Quantitative Forecasting Techniques:

Based on historical information that is usually available within the company. Various techniques are:

Trend Analysis:
A method for forecasting sales data when a definite upward or downward pattern exists. Model includes double exponential smoothing, regression & triple smoothing.

Seasonal Adjustment:
Seasonal models take into account the variation of demand from season to season. Adjustments can be made to a baseline forecast to predict the impact of a seasonal demand.

“A method of forecasting where time series data are separated into up to three components: trend, seasonal, and cyclical; where trend includes the general horizontal upward or downward movement over time; seasonal includes a recurring demand pattern such as day of the week,
weekly, monthly, or quarterly; and cyclical includes any repeating, non seasonal pattern. A fourth component is random, that is, data with no pattern. The new forecast is made by projecting the patterns individually determined and then combining them”.

Graphical Methods:
Plotting information in a graphical form. It is relatively easy to convert a spreadsheet into a graph that conveys the information in a visual manner. Trends & patterns are easier to spot & extrapolation of previous demand can be used to predict future demands.

Econometric Modeling:
A set of equations intended to be used simultaneously to capture the way in which dependent and independent variables are interrelated.

Life Cycle Modeling:
“A quantitative forecasting technique based on applying past patterns of demand data covering introduction, growth, maturity, saturation, and decline of similar products to a new product family”.