Measuring and Mitigating RiskBy Stephen Johnson | Posted 2012-06-05 Email Print
WEBINAR: On-demand webcast
Next-Generation Applications Require the Power and Performance of Next-Generation Workstations REGISTER >
Community services provider Arc of Yates utilizes simulation program to predict potential budget shortfalls.
By Stephen Johnson
Founded in 1975, Arc of Yates is a nonprofit organization for people with developmental disabilities. We provide a wide range of community-based services, including service coordination, residential living, clinical services, employment opportunities, and industrial and educational development throughout Yates County in upstate New York.
As a nonprofit organization, we primarily rely on funding from state and local governments. These funding streams are affected by state and local budget deficits, early retirements of knowledgeable government workers who are not being replaced, inconsistent rate-setting methodologies and heightened government audit protocols.
As a result, we feel the strain on our own year-to-year budgets. With the recent economic downturn, we were unsure of how to account for potential shortfalls in our annual budget planning. When planning the budget, we also had to consider a number of additional variables and uncertain factors, including Medicaid rate reductions, state contract reductions, county contribution, state audits and inflation.
We decided to use Monte Carlo simulation, an analytical
technique that evaluates and measures the risk associated with any given
venture or project, to manage and mitigate these risks, and we chose Palisade’s
@RISK software to do so. Monte Carlo simulation is a computerized mathematical
process that allows users to define uncertain variables in their models and
obtain a range of
possible outcomes, along with the
probabilities that they will occur. It
can show the extreme possibilities—outcomes for the both the most risky and the
most conservative, along with
everything in between.
The technique works by substituting ranges for values that apply to uncertain inputs in a model. These ranges are called probability distributions, where certain values are more likely to occur than others. The normal distribution, or is a common example.
In @RISK, these probability distributions are sampled over and over to record new outcomes each time. This is the simulation itself, and the result is a range or distribution of possible outcomes and associated probabilities.
Such simulations are highly flexible tools used extensively in risk management to gain insight into what could happen, so resources can be allocated more effectively, better strategies can be designed, mitigation plans can be developed and better decisions can be made. By exploring the full range of possible outcomes for a given situation, effective risk analysis such as this can both identify pitfalls and uncover new opportunities.