ارایه مدل مفهومی جهت مدیریت بافرها در مدیریت زنجیره بحرانی پروژه های نفت و گاز
محورهای موضوعی : مدیریت صنعتیBehnam Feizabadi 1 , Mahmoud Alborzi 2 , Abbas Tolouee 3 , Ahmad Makoyi 4
1 - Ph.D. Candidate in Production and Operations Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor of Industrial Management, Faculty of Economics and Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Assistant Professor of Industrial Management, Faculty of Economics and Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Assistant Professor of Industrial Management, Faculty of Economics and Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: risk management, مدیریت ریسک, مدیریت زنجیره بحرانی, محاسبه بافر, مدلسازی ساختاری تفسیری, Critical Chain Project Management, Buffer Sizing, Interpretive Structural Modelling,
چکیده مقاله :
هدف اصلی این مقاله تعیین عوامل تاثیرگذار در انتخاب اندازه بافرها با توجه به ریسکهای پروژه است که همواره این عوامل به دلیل آنکه حسی و شهودی هستند، دقیق محاسبه نمیشوند و همچنین به دلیل کمبود منابع و زمان موجب تاخیر در پروژه شده و یا بیشتر از نیاز واقعی فعالیتهای پروژه در نظر گرفته میشوند که این امر هزینههای پروژه را افزایش میدهد. از طرف دیگر در این مقاله سعی شده تا روابط و تعامل ریسکها با یکدیگر شناسایی شده و در قالبِ مدلی مفهومی ارائه گردد. در مدل ارایه شده ریسکهای پیشران و وابسته تعیین خواهند شد که در بستر مدل مفهومی ارائه شده می توان با مدیریت ریسک های پیشران بر اندازه بافرهای کل پروژه تأثیر گذاشته و شاخص های زمانی و هزینه ای پروژه را در حد قابل قبولی بهبود ببخشد. به منظور ارائه مدل مفهومی از روش مدلسازی ساختاری تفسیری (ISM) استفاده شده است و برای فراهم ساختن اطلاعات کیفی مدل مصاحبه های عمیق با بیست و هفت نفر از خبرگان و مدیران پروژه های بزرگ در حوزه نفت و گاز انجام شده است. بر اساس مدل مفهومی ارایه شده و همینطور تحلیل همزمان، مشکلات ناشی از پیچیدگی و شناخت کم از مسایل فنی پروژه عاملِ اصلی است که باعث می شود مدیران و پیمانکاران مجبور به افزایش سطح بافرها شوند.
This paper aims to determine factors influencing on the buffer sizing based on Project Risks, which are usually subjective and qualitative. Because of the subjective feature, they can’t be calculated accurately and they are responsible for the project delays. In addition, because projects may enter a time of shortage, as well as inadequate resources, estimated time duration prolongs which in turn increases the project costs. On the other side, offering a conceptual model, this investigation aims to identify risks relationships and interactions. Fundamental and related risks were defined in the proposed model which is based on a conceptual model. The model also can be used to better buffer sizing and improve time duration and cost estimations. Interpretative Structural Modelling was used to develop the conceptual modeling and 27 experts in Oil and Gas Mega Projects were interviewed to gather the needed data to provide the model. Based on the conceptual model and simultaneous analysis, the problems caused by the complexity and low recognition of the technical issues of the project are the main factor that put managers and contractors in a situation to increase the level of buffers.
1- Chapman Chris, S. W. (2004). Project Risk MGMT Processes, Techniques and Insights. John Wiley .
2- Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96(2), 343–350.
3- Cohen, I., Mandelahum, A. and Shtub, A. (2004). Multi-Project Scheduling and Control:A Process-Based: Comparative Study of the Critical Chain Methodology and some alternatives. Journal of Project Management; 35(39).
4- Correia, F., A & Abreu, A. )2011(. An Interview of Critical Chain Applied to Project Management. Proceedings of the 4th International Conference on MEQAPS.
5- Cox, J. (2010). Theory of Constraints. Mc Grow Hill.
6- EM, G. (1992). The Goal 2nd revised ed. North River Press .
7- Fallah, M., Ashtiani, B., & Aryanezhad, M. (2010). Critical Chain Project Scheduling: Utilizing Uncertainty for Buffer Sizing. International Journal of Research and Reviews in Applied Sciences.3:3.
8- Goldratt, E.M.(1997). Critical Chain. North River Press, Massachusetts.
9- Gvindan Kannana, N., Shaligram Pokharel, B, P., Sasi Kumarc. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider, Resources, Conservation and Recycling 54 (2009): 28–36.
10- Herrolen, W.S., Leus, R. 2001.On the Merits and Pitfalls of Critical Chain Scheduling. Journal of Operation Management 19: 559-577.
11- J.N, W. (1976). Social Systems: Planning and Complexity. New York : Willy Interscience.
12- Kamandani Pour, Keyvan and Arbabian, Mohammad Ebrahim. (2011). Using Fuzzy Approach to Determine the Size of Time Buffers in Project Critical Management, Seventh International Project Management Conference, Tehran, Iran Project Management Association, https://www.civilica.com/Paper -IPMC07-IPMC07_074.html.
