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Assessing Occupational Health and Safety Performances of Companies Using Multi-Criteria Methods

Year 2021, Volume: 9 Issue: 1, 337 - 359, 31.01.2021

Merve Erol Babek Erdebilli

https://doi.org/10.29130/dubited.801867
Cited By: 3

Abstract

Occupational health and safety and management systems were founded to overcome the threats of main catastrophes and health issues in many industries. With this in mind, as occupational health and safety and management systems evolved, there has been a necessity to assess the performance of these systems. It is argued that the preliminary phase of such an assessment may be to identify the right indicators and employ them using  expert data. To this end, the goals of this research are as to gather and scan a list of performance indicators in the context of OHS, to calculate the importance ratings of the scanned indicators and prioritize them using Entropy Method based on quantitative data, to evaluate the occupational health and safety performance of a company comparatively using TOPSIS, and finally to discuss the implications of our research. The findings of this study suggest that the number of accidents due to the unsafe activities is the most important criterion with 0,2971 using Entropy. Moreover, based on TOPSIS results the findings suggest that the case company has improved its occupational health and safety performance, 2018 being the first ranked year. The methodology developed in this study assists decision makers in making comparative analysis between the yearly performances of companies, making rational investment decisions, considering various indicators, and making more scientific decisions rather than intuitive.

