Research to a methodology to assess the overall risk position of Enexis
Author: B. Peppelman
Risk management is of major importance for companies nowadays and the lack of it can have enormous consequences as it is currently illustrated by the world wide credit crunch. Enexis uses risk management on a corporate and operational level. On the operational level Enexis uses risk management to find the optimal way to manage its physical asset base. Therefore Enexis is availed by an adequate operational risk management process. In this research a risk is defined as an event with a certain probability of occurrence, which negatively affects one of the business values of Enexis.
Enexis uses asset management to meet its mission, to be and stay an excellent network operator, and to meet the goals related to this mission. To be able to take complex decisions over the large number of assets, which show a big diversity, Enexis implemented the Risk Based Asset Management (RBAM) methodology (Essent Netwerk, 2006). This methodology has to guarantee an optimal risk reduction within the existing constraints of available funds and people. Within the current methodology most risks are treated as individual events that happen independent from each other. In practice risk are not independent events and for some risks policies are already in place. The current risk management process results in a overestimated risk position. Due to problems with interrelations/overlap the calculated risk position of Enexis shows an over-estimation of the true position. A over-estimated risk position may trigger more actions and expenses than can be justified on the basis of the actual risks Enexis faces. Eventually this may lead to a culture of incorrect decision-making and overspending Therefore Enexis want to optimize their risk management process. Hence the goal of this research is:
How can Enexis determine its risk position, make the effectiveness of her policy on the risk position theoretically and practically provable and how could this be secured in the future?
To answer this question the research is divided in two parts. The first part consists of a literature review of risk categorization, risk assessment methodologies and the choice of the optimal risk assessment methodology for Enexis. The second part consists of the application of the risk assessment methodology and the calculation of the improved risk position.
Within the current literature on risk categorization there is no unique best way for the categorization of risks. Each categorization of risks serves different objectives and therefore needs a specific categorization. Hence it is important that risk categories reflect management objectives instead of following a guideline for risk categorization. However there is no best way of categorization Morgan et al. (2000) defined a list of desirable attributes for an ideal risk categorization.
A literature study of risk assessment methodologies resulted in 28 different assessment methods. A quick scan of all methods resulted in two possible useful risk assessment methodologies for Enexis: Bow Tie Analysis or a combination of Fault Tree Analysis (FTA) and Event Tree Analysis (ETA). The Analytic Hierarchy Process (AHP) is used to determine the optimal risk assessment methodology for Enexis. As shown in the graph below the Bow Tie Analysis outperforms the combination of FTA and ETA on almost all criteria. From the graph it follows that the Bow Tie Analysis is the best risk assessment methodology for Enexis. A sensitivity analysis confirmed that changes in priorities do not significantly affect the outputs and therefore the ranking of the alternatives is assumed to be robust and satisfactory.
Assessing the risk position of Enexis With the desired attributed for ideal risk categorization in mind the risks identified by Enexis are categorized in eleven risk catogories. This results in about 25 till 30 risk per categorie, which is a well manageble amount of risk per category.
The Bow Tie methodology is applied on two of the defined risk categories to show its applicability on the risk register and specifically its use within the utility sector. As a result of the Bow Tie Analysis relations between risks and the relations between risks and risk management policies are visualized. During the process the quantification of Bow Tie Analysis appeared to be a complex matter. Quantification of Bow Tie Analysis require detailed information and knowledge about the effectively of barriers, since this information and knowledge is currently not available Enexis is at this moment in time not able to perform the final step of the Bow Tie Analysis and quantify the Bow Tie Analysis
The Bow Tie Analysis provided the desired insight in the relation between risks and the link between risks and policies. However to prove its effectiveness Enexis has to perform further research to the quantification of Bow Tie Analysis. It is recommended that Enexis starts with the determination of the effectiveness of barriers.
Monte Carlo Simulation is used to determine the risk position of Enexis. During the analysis of the risk register for the Bow Tie Analysis it became apparent that the data used for the Monte Carlo Simulation was not as accurate as it should be. Since simulations cannot be more accurate than the input variables they are based on this directly affects the accuracy of the simulation. This research limits itself to an improvement of the current input variables without questioning whether the type of probability distribution of the input variables is correct.
Based on the insight provided by the Bow Tie Analysis overlapping risks are excluded from the Monte Carlo Simulation. The combination of excluding overlapping risk and the improvement of the data quality resulted in an improved accuracy of the risk position between 100% and 270%. Therefore it is recommended that Enexis pays greater attention to the accuracy of the data in the risk register. However the theoretical risk position is improved it is still overestimating the actual risk position. Further research is needed to make a next step in the improvement of the theoretical risk position. It is expected that the assumed Poisson distribution might not be the best distribution to model the risk variables. However further research to the type of probability distribution used to model the risk variables is needed to justify this assumption. Read more >