13- Kannan G. Pokharel, S. and Sasi Kumar, P. (2009). A hybrid approach using ISM and fuzzy Comment. Journal of Recycling, 54(1): 28–36.
14- Koochaki, Samaneh; Nahavandi, Nasim and Qalmi, Laleh.(2010). Determining the Buffer Size in Managing a Resource-Restricted Chain Management Project, First International Management and Innovation Conference, Shiraz, https://www.civilica.com/Paper-MIEAC01-MIEAC01_167. html.
15- Leach, L.P. (2005). Critical Chain Project Management. Boston: Artech House.
16- Newbold, A. (1998). Practical for Project Management . John Wiley.
17- Pittman, P. (1994). A more effective methods for the planning and controlling of Projects . University of Georgia , Doctoral diss.
18- Rand, G. (1998). Critical Chain . Journal of Operation Research Society , 49(2), 181.
19- Sasikumar P, Kannan G. Issues in reverse supply chain, part II: reverse distribution issues – an overview. International Journal
20- Warfield, J. W. (1974). Developing interconnected matrixes in structural modeling. IEEE Transcript on Systems, Men and Cybernetics, 4(1), 51−81.
21- Xiao-ping, & Yang Pan, Gao. (2011). A Quantitative Research of the Time Buffer of Critical Chain Project Management. International Conference on E-Business and E-Government (ICEE).
22- Yang, T., & Hung, C. C. (2007). Multiple-attribute decision making methods for plant layout design problem. Robotics and Computer-Integrated Manufacturing, 23(1): 126–137.
_||_1- Chapman Chris, S. W. (2004). Project Risk MGMT Processes, Techniques and Insights. John Wiley .
2- Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96(2), 343–350.
3- Cohen, I., Mandelahum, A. and Shtub, A. (2004). Multi-Project Scheduling and Control:A Process-Based: Comparative Study of the Critical Chain Methodology and some alternatives. Journal of Project Management; 35(39).
4- Correia, F., A & Abreu, A. )2011(. An Interview of Critical Chain Applied to Project Management. Proceedings of the 4th International Conference on MEQAPS.
5- Cox, J. (2010). Theory of Constraints. Mc Grow Hill.
6- EM, G. (1992). The Goal 2nd revised ed. North River Press .
7- Fallah, M., Ashtiani, B., & Aryanezhad, M. (2010). Critical Chain Project Scheduling: Utilizing Uncertainty for Buffer Sizing. International Journal of Research and Reviews in Applied Sciences.3:3.
8- Goldratt, E.M.(1997). Critical Chain. North River Press, Massachusetts.
9- Gvindan Kannana, N., Shaligram Pokharel, B, P., Sasi Kumarc. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider, Resources, Conservation and Recycling 54 (2009): 28–36.
10- Herrolen, W.S., Leus, R. 2001.On the Merits and Pitfalls of Critical Chain Scheduling. Journal of Operation Management 19: 559-577.
11- J.N, W. (1976). Social Systems: Planning and Complexity. New York : Willy Interscience.
12- Kamandani Pour, Keyvan and Arbabian, Mohammad Ebrahim. (2011). Using Fuzzy Approach to Determine the Size of Time Buffers in Project Critical Management, Seventh International Project Management Conference, Tehran, Iran Project Management Association, https://www.civilica.com/Paper -IPMC07-IPMC07_074.html.
13- Kannan G. Pokharel, S. and Sasi Kumar, P. (2009). A hybrid approach using ISM and fuzzy Comment. Journal of Recycling, 54(1): 28–36.
14- Koochaki, Samaneh; Nahavandi, Nasim and Qalmi, Laleh.(2010). Determining the Buffer Size in Managing a Resource-Restricted Chain Management Project, First International Management and Innovation Conference, Shiraz, https://www.civilica.com/Paper-MIEAC01-MIEAC01_167. html.
15- Leach, L.P. (2005). Critical Chain Project Management. Boston: Artech House.
16- Newbold, A. (1998). Practical for Project Management . John Wiley.
17- Pittman, P. (1994). A more effective methods for the planning and controlling of Projects . University of Georgia , Doctoral diss.
18- Rand, G. (1998). Critical Chain . Journal of Operation Research Society , 49(2), 181.
19- Sasikumar P, Kannan G. Issues in reverse supply chain, part II: reverse distribution issues – an overview. International Journal
20- Warfield, J. W. (1974). Developing interconnected matrixes in structural modeling. IEEE Transcript on Systems, Men and Cybernetics, 4(1), 51−81.
21- Xiao-ping, & Yang Pan, Gao. (2011). A Quantitative Research of the Time Buffer of Critical Chain Project Management. International Conference on E-Business and E-Government (ICEE).
22- Yang, T., & Hung, C. C. (2007). Multiple-attribute decision making methods for plant layout design problem. Robotics and Computer-Integrated Manufacturing, 23(1): 126–137.