Keywords

Occupational health and safety systems Indicators Entropy TOPSIS Performance

References

  • [1] K. Hakkinen, “Safety Management: From Basic Understanding Towards Excellence,” Integrated Occupational Safety and Health Management, Switzerland: Springer, 2015, ss. 7-15.
  • [2] M. Erol, “Occupational health and work safety systems ın compliance with Industry 4.0: research direction,” International Journal of E-business and E-government Studies, c. 11, s. 2, ss. 119-133, 019.
  • [3] J. Harrison ve Dawson. L, “Occupational health: meeting the challenges of the next 20 years,” Safety and Health at Work, c. 7, ss. 143-149, 2016.
  • [4] G. Serin ve M.T. Çuhadar, “İş Güvenliği ve Sağlığı Yönetim Sistemi,” SDÜ Teknik Bilimler Dergisi, c. 5, s. 2, ss. 44-59, 2015.
  • [5] P. Drucker, Out of Crisis. The MIT Press, New York, 2000.
  • [6] C. Redinger, “Occupational Health and Safety Management Systems,” Occupational Health and Safety Management Systems, CRC Press Taylor and Francis Group, 2019, ss. 79-93.
  • [7] T. Akpınar ve E. Öğütoğulları, “OHSAS 18001 iş sağlığı ve güvenliği yönetim sistemi,” Balkan and Near Eastern Journal of Social Sciences, c. 2, s. 3, ss. 97-104, 2016.
  • [8] A. R. A. Hamid, B. Singh, W.Z.W Yusof, ve A.K.T. Yang, “Integration of safety, health and environment and quality management system in construction: a review,” Jurnal Kejuretaan Awam, c. 16, s. 1, ss. 24-27, 2004.
  • [9] E. Kwegyir-Afful, R. Addo-Tenkorang, J. Kantola, “Effects of Occupational Health and Safety Assessment Series (OHSAS) Standard: A Study on Core Competencies Building and Organizational Learning,” Advances in Human Factors, Business Management and Leadership, Stockholm, Switzerland: Springer, 2018, ss. 395-405.
  • [10] M.G. Erdoğan, ISO 45001, (8 Aralık, 2015). [Çevrimiçi]. Erişim: https://web.itu.edu.tr/erdoganmahm/documents/iso_45001.pdf Erişim Tarihi: 3 Ekim, 2019.
  • [11] E. Bottani, L. Monica ve G. Vignali, “Safety management systems: Performance differences between adopters and non-adopters,” Safety Science, c. 47, ss. 155-162, 2009.
  • [12] D. Podgorski, “Measuring operational performance of OSH management system: a demonstration of AHP-based selection of leading key performance indicators,” Safety Science, c. 73, ss. 146-166, 2015.
  • [13] S. Sultana, B.S. Andersen ve S. Haugen “Identifying safety indicators for safety performance measurement using a system engineering approach,” Process Safety and Environmental Protection, c. 128, ss. 107-120, 2019.
  • [14] I. Mohammadfam, M. Kamalinia, M. Momeni, R. Golmohammadi, Y. Hamidi ve A. Soltanian, “Developing an integrated decision making approach to assess and promote the effectiveness of occupational health and safety management systems,” Journal of Cleaner Production, c. 127, ss. 119-133, 2016.
  • [15] A. J. Haas ve P. Yorion, “Exploring the state of health and safety management system performance measurement in mining organizations,” Safety Science, c. 83, s. March, ss. 48-58, 2016.
  • [16] I. Mohammadfam, M. Kamalinia, M. Momeni, R. Golmohammadi, Y. Hamidi ve A. Soltanian, “Evaluation of the quality of occupational health and safety management systems based on key performance ındicators in certified organizations,” Safety and Health at Work, c. 8, ss. 156-161, June, 2017.
  • [17] L. Yan, L. Zhang, W. Liang, W. Li ve M. Dub, “Key factors identification and dynamic fuzzy assessment of health, safety and environment performance in petroleum enterprises,” Safety Science, c. 94, ss. 77-84, 2017.
  • [18] U. H. Inan, S. Gül, ve H. Yılmaz, “A multiple attribute decision model to compare the firms’ occupational health and safety management perspectives,” Safety Sciences. c. 91, ss. 221-231, 2017.
  • [19] E. Engüren, ve T. Koç, S, “İş sağlığı ve güvenliği uygulamaları performans değerlendirme ölçeği: geçerlik ve güvenirlik çalışması,” Sosyal Güvenlik Dergisi, c. 5, no. 2, ss. 124-144, 2015.
  • [20] A. Ediz, A. Yıldızbaşı ve E. Baytemur, “İş Sağliği ve güvenliği yönetim sistemi performans göstergelerinin Ahp ile değerlendirilmesi,” International Journal of Social Science, c. 62, ss. 275-294, December, 2017.
  • [21] B. Render ve R. Stair, Quantitative Analysis for Management, 4th ed., Allyn and Bacon, Maascahussets, USA.
  • [22] J. Mc Glade, “Foreword: Finding the Right Indicators for Policymaking,” Sustainabililty Indicators: A Scientific Assessment, Washington DC, USA: Scope, 2007, ss. 1-24.
