Improving Forecasts with Monte Carlo Simulations

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This Slideshow contains a brief description of Monte Carlo simulation and how it can benefit financial forecasting and other business modeling.

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Improving Forecasts with Monte Carlo Simulations

  1. 1. USING MONTE CARLO SIMULATION TO INCREASE FORECASTING CONFIDENCE Michael A. Wallace, CPA, CGMA, MBA michaelawallace@cpa.com
  2. 2. What is Monte Carlo Simulation (MCS)? • Investopedia Says: • A problem solving technique used to approximate the probability of certain outcomes by running multiple trial runs, called simulations, using random variables. One of the creators was fond of the casinos in Monte Carlo, hence the name
  3. 3. MCS Is Used In • Science, engineering, portfolio management, and business decision making • MCS was developed at Los Alamos National Labs during nuclear bomb development • Which, as the photo indicates, is another way of saying, MCS works
  4. 4. MCS Reduces Uncertainty • Forecasting anything, tomorrow’s weather, next month’s sales, commission payouts in Q3, ROI on an investment is difficult because it is about the FUTURE
  5. 5. For Many Forecasting is an Unweighted Roll Up of • Informed and uninformed opinions • Negotiated compromises • Swags • Incentive compensation sand bagging • Projection of past performance to the future
  6. 6. And After All That Pain (And Time) • Organizations often end up with a Single Point Estimate which is the NUMBER, but that no one believes is the RIGHT number Typical CFO After Preparing A Forecast • MCS improves forecasting so let’s discuss Distributions
  7. 7. Distributions? • To improve forecasts consider the estimates received as a distribution of possible outcomes • You are familiar with some distributions • Normal Distribution (The Bell Curve) is a distribution • The Classic Best, Worse, Most Likely Case ( A Triangular Distribution) • Binomial Distribution where an event either occurs or it doesn’t • Don’t focus on a specific number, Focus on getting the range, probabilities and shape of the distribution
  8. 8. Wait….Range? Not This Kind of Range • Determine the range of possible outcomes I.E. The boundaries • E.G. sales next quarter will not be less Than $1m because next quarter the Smithson purchase is delivered • EG sales next quarter will not exceed $2m because $2m is all the product that can be delivered
  9. 9. Probabilities? • What is the likelihood of a specific outcome? • Is it possible for outcomes beyond the range to occur? • Are some outcomes more likely than others? • Or do all outcomes have the same likelihood of occurring?
  10. 10. Shape? • Considering the Range and Probabilities, What is the Shape of the Distribution? • Is the Shape Normal or Triangular or Pert or Binomial? • Examples of each are below
  11. 11. A Simple Example • The CFO of a Software Reseller is preparing next quarter’s forecast for 5 products • After discussions with Sales, Marketing and BizDev The CFO creates the table on the next slide
  12. 12. Nice Table, But Which Number Do You Forecast? Software Product Worse Ale Most Likely Best 15,000 30,000 50,000 8,000 22,000 30,000 Cataloger 10,000 20,000 25,000 Dolphin 12,000 18,000 40,000 Elasticity 25,000 50,000 100,000 Sum 70,000 140,000 245,000 Bonsai Average of Cases 151,667 Note that this table indicates the range, the shape and the probabilities.
  13. 13. How and What Would You Decide? • Judgment? Or Average? Or the Most Likely? • Regardless of choice, what confidence is there that the choice was rational and defensible? • The next slide shows how you could decide…
  14. 14. How About Running 10,000 Simulations and Getting This Distribution (in Ten Seconds)
  15. 15. The Chart Isn’t Interactive, but Indicates • 0% Probability of Exceeding Best Case-$245,000 • 67% Probability of Exceeding Most Likely Case-$140,000 • 80% Probability of Exceeding $133,000 • 90% Probability of Exceeding $126,000 • 100% Probability of Exceeding Worse Case-$70,000 • Now Can You Decide What to Forecast?
  16. 16. Yes, You Can! The Original table with distributions added. Software Product Worse Most Likely Best Ale 15,000 30,000 50,000 8,000 22,000 30,000 10,000 20,000 25,000 12,000 18,000 40,000 25,000 50,000 100,000 70,000 140,000 245,000 Bonzai Cataloger Dolphin Elasticity Sum Mean of Cases 151,667 Distributions
  17. 17. But Wait, There is More! • What if MCS could also reveal which input had the most uncertainty? • Hint: It Can • Go back to prior slide and guess which one that is. • Then proceed to the next slide
  18. 18. It is the Elasticity Product-As Shown in This Tornado Chart
  19. 19. Which Means? • To further increase confidence in the forecast, focus on tightening the sales forecast for Elasticity • MCS not only increases confidence in the forecast it helps prioritizes actions that increase confidence even more!
  20. 20. Forecasting in Real Life Is Complicated • Much more complicated than the simple model used in this slide show • Real life models have thousands of inputs, not five • Many estimates don’t fit into a Worse, Most Likely, Best Distribution • Contingencies and Binominal Distributions are common
  21. 21. MCS Is A Powerful Tool • To improve forecasting • To identify priorities • To create more reliable forecasts • To increase confidence in models
  22. 22. Other Uses of MCS • Acquisition Modeling • Optimizing Inventory Stocking Levels • Portfolio Return Forecasting • Project Management Timelines • Pricing Decisions • And Yes, Building of Nuclear Weapons
  23. 23. Contact For Questions • MCS concepts are difficult to convey in a slide show so for more information contact • Michael Wallace @michaelawallace@cpa.com • The Software tool used in this Slide Show Was @Risk by Palisade. Learn more at www.palisade.com

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