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Enhancing Organizational Efficiency through Data Envelopment Analysis

Year 2025, , 19 - 24, 15.01.2025
https://doi.org/10.34248/bsengineering.1535117

Abstract

In today's competitive business landscape, organizations strive to maximize efficiency and productivity to maintain their competitive edge. Data Envelopment Analysis (DEA) has emerged as a powerful tool for evaluating the performance and efficiency of decision-making units across various industries. This paper provides a comprehensive review of DEA and its applications in enhancing organizational efficiency. The first section of the paper introduces the concept of DEA and its underlying principles, highlighting its ability to evaluate the relative efficiency of decision-making units by comparing their input-output relationships. Various DEA models, including CCR and BCC models, are discussed in detail, along with their mathematical formulations. The subsequent sections delve into the practical implementation of DEA, outlining the key stages involved in conducting an efficiency analysis. These stages include unit selection, input-output identification, data collection, efficiency measurement, and result interpretation. Special emphasis is placed on the importance of data quality and reliability in ensuring the accuracy of DEA results. For example, in a recent analysis, the efficiency score of the units ranged from 0.65 to 1.0, indicating a significant variation in performance. In some cases, units with scores below 0.8 were flagged for further investigation to identify areas for improvement. Furthermore, the paper explores the benefits of adopting DEA as a decision support tool within organizations. From identifying inefficiencies to guiding resource allocation and strategic planning, DEA offers a range of advantages for decision-makers. The paper also highlights the role of DEA in promoting a culture of continuous improvement and benchmarking against industry standards. In conclusion, this paper underscores the significance of DEA in enhancing organizational efficiency and offers insights into its practical implementation. By leveraging DEA as a strategic management tool, organizations can optimize their operations, drive performance improvements, and maintain a competitive advantage in today's dynamic business environment.

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

References

  • Banker RD. 1992. Estimation of returns to scale using data envelopment analysis. Eur J Oper Res, 62(1): 74-84.
  • Banker RD, Charnes A, Cooper WW. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci, 30(9): 1078-1092.
  • Boussofiane A, Dyson RG, Rhodes E. 1991. Applied data envelopment analysis. Eur J Oper Res, 52(6): 1-15.
  • Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units. Eur J Oper Res, 2(6): 429-444.
  • Cooper WW, Seiford LM, Tone K. 2006. Data envelopment analysis: a comprehensive text with models, applications, and software. Springer, New York, USA, pp: 143.
  • Çolak ÖF, Altan Ş. 2002. Toplam etkinlik ölçümü: Türkiye'deki özel ve kamu bankaları için bir uygulama. İktisat İşletme Finans, 169: 45-55.
  • Emrouznejad A, Yang GL. 2018. A survey and analysis of the first 40 years of scholarly research on data envelopment analysis. Socio Econ Plann Sci, 61: 4-8.
  • Esenbet M, Erkin MO, Erdoğan FK. 2001. Veri zarflama analizi ile dokuma, giyim eşyası ve deri sektöründe faaliyet gösteren firmaların etkinliğinin karşılaştırılması. URL: http://www.analiz.com/egitim/gaziOOl.html (accessed date: September 24, 2001).
  • Hadi A, Gohary A. 2015. Data envelopment analysis in the health sector: A review. J Health Manag, 17(2): 93-103.
  • Karasoy H. 2000. Veri zarflama analizi. Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye, ss: 68.
  • Narman M, Stoker B. 1991. Data envelopment analysis. John Wiley & Sons, New York, USA, pp: 245.
  • Seiford LM, Thrall RM. 1990. Data envelopment analysis: A review and extension. Oper Res, 38(3): 429-444.
  • Simons R. 1995. Data envelopment analysis aids efficiency. MP Action, 6(2): 1-3.
  • Tarım A. 2001. Veri Zarflama Analizi: Matematiksel Programlama Tabanlı Göreli Etkinlik Ölçümü Yaklaşımı. Sayıştay Yayın İşleri Müdürlüğü, Ankara, Türkiye, ss: 54.
  • Taticchi P, Tonelli F, Pasqualino R. 2013. Performance measurement and management: A literature review. J Ind Eng Manag, 6(1): 4-20.
  • Tiryaki F. 2000. Yeri zarflama analizi ile hisse senedi seçimi. In: İstatistik Araştırma Sempozyumu 2000 Bildiriler Kitabı, October 01-03, Ankara, Türkiye, pp: 353-370.
  • Yolalan R. 1993. İşletmelerde göreli etkinlik ölçümü. Milli Prodüktivite Merkezi Yayınları, Ankara, Türkiye, ss: 43.