  • [23] British Standards Institution (BSI), Occupational health and safety management systems requirements, (2018). [Çevrimiçi]. Erişim: https://www.bsigroup.com/en-GB/Occupational-Health-and-Safety-ISO-45001/ Erişim Tarihi: 18 Ekim, 2019.
  • [24] B. D. Rouyendegh, “Developing AHP and Intuitionistic Fuzzy TOPSIS Methodology,” Technical Gazette, c. 21 no. 6, ss. 1313-1319, 2014.
  • [25] B. D. Rouyendegh, U. Baç ve T. E. Erkan, “Sector Selection for ERP Implementation to Achieve Most Impact on Supply Chain Performance by using AHP-TOPSIS Hybrid Method,” Technical Gazette, c. 21 s. 5, ss. 933-937, 2014.
  • [26] B. D. Rouyendegh ve T. E. Erkan, “Selection the Best Supplier Using AHP Method,” African Journal of Business Management, c. 6, s. 4, ss. 1454-1462, 2012.
  • [27] B. D. Rouyendegh ve T. E. Erkan,”ERP System Selection by AHP Method: Case Study from TURKEY,” International Journal of Business and Management Studies, c. 3, ss. 39-48, 2011.
  • [28] L. G. Vargas, “An Overview of the Analytic Hierarchy Process and Its Applications,” European Journal of Operational Research, c. 48, ss. 2–8, 1990.
  • [29] A. Sopadang, B. Cho, and M. Leonard, “Development Of The Hybrid Weight Assessment System for Multiple Quality Attributes,” Quality Engineering, c.15 ss. 75-89, 2002.
  • [30] C. E. Shannon and W. Weaver, The Mathematical Theory of Communication, The University of Illinois Press, Urbana, 1947.
  • [31] P. Nijkamp, “Stochastic Quantitative and Qualitative Multi-criteria Analysis for environmental Design,” Papers of the Regional Science Association, c. 39, ss. 175-199, 1977.
  • [32] M. Zeleny, Linear Mu1ti-objective Programming, Springer-Verlag, Berlin: Heidelberg, New York, 1974.
  • [33] H. Tang, Y. Shi ve P. Dong, “Public blockchain evaluation using entropy and TOPSIS,” Expert Systems With Applications, c. 117, ss. 204-210, 2019.
  • [34] Y. Cui, P. Feng, J. Jin, ve L. Liu, “Water Resources Carrying Capacity Evaluation and Diagnosis Based on Set Pair Analysis and Improved the Entropy Weight Method,” Entropy, c. 20, ss. 1-20, 2018.
  • [35] T. Maruyama, T. Kawachi ve V. P. Singh, “Entropy-based assessment and clustering of potential water resources availability,” Journal of Hydrology, c. 309, ss. 104–113, 2005.
  • [36] C. Hwang ve K. Yoon, “Multiple Attribute Decision Making: Methods and Applications,” Springer Verlag, c. 186, 1981.
  • [37] Q. Xu, Z. G. Hu ve Q. Liu, “Multi-objective decision analysis of diversion standards based on entropy,” China Rural Water and Hydropower, c. 8, ss. 45-47, 2004.
  • [38] A. Alimoradi, R. M. Yussuf ve N. Zulkifli, “A hybrid model for remanufacturing facility location problem in a closed-loop supply chain,” International Journal of Sustainable Engineering, c. 4, ss. 16–23, 2011.
  • [39] M. Amiri, M. Zandieh, R. Soltani ve B. Vahdani, “A hybrid multi-criteria decision-making model for firms competence evaluation,” Expert Systems with Applications, c. 36, ss. 12314–12322, 2009.
  • [40] M. P. Amiri, “Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods,” Expert Systems with Applications, c. 37, ss. 6218–6224, 2010.
  • [41] Ö. Uzun, O. Eski ve C. Araz, “Determining the parameters of dual-card kanban system: An integrated multi criteria and artificial neural network methodology,” International Journal of Advanced Manufacturing Technology, c. 38, ss. 965–977, 2008.
  • [42] A. Awasthi, S. S. Chauhan ve S. K. Goyal, “A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty,” Mathematical and Computer Modeling, c. 53, ss. 98–109, 2011.
  • [43] Y. Deng ve F. T. S. Chan, “A new fuzzy dempster MCDM method and its application in supplier selection,” Expert Systems with Applications, c. 38, ss. 9854–9861, 2011.
  • [44] T. C. Chu ve Y. C. Lin, “A fuzzy TOPSIS method for robot selection,” International Journal of Advanced Manufacturing Technology, c. 21, ss. 284–290, 2003.
  • [45] C. H. Yeh, “The Selection of Multi-attribute Decision Making Methods for Scholarship Student Selection,’’ International Journal of Selection and Assessment, c. 11, ss. 289-296, 2003.
  • [46] İhracat Genel Müdürlüğü Maden, Metal ve Orman Ürünleri Dairesi, Demir Çelik Sektör Raporu (2018) [Çevrimiçi] Erişim: https://ticaret.gov.tr/data/5b87000813b8761450e18d7b/Demir_Celik_Demir_Celikten_Esya.pdf Erişim Tarihi: 11 Ağustos, 2019.
  • [47] V. Belton ve T. J. Stewart, Multi-Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers, USA, 2002.

