QUANTITATIVE ANALYSIS FOR DECISION MAKING - Essay Example

A forecasting model refers to the approach or technique that is used in the art and science of predicting the outcome of future events. The time horizon for any forecast varies: Short- range forecast can go up to one year, but generally encompasses only a three month period. These forecasts are typically used for planning, purchasing, job scheduling production and costs assignments. Medium range forecast generally spans from there months up to three years. The typical tasks involve sales and production planning, budgeting, and operational plan analysis.

Long-range forecast involve timeframes of over three years involving capital expenditures, R&D, new product planning and expansion (Heizer & Render). There are two different approaches of forecasting. The first approach is subjective or qualitative where factor’s such as the manager’s intuition, experience, emotions and opinion are utilized in reaching a forecast. The second quantitative approach involves the use of some historical data coupled with the use of some mathematical models in order to predict possible future outcomes.

The qualitative approach is often useful in estimating sales for certain new consumer products or services that rely heavily on customer needs and preferences. Under the qualitative approach to forecasting there are four different types of methods used (Heizer, et. al.): Jury of executive Opinion- the opinion of a group of high level managers or decision makers is used in combination with some statistical models to arrive at a conclusion Sales Force composite- the individual salesperson sales forecasts are all combined and reviewed for accuracy in order to determine an overall forecast.

Delphi Method- in these approach there are three types of participants: staff personnel, respondents and decision makers. The decision makers typically consist of a panel of 5 to 10 experts who will be making the actual forecasts. The staff personnel assist the decision makers by preparing, redacting, collecting and summarizing all questionnaires and survey results. Consumer Market Survey- this method involves using the input and opinion from customers or potential customers regarding their preferences, future purchase plans and product opinion.

The quantitative approach is divided between the time series models and causal models. Both the time-series and causal model are typically used by technology, manufacturing, and software companies. The time series model works under the assumption that the future is a function of the past. There are three ways of using the Time Series approach (Mann): Naive approach- is the simplest way to forecast because it simply assumes that future sales will be the same as the demand for the previous period.

It helps provide a starting point to which more sophisticated approaches can be incorporated. Moving Average- it incorporates a number of recent historical data records to generate an average which can be used to forecast sales. Exponential smoothing- it is a more sophisticated version of moving- average. The basic moving average smoothing formula is: Last period forecast + U (last period actual demand-last period forecast) U is a weight or smoothing constant chosen by the forecaster. This concept simply tries to adjust the latest estimate demand by a fraction of the difference between last period’s actual demand and the old estimate.

Since the U can vary from 0 to 1, the forecaster can tailor the weight of the adjustment to suit changes in consumer demand. The causal model is a more sophisticated quantitative approach which usually considers more than one variable related to the forecast being predicted. There are two types of causal model trends analysis and linear-regression causal analysis. Once the related variables are recognized, a statistical model is used to build the forecast. Simply stated the causal model is much more precise than the time-series model that only uses historical data to build the forecast model.

Work Cited Page Mann, P. 1995. Statistics For Business and Economics. New York: John Wiley & Sons. Heizer, J., Render, B. 1996. Production & Operation Management (4th ed.). New Jersey: Prentice Hall.