The cloud-based provider of business forecasting and planning solutions brings two decades of experience in Monte Carlo simulation to non-technical business users of its flagship software platform.
Cary, N.C. November 11, 2016 – Vanguard Software today announced that Vanguard Forecast Server™, the company’s IBP and Forecasting platform, now features Monte Carlo simulation.
The Advanced Analytics add-on gives customers the ability to model highly complex systems, production processes to consumer behavior, in ways that reveal not just potential outcomes, but the probabilities of those outcomes.
“Monte Carlo simulation is an invaluable strategic planning tool because it lets organizations test different courses of action to see, in each case, the likelihood of hitting specific targets, sustaining specific losses, or encountering countless other circumstances,” said Rob Suggs, CEO of Vanguard Software. He said the technology also delivers exceptional sensitivity analyses, letting users quantify the effect of specific drivers on specific outcomes.
For more than two decades, experts worldwide have relied on Vanguard Software Monte Carlo simulation to model uncertainty and volatility to better manage business opportunity and risk.
“The difference now is that non-expert business users across an organization can apply the same advanced-analytic technology to collaborate more effectively and gain a competitive edge,” Suggs said.
Vanguard’s Monte Carlo announcement comes just one week after the expansion of its cloud network from one data center in North Carolina to five data centers globally. That investment, which adds new sites in Frankfurt, Sydney, Oregon, and Virginia, is expected to improve the speed and reliability of all Vanguard solutions for customers worldwide.
Forecast Server’s new Monte Carlo add-on is no exception. The system operates in cloud, on-premise, or grid-computing environments, each of which can be configured to run exhaustive simulations in seconds – up 1,500 times faster than spreadsheets. That’s crucial because Monte Carlo simulations are designed to evaluate models over thousands of iterations.
In contrast, most organizations today use a traditional analysis known as the three-point estimate. This approach to business planning calculates low, medium, and high scenarios, or business outcomes. For revenue, the low and high represent the worst- and best-case scenarios of a plan’s performance. The medium represents the middle, or what some mistakenly assume is most likely outcome (in reality, it’s not).
Monte Carlo simulation replaces this rudimentary and inherently flawed analysis with one that produces far more dynamic and accurate predictions.
Unlike point-estimation, Monte Carlo simulation considers the potentially hundreds or thousands of possible outcomes – not just three. It also reveals the probability of each outcome.
One customer already uses Vanguard Monte Carlo simulation to test its multi-billion-dollar R&D pipeline, analyze long-term revenue potential, plan investments, and map out liquidity and other resource needs accordingly. Another uses it to make smarter decisions about how much to bid on contracts, and how to manage parts, staff, and facilities.
“Smart decisions require an understanding of risks and trade-offs,” said Suggs. “That’s exceedingly difficult if you have no clue of the likelihood of potential outcomes.”