Firmaların İş Sağlığı ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi

Year 2021, Volume: 9 Issue: 1, 337 - 359, 31.01.2021

Merve Erol Babek Erdebilli

https://doi.org/10.29130/dubited.801867
Cited By: 3

Abstract

İş sağlığı ve güvenliği yönetim sistemleri, iş süreçlerinde oluşan kaza ve hastalıkların sayısının azaltılması amacıyla kullanılan temel yaklaşımlardan biridir. Sürekli gelişme bağlamında inşa edilen bu sistemlerin hedeflenen sonuçları doğurması, sistem kapsamında geliştirilen performans ölçüm araçlarının oluşturulması ile mümkündür. Kapsamlı bir performans ölçüm aracı, birden fazla göstergeyi içermeli, sayısal analiz imkanı sunmalı ve karşılaştırma yapabilmeye elverişli olmalıdır. Bu araştırmada, etkin bir iş sağlığı ve güvenliği performans ölçümü aracının geliştirilmesi amacıyla; performans gösterge havuzu oluşturulmuş ve uygun göstergeler taranarak belirlenmiş, Entropy ve TOPSIS tabanlı çok kriterli bir karar verme modeli geliştirilmiş, Türkiye’de demir-çelik sektöründe faaliyet gösteren bir firmadan elde edilen veriler kullanılarak Entropy tabanlı bir gösterge ağırlığı belirleme çalışması yapılmış, firmanın iş sağlığı ve güvenliği performansı, yıllar itibariyle TOPSIS kullanılarak karşılaştırmalı olarak hesaplanmış ve araştırmanın sonuçları, zayıf yanları ve yeni araştırma imkanları tartışılmıştır. Araştırmanın bulgularına göre, “güvenli olmayan faaliyetler nedeniyle meydana gelen kazaların sayısı”, 0,2971 ağırlığıyla en önemli göstergedir. Buna ek olarak, TOPSIS tabanlı göreceli yakınlık değerleri, firmanın iş sağlığı ve güvenliği performans sıralamasının 2018, 2017, 2016, 2015 ve 2014 şeklinde gerçekleştiğini ortaya koymaktadır. Bu bulgulara göre, firmanın iş sağlığı ve güvenliği performansında yıllar itibari ile bir gelişme sağlandığı sonucuna ulaşılmıştır. Bu araştırmada geliştirilen çok kriterli karar verme modelinin karar vericilerin karar alma süreçlerine belirtilen katkıları sunabileceği öngörülmektedir: iş sağlığı ve güvenliği performanslarının karşılaştırmalı analizlerinin yapılması, iş sağlığı ve güvenliği alanındaki farklı göstergelerin dikkate alınması, bu analizlerin yıllık raporlarda kullanılmasıyla paydaşların firma hakkında yapacakları yatırım kararlarının sağlıklı bir biçimde alınabilmesi ve sezgilere dönük karar verme sürecinin bilimsel olarak desteklenmesi.

