Protecting asset value and driving performance with a dynamic, risk-based contingency fund

Environment Systems and Decisions - Tập 34 - Trang 417-424 - 2014
C. W. Mauelshagen1, S. J. T. Pollard1, D. Owen2, S. Herndlhofer2, P. Firth2, J. McKenna2, N. Bingley2, P. Jenson2
1Cranfield University, Cranfield, UK
2Yorkshire Water Services, Bradford, UK

Tóm tắt

We present a risk-based contingency fund management methodology to mitigate the impact of external risks on asset value and performance. Many asset intensive industries, such as water and energy utilities, are significantly affected by external risks such as extreme weather events. We put the case for a centrally held risk-based contingency fund that would mitigate against ‘medium’ impact ‘medium’ probability events that fall outside of large losses covered by insurance and smaller ‘normal’ operating losses. Our risk-based contingency approach is appropriate for short-term business planning (1–5 years) and would complement longer term planning, for example climate change adaptation and mitigation strategies. Our approach offers a risk-based methodology to manage contingency that is explicit and defensible. Critically, our methodology allows contingency to be managed dynamically as risk probabilities and impacts change, creating a mechanism for contingency funds to be periodically released if risk exposure reduces. The long-term benefit of dynamic, risk-based contingency is to reduce the impact of external risks and support long-term sustainability.

Tài liệu tham khảo

Alarcón LF, Ashley DB, de Hanily AS et al (2011) Risk planning and management for the panama canal expansion program. J Constr Eng Manag 137:762–771. doi:10.1061/(ASCE)CO.1943-7862.0000317 Easterling DR, Meehl GA, Parmesan C et al (2000) climate extremes: observations, modeling, and impacts. Science 289:2068–2074. doi:10.1126/science.289.5487.2068 Eti MC, Ogaji SOT, Probert SD (2006) Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture. Appl Energy 83:1235–1248. doi:10.1016/j.apenergy.2006.01.002 Flage R (2013) On risk reduction principles in the context of maintenance optimisation modelling. Proc Inst Mech Eng Part O J Risk Reliab 227:241–250. doi:10.1177/1748006X13489954 Hopkins A (2008) Failure to learn: the BP Texas City refinery disaster. CCH Australia, Sydney, NSW IBM (2007) IBM Research | Technical Paper Search | Harnessing Uncertainty: the future of risk analytics (search reports). http://domino.research.ibm.com/library/cyberdig.nsf/papers/B910FD442135744585257434005349F4. Accessed 1 May 2013 Le May I, Deckker E (2009) Reducing the risk of failure by better training and education. Eng Fail Anal 16:1153–1162. doi:10.1016/j.engfailanal.2008.07.006 Pollard SJT, Strutt JE, Macgillivray BH et al (2004) Risk analysis and management in the water utility sector: a review of drivers, tools and techniques. Process Saf Environ Prot 82:453–462. doi:10.1205/psep.82.6.453.53207 Rees M (2008) Financial modelling in practice. Wiley [distributor], Hoboken, NJ, Chichester Rowan E, Evans C, Riley-Gilbert M, Hyman R, Kafalenos R, Beucler B, Rodehorst B, Choate A, Schultz P (2013) Assessing the sensitivity of transportation assets to extreme weather events and climate change. Transp Res Rec J Transp Res Board 2326:16–23. doi:10.3141/2326-03 Taroun A (2014) Towards a better modelling and assessment of construction risk: insights from a literature review. Int J Proj Manag 32:101–115. doi:10.1016/j.ijproman.2013.03.004 Wu S, Hrudey S, French S et al (2009) A role for human reliability analysis (HRA) in preventing drinking water incidents and securing safe drinking water. Water Res 43:3227–3238. doi:10.1016/j.watres.2009.04.040