Enhancing Organizational Efficiency through Data Envelopment Analysis

Year 2025, , 19 - 24, 15.01.2025
https://doi.org/10.34248/bsengineering.1535117

Abstract

In today's competitive business landscape, organizations strive to maximize efficiency and productivity to maintain their competitive edge. Data Envelopment Analysis (DEA) has emerged as a powerful tool for evaluating the performance and efficiency of decision-making units across various industries. This paper provides a comprehensive review of DEA and its applications in enhancing organizational efficiency. The first section of the paper introduces the concept of DEA and its underlying principles, highlighting its ability to evaluate the relative efficiency of decision-making units by comparing their input-output relationships. Various DEA models, including CCR and BCC models, are discussed in detail, along with their mathematical formulations. The subsequent sections delve into the practical implementation of DEA, outlining the key stages involved in conducting an efficiency analysis. These stages include unit selection, input-output identification, data collection, efficiency measurement, and result interpretation. Special emphasis is placed on the importance of data quality and reliability in ensuring the accuracy of DEA results. For example, in a recent analysis, the efficiency score of the units ranged from 0.65 to 1.0, indicating a significant variation in performance. In some cases, units with scores below 0.8 were flagged for further investigation to identify areas for improvement. Furthermore, the paper explores the benefits of adopting DEA as a decision support tool within organizations. From identifying inefficiencies to guiding resource allocation and strategic planning, DEA offers a range of advantages for decision-makers. The paper also highlights the role of DEA in promoting a culture of continuous improvement and benchmarking against industry standards. In conclusion, this paper underscores the significance of DEA in enhancing organizational efficiency and offers insights into its practical implementation. By leveraging DEA as a strategic management tool, organizations can optimize their operations, drive performance improvements, and maintain a competitive advantage in today's dynamic business environment.

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

References

  • Banker RD. 1992. Estimation of returns to scale using data envelopment analysis. Eur J Oper Res, 62(1): 74-84.
  • Banker RD, Charnes A, Cooper WW. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci, 30(9): 1078-1092.
  • Boussofiane A, Dyson RG, Rhodes E. 1991. Applied data envelopment analysis. Eur J Oper Res, 52(6): 1-15.
  • Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units. Eur J Oper Res, 2(6): 429-444.
  • Cooper WW, Seiford LM, Tone K. 2006. Data envelopment analysis: a comprehensive text with models, applications, and software. Springer, New York, USA, pp: 143.
  • Çolak ÖF, Altan Ş. 2002. Toplam etkinlik ölçümü: Türkiye'deki özel ve kamu bankaları için bir uygulama. İktisat İşletme Finans, 169: 45-55.
  • Emrouznejad A, Yang GL. 2018. A survey and analysis of the first 40 years of scholarly research on data envelopment analysis. Socio Econ Plann Sci, 61: 4-8.
  • Esenbet M, Erkin MO, Erdoğan FK. 2001. Veri zarflama analizi ile dokuma, giyim eşyası ve deri sektöründe faaliyet gösteren firmaların etkinliğinin karşılaştırılması. URL: http://www.analiz.com/egitim/gaziOOl.html (accessed date: September 24, 2001).
  • Hadi A, Gohary A. 2015. Data envelopment analysis in the health sector: A review. J Health Manag, 17(2): 93-103.
  • Karasoy H. 2000. Veri zarflama analizi. Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye, ss: 68.
  • Narman M, Stoker B. 1991. Data envelopment analysis. John Wiley & Sons, New York, USA, pp: 245.
  • Seiford LM, Thrall RM. 1990. Data envelopment analysis: A review and extension. Oper Res, 38(3): 429-444.
  • Simons R. 1995. Data envelopment analysis aids efficiency. MP Action, 6(2): 1-3.
  • Tarım A. 2001. Veri Zarflama Analizi: Matematiksel Programlama Tabanlı Göreli Etkinlik Ölçümü Yaklaşımı. Sayıştay Yayın İşleri Müdürlüğü, Ankara, Türkiye, ss: 54.
  • Taticchi P, Tonelli F, Pasqualino R. 2013. Performance measurement and management: A literature review. J Ind Eng Manag, 6(1): 4-20.
  • Tiryaki F. 2000. Yeri zarflama analizi ile hisse senedi seçimi. In: İstatistik Araştırma Sempozyumu 2000 Bildiriler Kitabı, October 01-03, Ankara, Türkiye, pp: 353-370.
  • Yolalan R. 1993. İşletmelerde göreli etkinlik ölçümü. Milli Prodüktivite Merkezi Yayınları, Ankara, Türkiye, ss: 43.
There are 17 citations in total.