Keywords

İş Sağlığı ve Güvenliği Göstergeler Entropy Topsis Performans

References

  • [1] K. Hakkinen, “Safety Management: From Basic Understanding Towards Excellence,” Integrated Occupational Safety and Health Management, Switzerland: Springer, 2015, ss. 7-15.
  • [2] M. Erol, “Occupational health and work safety systems ın compliance with Industry 4.0: research direction,” International Journal of E-business and E-government Studies, c. 11, s. 2, ss. 119-133, 019.
  • [3] J. Harrison ve Dawson. L, “Occupational health: meeting the challenges of the next 20 years,” Safety and Health at Work, c. 7, ss. 143-149, 2016.
  • [4] G. Serin ve M.T. Çuhadar, “İş Güvenliği ve Sağlığı Yönetim Sistemi,” SDÜ Teknik Bilimler Dergisi, c. 5, s. 2, ss. 44-59, 2015.
  • [5] P. Drucker, Out of Crisis. The MIT Press, New York, 2000.
  • [6] C. Redinger, “Occupational Health and Safety Management Systems,” Occupational Health and Safety Management Systems, CRC Press Taylor and Francis Group, 2019, ss. 79-93.
  • [7] T. Akpınar ve E. Öğütoğulları, “OHSAS 18001 iş sağlığı ve güvenliği yönetim sistemi,” Balkan and Near Eastern Journal of Social Sciences, c. 2, s. 3, ss. 97-104, 2016.
  • [8] A. R. A. Hamid, B. Singh, W.Z.W Yusof, ve A.K.T. Yang, “Integration of safety, health and environment and quality management system in construction: a review,” Jurnal Kejuretaan Awam, c. 16, s. 1, ss. 24-27, 2004.
  • [9] E. Kwegyir-Afful, R. Addo-Tenkorang, J. Kantola, “Effects of Occupational Health and Safety Assessment Series (OHSAS) Standard: A Study on Core Competencies Building and Organizational Learning,” Advances in Human Factors, Business Management and Leadership, Stockholm, Switzerland: Springer, 2018, ss. 395-405.
  • [10] M.G. Erdoğan, ISO 45001, (8 Aralık, 2015). [Çevrimiçi]. Erişim: https://web.itu.edu.tr/erdoganmahm/documents/iso_45001.pdf Erişim Tarihi: 3 Ekim, 2019.
  • [11] E. Bottani, L. Monica ve G. Vignali, “Safety management systems: Performance differences between adopters and non-adopters,” Safety Science, c. 47, ss. 155-162, 2009.
  • [12] D. Podgorski, “Measuring operational performance of OSH management system: a demonstration of AHP-based selection of leading key performance indicators,” Safety Science, c. 73, ss. 146-166, 2015.
  • [13] S. Sultana, B.S. Andersen ve S. Haugen “Identifying safety indicators for safety performance measurement using a system engineering approach,” Process Safety and Environmental Protection, c. 128, ss. 107-120, 2019.
  • [14] I. Mohammadfam, M. Kamalinia, M. Momeni, R. Golmohammadi, Y. Hamidi ve A. Soltanian, “Developing an integrated decision making approach to assess and promote the effectiveness of occupational health and safety management systems,” Journal of Cleaner Production, c. 127, ss. 119-133, 2016.
  • [15] A. J. Haas ve P. Yorion, “Exploring the state of health and safety management system performance measurement in mining organizations,” Safety Science, c. 83, s. March, ss. 48-58, 2016.
  • [16] I. Mohammadfam, M. Kamalinia, M. Momeni, R. Golmohammadi, Y. Hamidi ve A. Soltanian, “Evaluation of the quality of occupational health and safety management systems based on key performance ındicators in certified organizations,” Safety and Health at Work, c. 8, ss. 156-161, June, 2017.
  • [17] L. Yan, L. Zhang, W. Liang, W. Li ve M. Dub, “Key factors identification and dynamic fuzzy assessment of health, safety and environment performance in petroleum enterprises,” Safety Science, c. 94, ss. 77-84, 2017.
  • [18] U. H. Inan, S. Gül, ve H. Yılmaz, “A multiple attribute decision model to compare the firms’ occupational health and safety management perspectives,” Safety Sciences. c. 91, ss. 221-231, 2017.
  • [19] E. Engüren, ve T. Koç, S, “İş sağlığı ve güvenliği uygulamaları performans değerlendirme ölçeği: geçerlik ve güvenirlik çalışması,” Sosyal Güvenlik Dergisi, c. 5, no. 2, ss. 124-144, 2015.
  • [20] A. Ediz, A. Yıldızbaşı ve E. Baytemur, “İş Sağliği ve güvenliği yönetim sistemi performans göstergelerinin Ahp ile değerlendirilmesi,” International Journal of Social Science, c. 62, ss. 275-294, December, 2017.
  • [21] B. Render ve R. Stair, Quantitative Analysis for Management, 4th ed., Allyn and Bacon, Maascahussets, USA.
  • [22] J. Mc Glade, “Foreword: Finding the Right Indicators for Policymaking,” Sustainabililty Indicators: A Scientific Assessment, Washington DC, USA: Scope, 2007, ss. 1-24.
