), pages 241-252.
  • Handle: RePEc:eee:ejores:v:234:y:2014:i:1:p:241-252
    DOI: 10.1016/j.ejor.2013.09.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713007856
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2013.09.030?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. Dios-Palomares, Rafaela & Martínez-Paz, José M., 2011. "Technical, quality and environmental efficiency of the olive oil industry," Food Policy, Elsevier, vol. 36(4), pages 526-534, August.
    2. Sarrico, C. S. & Dyson, R. G., 2004. "Restricting virtual weights in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 159(1), pages 17-34, November.
    3. Merja Halme & Tarja Joro & Pekka Korhonen & Seppo Salo & Jyrki Wallenius, 1999. "A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis," Management Science, INFORMS, vol. 45(1), pages 103-115, January.
    4. Lidia Angulo-Meza & Marcos Lins, 2002. "Review of Methods for Increasing Discrimination in Data Envelopment Analysis," Annals of Operations Research, Springer, vol. 116(1), pages 225-242, October.
    5. Halme, Merja & Korhonen, Pekka, 2000. "Restricting weights in value efficiency analysis," European Journal of Operational Research, Elsevier, vol. 126(1), pages 175-188, October.
    6. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264.
    7. 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.
    8. Korhonen, Pekka & Tainio, Risto & Wallenius, Jyrki, 2001. "Value efficiency analysis of academic research," European Journal of Operational Research, Elsevier, vol. 130(1), pages 121-132, April.
    9. Lorenzo Castelli & Raffaele Pesenti & Walter Ukovich, 2010. "A classification of DEA models when the internal structure of the Decision Making Units is considered," Annals of Operations Research, Springer, vol. 173(1), pages 207-235, January.
    10. R. Allen & A. Athanassopoulos & R.G. Dyson & E. Thanassoulis, 1997. "Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions," Annals of Operations Research, Springer, vol. 73(0), pages 13-34, October.
    11. William W. Cooper & Lawrence M. Seiford & Joe Zhu, 2011. "Data Envelopment Analysis: History, Models, and Interpretations," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 1-39, Springer.
    12. N Adler & B Golany, 2002. "Including principal component weights to improve discrimination in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 985-991, September.
    13. Doyle, J & Green, R, 1993. "Data envelopment analysis and multiple criteria decision making," Omega, Elsevier, vol. 21(6), pages 713-715, November.
    14. G R Jahanshahloo & M Zohrehbandian & A Alinezhad & S Abbasian Naghneh & H Abbasian & R Kiani Mavi, 2011. "Finding common weights based on the DM's preference information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(10), pages 1796-1800, October.
    15. William W. Cooper & José L. Ruiz & Inmaculada Sirvent, 2011. "Choices and Uses of DEA Weights," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 93-126, Springer.
    16. Sarkis, Joseph, 2000. "A comparative analysis of DEA as a discrete alternative multiple criteria decision tool," European Journal of Operational Research, Elsevier, vol. 123(3), pages 543-557, June.
    17. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, November.
    18. Jyrki Wallenius & James S. Dyer & Peter C. Fishburn & Ralph E. Steuer & Stanley Zionts & Kalyanmoy Deb, 2008. "Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead," Management Science, INFORMS, vol. 54(7), pages 1336-1349, July.
    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. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    2. Panagiotis Ravanos & Giannis Karagiannis, 2022. "On VEA, Production Trade-offs and Weight Restrictions," Discussion Paper Series 2022_04, Department of Economics, University of Macedonia, revised Jun 2022.
    3. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
    4. Panagiotis Ravanos & Giannis Karagiannis, 2021. "Using VEA to assess effectiveness in the development of human capabilities," Economic Change and Restructuring, Springer, vol. 54(1), pages 75-99, February.
    5. Santos, Sérgio P. & Belton, Valerie & Howick, Susan & Pilkington, Martin, 2018. "Measuring organisational performance using a mix of OR methods," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 18-30.
    6. Suzuki, Soushi & Nijkamp, Peter & Rietveld, Piet & Pels, Eric, 2010. "A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1104-1115, December.
    7. Pereira, Miguel Alves & Camanho, Ana Santos & Figueira, José Rui & Marques, Rui Cunha, 2021. "Incorporating preference information in a range directional composite indicator: The case of Portuguese public hospitals," European Journal of Operational Research, Elsevier, vol. 294(2), pages 633-650.
    8. Ahti Salo & Antti Punkka, 2011. "Ranking Intervals and Dominance Relations for Ratio-Based Efficiency Analysis," Management Science, INFORMS, vol. 57(1), pages 200-214, January.
    9. Roets, Bart & Verschelde, Marijn & Christiaens, Johan, 2018. "Multi-output efficiency and operational safety: An analysis of railway traffic control centre performance," European Journal of Operational Research, Elsevier, vol. 271(1), pages 224-237.
    10. Ioannis Gkouvitsos & Ioannis Giannikos, 2022. "Using a MACBETH based multicriteria approach for virtual weight restrictions in each stage of a DEA multi-stage ranking process," Operational Research, Springer, vol. 22(3), pages 1787-1811, July.
    11. Giannis Karagiannis & Panagiotis Ravanos, 2023. "On Value Efficiency Analysis and Cone-Ratio Data Envelopment Analysis models," Discussion Paper Series 2023_03, Department of Economics, University of Macedonia, revised Mar 2023.
    12. Martin Eling, 2006. "Performance measurement of hedge funds using data envelopment analysis," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(4), pages 442-471, December.
    13. Peter Nijkamp & Soushi Suzuki, 2009. "A Generalized Goals-achievement Model in Data Envelopment Analysis: an Application to Efficiency Improvement in Local Government Finance in Japan," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(3), pages 249-274.
    14. Hamdan, Amer & (Jamie) Rogers, K.J., 2008. "Evaluating the efficiency of 3PL logistics operations," International Journal of Production Economics, Elsevier, vol. 113(1), pages 235-244, May.
    15. Tavana, Madjid & Ebrahimnejad, Ali & Santos-Arteaga, Francisco J. & Mansourzadeh, Seyed Mehdi & Matin, Reza Kazemi, 2018. "A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 70-89.
    16. Petridis, Konstantinos & Malesios, Chrisovalantis & Arabatzis, Garyfallos & Thanassoulis, Emmanuel, 2013. "Efficiency analysis of forestry journals: Suggestions for improving journals’ quality," Journal of Informetrics, Elsevier, vol. 7(2), pages 505-521.
    17. Harald Dyckhoff, 2018. "Multi-criteria production theory: foundation of non-financial and sustainability performance evaluation," Journal of Business Economics, Springer, vol. 88(7), pages 851-882, September.
    18. Jaime Bonet-Morón & Jhorland Ayala-García, 2016. "La brecha fiscal territorial en Colombia," Documentos de trabajo sobre Economía Regional y Urbana 235, Banco de la Republica de Colombia.
    19. Yang, Jian-Bo & Wong, Brandon Y.H. & Xu, Dong-Ling & Stewart, Theodor J., 2009. "Integrating DEA-oriented performance assessment and target setting using interactive MOLP methods," European Journal of Operational Research, Elsevier, vol. 195(1), pages 205-222, May.
    20. Gerami, Javad & Mozaffari, Mohammad Reza & Wanke, Peter F. & Correa, Henrique L., 2022. "Improving information reliability of non-radial value efficiency analysis: An additive slacks based measure approach," European Journal of Operational Research, Elsevier, vol. 298(3), pages 967-978.

    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:eee:ejores:v:234:y:2014:i:1:p:241-252. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.