Measuring and Mitigating Risk

ByStephen Johnson

Founded in 1975, Arc of Yates is a nonprofitorganization for people with developmental disabilities. We provide a widerange of community-based services, including servicecoordination, residential living, clinical services, employment opportunities,and industrial and educational development throughout Yates County in upstateNew York.

As a nonprofit organization, we primarilyrely on funding from state and local governments.These funding streams are affected by state and local budgetdeficits, early retirements of knowledgeable government workers who are notbeing replaced, inconsistent rate-setting methodologies and heightenedgovernment audit protocols.

As a result, we feel the strain on our own year-to-yearbudgets. With the recenteconomic downturn, we were unsure of how to account for potential shortfalls inour annual budget planning. When planning the budget, we also hadto consider a number of additional variables and uncertain factors, includingMedicaid rate reductions, state contract reductions, county contribution, stateaudits and inflation.

We decided to use Monte Carlo simulation, an analyticaltechnique that evaluates and measures the risk associated with any givenventure or project, to manage and mitigate these risks, and we chose [email protected] software to do so. Monte Carlo simulation is a computerized mathematicalprocess that allows users to define uncertain variables in their models andobtain a range ofpossible outcomes, along with theprobabilities that they will occur. Itcan show the extreme possibilities?outcomes for the both the most risky and themost conservative, along witheverything in between.

Thetechnique works by substituting ranges for values that applyto uncertain inputs in a model. These rangesare called probability distributions, where certain values are more likely tooccur than others. Thenormal distribution, or is a commonexample.

In @RISK, these probability distributions are sampled overand over to record new outcomes eachtime. This is thesimulation itself, and the result is a range ordistribution of possibleoutcomes and associated probabilities.

Such simulationsare highlyflexible tools used extensively in risk managementto gain insight into what could happen, so resources can be allocated moreeffectively, better strategies can be designed, mitigation plans can be developedand better decisions can be made. By exploring the full range ofpossible outcomes for a given situation, effective risk analysis such as thiscan both identify pitfalls and uncover new opportunities.