Volume 2, Issue 3, September 2018, Page: 58-66
Hybrid Framework of, EWGM-FMEA, Analytical Hierarchy Process and Risk Balance Score Card for Risks Assessment in Energy Sector
Sahar Mohammad Al Mashaqbeh, Department of Mechanical and Automotive Engineering, University of Bradford, Bradford, UK
Jose Eduardo Munive Hernandez, Department of Mechanical and Automotive Engineering, University of Bradford, Bradford, UK
Mohammad Khurshid Khan, Department of Mechanical Engineering, Abdul Wali Khan University, Mardan, Pakistan
Received: Oct. 30, 2018;       Accepted: Nov. 21, 2018;       Published: Dec. 17, 2018
DOI: 10.11648/j.ijem.20180203.12      View  80      Downloads  10
Power plants are very important for continuous electricity energy supply and have been affected by many disruptions. Furthermore, the power grid is a critical item for both economy and society. Accordingly, the aim of this paper is to adopt a risk assessment tool combining an improved Failure Mode and Effect Analysis (FMEA), Analytical Hierarchy Process (AHP) and enhanced Risk Balance Score Card (RBSC) to model nine risk categories in the energy sector. The outputs of the improved FMEA methodology will be utilised as the inputs for the BSC-AHP framework. The improved FMEA methodology combines the exponential and weighted geometric mean to overcome some drawbacks of the conventional FMEA. The approach helps the top management in prioritising 84 risk indicators particularly, in power plants. The results of this model elucidate that the highest priority (most risky perspective) is for the supply chain perspective with 24.2% of the influence, followed by the internal and operational business process perspective with 18.4%. In this perspective, the technical risk is the key risk with 10.4% followed by the disruption risk with 9.4% while the lowest priority risk in this perspective is the project neglect risk with 2.5%. The sustainability perspective coming as the third priority perspective with 17.7%, where the environmental and safety health category covers about 41.7%, followed by the technological pillar with 35.5% and the social pillar with 22.8%. At the fourth level, the customer/demand perspective is coming with 14%, where the load forecasting risk has the highest priority in this perspective with 49%. The learning and growth perspective stay at the fifth level with 13% where the human resources risks category has more influence than the management risks category. The lowest risk perspective priority is the economic perspective with 12.7%. These results will help the top management in taking a holistic view of various non-technical risks at the strategic level and the priority for each one then, the suitable decision can be taken. The significance of this research is in presenting a novel improved for the traditional FMEA and combining it with the BSC-AHP methods to improve the risk assessment process of 84 risks of six perspectives of BSC in power plants at the strategic level.
Exponential Weighed Geometric Mean (EWGM), FMEA, RPN, AHP and BSC
To cite this article
Sahar Mohammad Al Mashaqbeh, Jose Eduardo Munive Hernandez, Mohammad Khurshid Khan, Hybrid Framework of, EWGM-FMEA, Analytical Hierarchy Process and Risk Balance Score Card for Risks Assessment in Energy Sector, International Journal of Engineering Management. Vol. 2, No. 3, 2018, pp. 58-66. doi: 10.11648/j.ijem.20180203.12
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
K. O. Achebe, “Risk Based Models for the Optimization of Oil and Gas Supply Chain Critical Infrastructure,” 2011.
N. H. Afgan, M. G. Carvalho, and N. V. Hovanov, “Energy system assessment with sustainability indicators,” Energy Policy, vol. 28, no. 9, pp. 603–612, 2000.
J. Gonzalez, “Modelling and Controlling Risk in Energy Systems,” University of Manchester, 2015.
M. Garbuzova-schlifter and R. Madlener, “AHP-based risk analysis of energy performance contracting projects in Russia,” Energy Policy, vol. 97, pp. 559–581, 2016.
Y. Geng, J. Fu, J. Sarkis, and B. Xue, “Towards a national circular economy indicator system in China: An evaluation and critical analysis,” J. Clean. Prod., vol. 23, no. 1, pp. 216–224, 2012.
D. D. Wu and D. L. Olson, “Introduction to the special section on ‘optimizing risk management: Methods and tools,’” Hum. Ecol. Risk Assess., vol. 15, no. 2, pp. 220–226, 2009.
X. Liu and T. Arthanari, “A system dynamics model for managing corruption risks in dairy supply chains,” 34th Int. Conf. Syst. Dyn. Soc., pp. 1–17, 2016.
WEF, “Building Resilience in Supply Chain,” Geneva, 2013.
P. Burgherr, P. Eckle, and S. Hirschberg, “Comparative assessment of severe accident risks in the coal, oil and natural gas chains,” Reliab. Eng. Syst. Saf., vol. 105, pp. 97–103, 2012.
A. Samvedi, V. Jain, and F. T. S. Chan, “Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS,” Int. J. Prod. Res., vol. 51, no. 8, pp. 2433–2442, 2013.
B. R. S. Kaplan, “Risk Management and the Strategy Execution System,” 2009.
G. Radivojević and V. Gajović, “Supply chain risk modeling by AHP and Fuzzy AHP methods,” J. Risk Res., vol. 17, no. 3, pp. 337–352, 2014.
M. S. Beasley, B. C. Branson, B. V Hancock, and C. Landes, “Developing key risk indicators to strengthen enterprise risk management,” 2010.
M. A. Brown, D. D. Arcy, L. Melissa, Sharma Isha, and Li Yufei, Solid Waste from the Operation and Decommissioning of Power Plants. 2017.
T. L. Saaty, “Decision making with the analytic hierarchy process,” Int. J. Serv. Sci., vol. 1, no. 1, p. 83, 2008.
Browse journals by subject