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  • توسعه مدل پویای مدیریت دانش صنعت نفت ایران با استفاده از رویکرد پویایی شناسی سیستم (SD)

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آدرس مقاله


کد مقاله : IMJ-2005-1389 (R2) بازدید : 81 صفحه: 67 - 87

نوع مقاله: پژوهشی

توسعه مدل پویای مدیریت دانش صنعت نفت ایران با استفاده از رویکرد پویایی شناسی سیستم (SD)

محورهای موضوعی : مدیریت صنعتی

reza moeini jazani 1 (Oil Ministry)
Ahmad Reza Kasrai 2 (Assistant)
AHMAD ASLIZADEH 3 (Faculty member of Islamic Azad University, Imam Memorial Branch)
Tsohrabi sohrabi 4 (Department of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.)

تاریخ دریافت : 1399/02/28 تاریخ پذیرش : 1399/05/11 تاریخ انتشار : 1399/06/01

کلید واژه: نظام مدیریت دانش, پویایی شناسی سیستم (SD), صنعت نفت ایران,

چکیده مقاله :

این پژوهش به مدل سازی و شبیه سازی نظام مدیریت دانش صنعت نفت با استفاده از رویکرد پویایی شناسی سیستم (SD) به منظور سیاست گذاری توسعه مدیریت دانش صنعت نفت به جهت از بین رفتن شکاف بین سرمایه دانش موجود و دانش کاربردی پرداخته است. ابتدا با مشارکت برنامه ریزان درگیر در وزارت نفت، زیرساخت های سازمانی موفقیت استقرار نظام مدیریت دانش شناسایی و بر مبنای فرآیند مدیریت دانش نمودار علّی ترسیم شد. مدل جریان بر اساس داده های صنعت نفت طراحی و در افق بیست ساله شبیه سازی گردید. پس از اعتباربخشی مدل مبتنی بر نتایج تحلیل حساسیت رفتار متغیر شکاف دانش، سیاست های توسعه نظام مدیریت دانش صنعت نفت استخراج و مورد تجزیه و تحلیل قرار گرفت. مطابق با یافته های پژوهش سه سیاست توسعه خلق دانش، توسعه زیرساخت های مستندسازی دانش و توسعه تسهیم دانش در صنعت نفت و نیز منتخب ترکیبی از سیاست ها شناسایی و شبیه سازی شدند. در نتیجه شبیه سازی منتخب ترکیبی از سیاست ها شامل توسعه همکاری با قطب های علمی کشور، طراحی سیستم های ثبت تجربه، فرهنگ سازی تسهیم دانش، تیم سازی دانش محور، رعایت حقوق مالکیت معنوی دانش، تجاری سازی دانش صنعت نفت به عنوان بهترین راه کار، جهت سیاستگذاری و برنامه ریزی موفقیت استقرار نظام مدیریت دانش صنعت نفت ایران ارائه گردید.

چکیده انگلیسی:

This study simulates and simulates the knowledge industry management system of the oil industry using the system dynamics approach (SD) in order to policy the development of knowledge management of the oil industry to eliminate the gap between existing knowledge capital and applied knowledge. First, with the participation of the planners involved in the Ministry of Oil, the organizational infrastructure of the success of the knowledge management system was identified and a causal chart was drawn based on the knowledge management process. The flow model was designed based on oil industry data and simulated on a 20-year horizon. After validating the model based on the results of the analysis of the sensitivity behavior of the variable knowledge gap, the development policies of the knowledge management system of the oil industry were extracted and analyzed. According to the research findings, three policies for the development of knowledge creation, the development of knowledge documentation infrastructure and the development of knowledge sharing in the oil industry, as well as the selection of a combination of policies were identified and simulated. As a result, the selected simulation is a combination of policies including the development of cooperation with the country's scientific poles, design of experience registration systems, culture-building of knowledge sharing, knowledge-based team building, respect for intellectual property rights, commercialization of oil industry knowledge as the best solution. , For the policy and planning of success, the establishment of the knowledge management system of Iran's oil industry was presented.

منابع و مأخذ:


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_||_

  1. Al-Nahyan, M., Sohal, A., Hawas, Y. and Fildes, B. (2018). Communication, coordination, decision-making and knowledge-sharing: a case study in construction management. Journal of Knowledge Management, 23(9), 1764-1781.
    2.
  2. Amiri, Y., Karami, H. (2014). The role of organizational culture in improving the level of knowledge management: Dynamic analysis with a dynamic systems approach. The first international conference on accounting and management. Kish Azar. 2014.
    3.
  3. Asish, Mathew, O. Lewlyn L. R.Rodrigues, Alapati Vittaleswar, (2012), Human Factors & Knowledge Management: A System Dynamics Based Analysis, Journal of Knowledge Management Practice, 13(2), June 2012.
    4.
  4. Chang, H. & Huang, W., Application of a quantification SWOT analytical method. Mathematical and computer modelling, 2006. 43(1),158-169.
    5.
  5. Charmchi, H. (2014). The concept of supply chain in the industry and its benefits. Industry Quarterly; 23 (3).
    6.
  6. Du, L. (2017). Acquiring competitive advantage in industry through supply chain integration: a case study of Yue Yuen Industrial Holdings Ltd. Information Management, 20, 65-90
    7.
  7. Ekhtiarzadeh, A. (2010). Iran Code as a communication platform for SCM and ERP. The first conference on organizational resource planning systems.
    8.
  8. Finney, S, & Corbett, M. (2017). ERP implementation: a compilation and analysis of critical success factors. Bus Process Management Journal, 13(3), 329–347.
    9.
  9. Gholipour et al., (2009). Investigating the Impact of Servant Leadership on Organizational Trust and Empowerment in Governmental Organizations, Journal of Public Management, 2(1).
    10.
  10. Ganeshan, R. & Harrison, T.P. (2015). An Introduction to Supply Chain Management: Department of Management Sciences and Information systems, 303 Beam Business Building, Penn State University, University park.
    11.
  11. Gargeya, V. B. and Brady, C. (2015). Success and Failure Factor of Adopting SAP in ERP System Implementation. Business Process Management Journal, 11(5), 501-516.
    12.
  12. Hatami, R., Safaei, B., Gholami, S. (2015). Investigating knowledge management models and presenting a conceptual model of knowledge management as a competitive advantage using the system dynamics approach. Second International Conference on Management, Economics and Humanities. Dubai. 2015
    13.
  13. Hosseini, S.H., Ahmadi, M., Piruzfar, Sh. Imani, M. (2017). A system model for identifying the success factors of establishing a knowledge management system in aviation organizations and providing improvement strategies (Case study: one of the aviation organizations). Fourteenth International Conference on Industrial Engineering.
    14.
  14. Khadivar, A. Hosseinzadeh Sh., Javaheri, S. (2014). Provide a model for selecting knowledge management strategy using systems dynamics approach. Sixth Knowledge Management Conference. March 2014
    15.
  15. Kuo, R. & Lee, G. (2011), Knowledge management system adoption: exploring the effects of empowering leadership, task-technology fit and compatibility. Behaviour & Information Technology, 30(1),113-129.
    16.
  16. Mozafari1, P., Bagheri, F., Taghavi M. (2016) , Evaluation and System Dynamics Modeling of KnowledgeManagement Flows in Performance of Organizations: A Case Studyin PADYAV Consulting Engineering Company, ulletin de la Société des Sciences de Liège, 85, 1219 – 1228.
    17.
  17. Mujibian, F., Fartoukzadeh, H., Rajabi N., Mustafaei, Kh. (2015). Investigating the reasons for the departure of knowledge employees from knowledge-based companies with a system dynamics approach. Iranian Management Quarterly, 10(37), 23-49.
    18.
  18. Noor al-Nisa. Jafari, M., Hosseini Azabadi, J., Dehghani Srizadi, M., (2011). Analyzing the effects of knowledge management on organizational excellence using dynamic systems. The first systemic approach conference.
    19.
  19. Park, Sang-hyun, Seung-jun Yeon, Dong-ho Kim, Sang-wook Kim , (2003), Building A System Dynamics Modelfor Strategic Knowledge Management in IT Company
    20.
  20. Report of the Deputy Minister of Human Resources of the Ministry of Oil, (2017). Statistical report on the situation of oil industry manpower.
    21.
  21. Sohrabi, Babak; Mahjoub, Hamed and Raisi Vanani, Iman (2014). Designing a fuzzy inference system to prioritize and select the most appropriate resource planning system of the organization. Quarterly Journal of Industrial Management, 3(10), 101-128.
    22.
  22. Sterman. (2000). Business Dynamics: Systems Thinking and Modeling for a complex word. Boston: Permission of the McGraw-Hill companies.
    23.
  23. Wang, M. & Yang, T. (2016) investigating the success of knowledge management: An empirical study of small-and medium-sized enterprises. Asia Pacific Management Review, 21(2), 79-91.
    24.
  24. Wu, W., (2012) Segmenting critical factors for successful knowledge management implementation using the fuzzy DEMATEL method. Applied Soft Computing, 12(1)., 527-535.
    25.
  25. Zaima, S., Nizamettin, B., Mehves, T., Halil Zaimd, Y., (2013). System dynamics modeling of a knowledge management process: A case study in Turkish Airlines. Published by Elsevier Ltd.Selection and peer-review under responsibility of the International Strategic Management Conference.
    26.

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