Details

Primary Language Turkish
Subjects Statistics (Other)
Journal Section Research Articles
Authors

Hatice Dilaver 0000-0002-4484-5297

Kamil Fatih Dilaver 0000-0001-7557-9238

Publication Date January 15, 2025
Submission Date August 18, 2024
Acceptance Date November 17, 2024
Published in Issue Year 2025

Cite

APA Dilaver, H., & Dilaver, K. F. (2025). Enhancing Organizational Efficiency through Data Envelopment Analysis. Black Sea Journal of Engineering and Science, 8(1), 19-24. https://doi.org/10.34248/bsengineering.1535117
AMA Dilaver H, Dilaver KF. Enhancing Organizational Efficiency through Data Envelopment Analysis. BSJ Eng. Sci. January 2025;8(1):19-24. doi:10.34248/bsengineering.1535117
Chicago Dilaver, Hatice, and Kamil Fatih Dilaver. “Enhancing Organizational Efficiency through Data Envelopment Analysis”. Black Sea Journal of Engineering and Science 8, no. 1 (January 2025): 19-24. https://doi.org/10.34248/bsengineering.1535117.
EndNote Dilaver H, Dilaver KF (January 1, 2025) Enhancing Organizational Efficiency through Data Envelopment Analysis. Black Sea Journal of Engineering and Science 8 1 19–24.
IEEE H. Dilaver and K. F. Dilaver, “Enhancing Organizational Efficiency through Data Envelopment Analysis”, BSJ Eng. Sci., vol. 8, no. 1, pp. 19–24, 2025, doi: 10.34248/bsengineering.1535117.
ISNAD Dilaver, Hatice - Dilaver, Kamil Fatih. “Enhancing Organizational Efficiency through Data Envelopment Analysis”. Black Sea Journal of Engineering and Science 8/1 (January 2025), 19-24. https://doi.org/10.34248/bsengineering.1535117.
JAMA Dilaver H, Dilaver KF. Enhancing Organizational Efficiency through Data Envelopment Analysis. BSJ Eng. Sci. 2025;8:19–24.
MLA Dilaver, Hatice and Kamil Fatih Dilaver. “Enhancing Organizational Efficiency through Data Envelopment Analysis”. Black Sea Journal of Engineering and Science, vol. 8, no. 1, 2025, pp. 19-24, doi:10.34248/bsengineering.1535117.
Vancouver Dilaver H, Dilaver KF. Enhancing Organizational Efficiency through Data Envelopment Analysis. BSJ Eng. Sci. 2025;8(1):19-24.

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