  • [23] British Standards Institution (BSI), Occupational health and safety management systems requirements, (2018). [Çevrimiçi]. Erişim: https://www.bsigroup.com/en-GB/Occupational-Health-and-Safety-ISO-45001/ Erişim Tarihi: 18 Ekim, 2019.
  • [24] B. D. Rouyendegh, “Developing AHP and Intuitionistic Fuzzy TOPSIS Methodology,” Technical Gazette, c. 21 no. 6, ss. 1313-1319, 2014.
  • [25] B. D. Rouyendegh, U. Baç ve T. E. Erkan, “Sector Selection for ERP Implementation to Achieve Most Impact on Supply Chain Performance by using AHP-TOPSIS Hybrid Method,” Technical Gazette, c. 21 s. 5, ss. 933-937, 2014.
  • [26] B. D. Rouyendegh ve T. E. Erkan, “Selection the Best Supplier Using AHP Method,” African Journal of Business Management, c. 6, s. 4, ss. 1454-1462, 2012.
  • [27] B. D. Rouyendegh ve T. E. Erkan,”ERP System Selection by AHP Method: Case Study from TURKEY,” International Journal of Business and Management Studies, c. 3, ss. 39-48, 2011.
  • [28] L. G. Vargas, “An Overview of the Analytic Hierarchy Process and Its Applications,” European Journal of Operational Research, c. 48, ss. 2–8, 1990.
  • [29] A. Sopadang, B. Cho, and M. Leonard, “Development Of The Hybrid Weight Assessment System for Multiple Quality Attributes,” Quality Engineering, c.15 ss. 75-89, 2002.
  • [30] C. E. Shannon and W. Weaver, The Mathematical Theory of Communication, The University of Illinois Press, Urbana, 1947.
  • [31] P. Nijkamp, “Stochastic Quantitative and Qualitative Multi-criteria Analysis for environmental Design,” Papers of the Regional Science Association, c. 39, ss. 175-199, 1977.
  • [32] M. Zeleny, Linear Mu1ti-objective Programming, Springer-Verlag, Berlin: Heidelberg, New York, 1974.
  • [33] H. Tang, Y. Shi ve P. Dong, “Public blockchain evaluation using entropy and TOPSIS,” Expert Systems With Applications, c. 117, ss. 204-210, 2019.
  • [34] Y. Cui, P. Feng, J. Jin, ve L. Liu, “Water Resources Carrying Capacity Evaluation and Diagnosis Based on Set Pair Analysis and Improved the Entropy Weight Method,” Entropy, c. 20, ss. 1-20, 2018.
  • [35] T. Maruyama, T. Kawachi ve V. P. Singh, “Entropy-based assessment and clustering of potential water resources availability,” Journal of Hydrology, c. 309, ss. 104–113, 2005.
  • [36] C. Hwang ve K. Yoon, “Multiple Attribute Decision Making: Methods and Applications,” Springer Verlag, c. 186, 1981.
  • [37] Q. Xu, Z. G. Hu ve Q. Liu, “Multi-objective decision analysis of diversion standards based on entropy,” China Rural Water and Hydropower, c. 8, ss. 45-47, 2004.
  • [38] A. Alimoradi, R. M. Yussuf ve N. Zulkifli, “A hybrid model for remanufacturing facility location problem in a closed-loop supply chain,” International Journal of Sustainable Engineering, c. 4, ss. 16–23, 2011.
  • [39] M. Amiri, M. Zandieh, R. Soltani ve B. Vahdani, “A hybrid multi-criteria decision-making model for firms competence evaluation,” Expert Systems with Applications, c. 36, ss. 12314–12322, 2009.
  • [40] M. P. Amiri, “Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods,” Expert Systems with Applications, c. 37, ss. 6218–6224, 2010.
  • [41] Ö. Uzun, O. Eski ve C. Araz, “Determining the parameters of dual-card kanban system: An integrated multi criteria and artificial neural network methodology,” International Journal of Advanced Manufacturing Technology, c. 38, ss. 965–977, 2008.
  • [42] A. Awasthi, S. S. Chauhan ve S. K. Goyal, “A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty,” Mathematical and Computer Modeling, c. 53, ss. 98–109, 2011.
  • [43] Y. Deng ve F. T. S. Chan, “A new fuzzy dempster MCDM method and its application in supplier selection,” Expert Systems with Applications, c. 38, ss. 9854–9861, 2011.
  • [44] T. C. Chu ve Y. C. Lin, “A fuzzy TOPSIS method for robot selection,” International Journal of Advanced Manufacturing Technology, c. 21, ss. 284–290, 2003.
  • [45] C. H. Yeh, “The Selection of Multi-attribute Decision Making Methods for Scholarship Student Selection,’’ International Journal of Selection and Assessment, c. 11, ss. 289-296, 2003.
  • [46] İhracat Genel Müdürlüğü Maden, Metal ve Orman Ürünleri Dairesi, Demir Çelik Sektör Raporu (2018) [Çevrimiçi] Erişim: https://ticaret.gov.tr/data/5b87000813b8761450e18d7b/Demir_Celik_Demir_Celikten_Esya.pdf Erişim Tarihi: 11 Ağustos, 2019.
  • [47] V. Belton ve T. J. Stewart, Multi-Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers, USA, 2002.
There are 47 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Merve Erol 0000-0003-0487-3338

