Forecasting

Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions

What is Forecasting? The art and science of predicting future events

Time Horizon Short Range – 3 – 12 months Medium Range – 3 months – 3 years Long Range – 3+ years

Where do we Use Forecasts? Economic Forecast Inflation rate Exchange Rate Technological Forecast Probabilities of new discoveries Time to commercialize technologies Demand Forecast

Impact of Forecasts Human Resources: forecast gives warning of need to hire or lay off Production Capacity: forecast gives warning of need for more or less capacity Supply Chain: forecast gives warning of need for more or less inputs to production

How to Make a Forecast Determine use of forecast Select variable to be forecasted Determine time horizon Select forecasting model Gather data Make forecast Implement results and review model

Qualitative Methods Jury of Executive Opinion Sales Force Composite Delphi Consumer Marketing Survey

Quantitative Methods Time Series Associative

Time Series Methods A sequence of evenly spaced data points (weekly, monthly, quarterly, etc) Future values predicted only from past values X axis is always time

Example of a Time Series Year 1 2 3 4 Seasonal peaks Trend component Actual demand line Average demand over four years Demand for product or service Random variation

Trend Upward or downward pattern Due to changes in income, population, technology, etc Several years duration Time Demand © 1984-1994 T/Maker Co.

Seasonality Repeating pattern over a period Could be quarterly, monthly, weekly Due to weather or customs Time Demand Summer © 1984-1994 T/Maker Co.

 Cycles Pattern that occurs over several years Affected by political events or international turmoil Time Demand Cycle 

Random Variations Erratic, unsystematic Caused by random chance and unusual situations Short duration, non-repeating

Naïve Approach Forecast for next period is the same as demand in most recent period

Moving Average Approach MA n   Demand in Previous Periods

Weighted Moving Average Σ(Weight for period n) (Demand in period n) WMA = ΣWeights

Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1)

MAD

MSE

Exponential Smoothing With Trend Adjustment Ft = (At) + (1- )Ft-1 + Tt-1 Tt = (Ft - Ft-1) + (1- )Tt-1

Linear Trend Projection Equation: Slope: Y-Intercept:

Seasonal Variations Calculate average historical demand for each season Compute average demand over all periods Compute a seasonal index – historical demand / average demand Estimate next year’s total demand Divide estimate by number of seasons, multiply by seasonal index

Regression Analysis An associative method Find the relationship between an independent variable and a dependant variable Independent variable is a variable other than time

Regression Analysis Equation: Slope: Y-Intercept:

Standard Error of Estimate

What Does Standard Error Mean? Standard Deviation of data forming the regression line. If error becomes large, regression data is widely dispersed and less reliable

Correlation Coefficient

What Does Correlation Coefficient Mean? Strength of linear relationship between independent variable and dependant variable A number between +1 and -1

What Does Correlation Coefficient Mean? -1.0 +1.0 Perfect Positive Correlation Increasing degree of negative correlation -.5 +.5 Perfect Negative Correlation No Correlation Increasing degree of positive correlation

Coefficient of Determination Percent of variation in dependant variable that is explained by the regression equation

Evaluating the Forecast Monitor the forecast with a tracking signal = RSFE / MAD Small deviations are ok and should cancel each other out over time A consistent tendency for the forecast to be higher or lower than actual values is called a bias error

Tracking Signal Limits +/- 2, 3 or 4 MAD’s Smaller range = less tollerance of error But smaller range = higher costs

Other Ways to Forecast Adaptive Smoothing – Exponential smoothing constants adapted when tracking signal outside limits Focus Forecasting – Computer tries all forecast methods and selects best fit for next month’s forecast