Forecasting and Risk Analysis 

Challenge 

Are your sales forecasts wrong more often than they are right?  Do you understand the risks associated with your business decisions and their significance?  Are your decisions plagued with uncertainty sometimes causing you to make a best guess?  

Forecasting is used extensively in most organizations to predict future business outcomes in profitability, sales revenue, sales volume, production levels, or other measures of success.  Unfortunately, the models developed are limited by the nature of the spreadsheets used.  Typical approaches generate potential outcomes, but do not show the probability of achieving those outcomes, i.e., the degree of uncertainty.  As a result, we often see business decisions being made with high risks, and having a very low probability of ever working.  

Solution 

Our consultants work with you to develop forecasting models that are no longer restricted to deterministic point estimates.  By defining assumptions of each input variable with probability distributions and running Monte Carlo simulations, probabilistic forecasts are developed.  Thus you can determine the entire range of possible outcomes and the probability of a specific performance level being achieved.   

Approach 

Our forecasting and risk analysis process begins with Excel spreadsheet data.  We work with you to develop a model that defines the causal relationship between all process input and output variables.   Assumptions are then assigned for each input variable which are characterized by a probability distribution.  Historical data can be used to define the proper distribution or one can be assigned based on our experience. 

Each outcome or forecast is identified and a computer simulation is run using Monte Carlo or Latin Squares simulation techniques.  Forecasts are generated for the entire range of results possible for a given situation.  Confidence levels are also shown, so the likelihood of any specific event taking place is known.   

Finally, the results are analyzed using sensitivity analysis tools.  Changes are made to the model (or process) and the simulation is redone until the optimum results are achieved.  

Results 

Our process modeling techniques will help your organization better understand expected process outcomes and their probability of occurring.  Use of these techniques will help: 

p      Improve forecasting accuracy

p      Reduce inventory costs and lost revenue

p      Clarify the risks associated with business decisions to prevent costly errors

p      Focus resources on the right problems

p      Increase your competitive position