Babek Erdebilli 0000-0001-8860-3903

Publication Date January 31, 2021
Published in Issue Year 2021 Volume: 9 Issue: 1

Cite

APA Erol, M., & Erdebilli, B. (2021). Firmaların İş Sağlığı ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 9(1), 337-359. https://doi.org/10.29130/dubited.801867
AMA Erol M, Erdebilli B. Firmaların İş Sağlığı ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi. DUBİTED. January 2021;9(1):337-359. doi:10.29130/dubited.801867
Chicago Erol, Merve, and Babek Erdebilli. “Firmaların İş Sağlığı Ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 9, no. 1 (January 2021): 337-59. https://doi.org/10.29130/dubited.801867.
EndNote Erol M, Erdebilli B (January 1, 2021) Firmaların İş Sağlığı ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 1 337–359.
IEEE M. Erol and B. Erdebilli, “Firmaların İş Sağlığı ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi”, DUBİTED, vol. 9, no. 1, pp. 337–359, 2021, doi: 10.29130/dubited.801867.
ISNAD Erol, Merve - Erdebilli, Babek. “Firmaların İş Sağlığı Ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9/1 (January 2021), 337-359. https://doi.org/10.29130/dubited.801867.
JAMA Erol M, Erdebilli B. Firmaların İş Sağlığı ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi. DUBİTED. 2021;9:337–359.
MLA Erol, Merve and Babek Erdebilli. “Firmaların İş Sağlığı Ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, vol. 9, no. 1, 2021, pp. 337-59, doi:10.29130/dubited.801867.
Vancouver Erol M, Erdebilli B. Firmaların İş Sağlığı ve Güvenliği Performansının Çok Kriterli Karar Verme Yöntemleri Yardımıyla Ölçülmesi. DUBİTED. 2021;9(1):337-59.

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