| Cause-and-Effect Modeling
The Problem
Managers are paid to anticipate the future and make appropriate adjustments, and planners are paid to provide them with the right information to do that successfully. Faced with shifts in their business environments, managers and planners need to understand the effects of these shifts on their own business. Of special interest to them are situations where key business variables that are highly interrelated move away from their current levels, creating new scenarios and possibly new opportunities. Will auto production increase in the near future? Will new housing starts decrease during the same time period? If so, what will be the combined effect on the demand for glass industry-wide?
In addition, managers and planners would like to know whether a planned change in one aspect of their business, such as a 10% reduction in product prices coupled with a 5% increase in advertising expenditures, will have a significant effect on another aspect of their business, such as the level of company sales revenues.
The Smart Solution
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| SmartForecasts’ multiple regression models
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Cause-and-effect modeling uses “regression” analysis to quantify the effects of one or more “predictor” variables on another “dependent” variable you want to forecast. SmartForecasts’ multivariate regression capability implements the power of causal modeling in an easy-to-use format, supported by informative reports and graphical displays. Demand patterns for the predictor variables can themselves be forecasted automatically, and those forecasts can then be converted via a regression model into forecasts of the dependent variable. Alternatively, regression models can directly convert current values of “leading indicators” into forecasts of future values of dependent variables. Both approaches can facilitate “what-if” scenario analyses that allow you to better anticipate changes in your business situation.
The Benefits
With SmartForecasts, you can exploit your market knowledge to build multiple regression models linking causal factors, such as product price, advertising expenditures and general economic conditions, to dependent variables like market demand and company sales revenues. These models can be used to forecast most likely scenarios as well as any number of what-if scenarios. The latter is especially important for coping with the possibility of major disruptions in the current way of doing business—which is becoming a significant issue for business planning and management.
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