Forecasting IME 451, Lecture 2. Laws of Forecasting 1.Forecasts are always wrong! 2.Detailed forecasts are worse than aggregate forecasts! Dell forecasts.

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Presentation transcript:

Forecasting IME 451, Lecture 2

Laws of Forecasting 1.Forecasts are always wrong! 2.Detailed forecasts are worse than aggregate forecasts! Dell forecasts 410,000 orders for new computer systems in 2004 and 465,000 in 2005 Forecasts for the percentage of laptops vs. desktops are less reliable, as are configurations of processor speed, monitor style, hard drive size 3.The further into the future, the less reliable the forecast will be! In 2015 will 750,000 computer orders be filled, or 890,000?

Importance of Forecasting Forecasts provide the only sensible way to plan for future production and inventory Strive for robust forecasting decisions e.g., with agile manufacturing, plants can better respond to changes in product types and volumes, despite forecasting errors Cross-train workforce Shorten manufacturing cycle times to reduce dependence on forecasts

Forecasting Methods Qualitative – attempts to develop likely future scenarios using human expertise Delphi method – survey experts about likelihood of future events (technology introduction, industry trend) until consensus or stability is attained Quantitative – attempts to predict future based on a mathematical model Causal – predict a parameter as a function of other parameters (e.g., interest rates, GNP growth, etc) Time Series Models – predict a parameter as a function of its past values (e.g., historical demand)

Causal – Simple Linear Model Y – parameter to be predicted X i – predictive parameters b i – constants that are statistically determined from data (and b 0 is the Y-intercept of the straight line that best fits the data) Regression analysis is used to fit a function to the data

Time Series – Moving Average Simple average method Assumes no trend Moving average eliminates older data and considers only the last m time periods Higher values of m make model more stable, but less responsive to actual process changes Underestimates increasing trends; Overestimates decreasing trends

Exponential Smoothing Alpha is chosen by the user, 0 <  < 1 Lower values of  make model more stable, but less responsive to actual process changes Underestimates increasing trends; Overestimates decreasing trends Try different values for  and see which one generates a curve that follows historical data

Exponential Smoothing with a Linear Trend Tracks data with upward or downward trends  and  are smoothing constants between 0 and 1

Winters Method for Seasonality Tracks data with seasonal trends – ice cream, snow shovels, air conditioners, etc.   and  are smoothing constants between 0 and 1 c(t) is ratio of demand during period t to average demand during the season (the sum of c(t) factors = N if there are N periods in the season)

Evaluating Forecasting Models Mean absolute deviation, mean square deviation, or bias are used to evaluate models Minimize MAD and MSD, which are always positive BIAS should be as close to 0 as possible. But, this only indicates that errors are balanced +/-, not accurate

Final Note: Forecasting is an art…