1 / 18

Defn : Forecasting is the art and science of predicting future events.

Defn : Forecasting is the art and science of predicting future events. 1. Identify the. 2. Collect. 3. Plot data and. purpose of forecast. historical data. identify patterns. 9. Adjust forecast based. 8a. Forecast over. on additional qualitative.

desma
Télécharger la présentation

Defn : Forecasting is the art and science of predicting future events.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Defn: Forecasting is the art and science of predicting future events.

  2. 1. Identify the 2. Collect 3. Plot data and purpose of forecast historical data identify patterns 9. Adjust forecast based 8a. Forecast over on additional qualitative planning horizon information and insight Forecasting Process 5. Develop / compute forecast for 6. Check forecast accuracy 4. Select a forecast model that period of historical data with one or more measures seems appropriate for data 8b. Select new forecast model or adjust parameters of existing model 7. Is accuracy of forecast acceptable? 10. Monitor results and measure forecast accuracy

  3. 1. Identify the 2. Collect 3. Plot data and purpose of forecast historical data identify patterns 9. Adjust forecast based 8a. Forecast over on additional qualitative planning horizon information and insight Forecasting Process 5. Develop / compute forecast for 6. Check forecast accuracy 4. Select a forecast model that period of historical data with one or more measures seems appropriate for data 8b. Select new forecast model or adjust parameters of existing model 7. Is accuracy of forecast acceptable? 10. Monitor results and measure forecast accuracy

  4. Forecasting Methods • Time Series Models • Associative Models • Qualitative

  5. Associative Models Assumption: One or more variables can be identified which has a relationship with demand. Approaches: Simple Linear Regression Multiple Linear Regression

  6. “Time Series” Defn: A time-ordered sequence of observations that have been taken at regular intervals. Examples: past monthly demands, past annual demands. Assumption: Future values can be estimated from past values of the series.

  7. Time Series Approaches • Naïve Approach • Moving Averages • Exponential Smoothing • Trend Projection • Seasonal Adjustments

  8. 1. Identify the 2. Collect 3. Plot data and purpose of forecast historical data identify patterns 9. Adjust forecast based 8a. Forecast over on additional qualitative planning horizon information and insight Forecasting Process 5. Develop / compute forecast for 6. Check forecast accuracy 4. Select a forecast model that period of historical data with one or more measures seems appropriate for data 8b. Select new forecast model or adjust parameters of existing model 7. Is accuracy of forecast acceptable? 10. Monitor results and measure forecast accuracy

  9. Step 3: Demand Behavior • Trend • gradual, long-term up or down movement • Cycles • up & down movement repeating over long time frame • Seasonality • periodic oscillation in demand which repeats based on calendar schedule (days, weeks, months or quarters) • Random movements- follow no pattern • Class Exercise:Decompose the TimeSeriesGraphs.xls time series into the behavioral components

  10. Which demand behavior is most prevalent in Chart 1? • Upward trend • Downward trend • Seasonality • Random Variation

  11. Which demand behavior is most prevalent in Chart 2? • Upward trend • Downward trend • Seasonality • Random Variation

  12. Which demand behavior is most prevalent in Problem Set 1 #1? • Upward trend • Downward trend • Seasonality • Random Variation

  13. Step 4: Which Time Series Model Should You Pick? • Three possible models to choose from when there is no seasonality and not a strong trend pattern: • Naïve Approach • n Period Moving Average • Exponential Smoothing

  14. Demand in Previous n Periods MA  n nperiod Moving Average Method • MA is a series of arithmetic means • Used if little or no trend • Used oftenfor smoothing

  15. 1. Identify the 2. Collect 3. Plot data and purpose of forecast historical data identify patterns 9. Adjust forecast based 8a. Forecast over on additional qualitative planning horizon information and insight Forecasting Process 5. Develop / compute forecast for 6. Check forecast accuracy 4. Select a forecast model that seems appropriate for data period of historical data with one or more measures 8b. Select new forecast model or adjust parameters of existing model 7. Is accuracy of forecast acceptable? 10. Monitor results and measure forecast accuracy

  16. Step 6: Forecast Error Equations • k= # of historical periods forecasted • Mean Absolute Deviation (MAD) • Mean Absolute Percent Deviation (MAPD)

  17. Exponential Smoothing • New Forecast • = Last Period Forecast + Correction for error made last period • = Last Period Forecast + α (Last Period Demand – Last Period Forecast) • Class exercise: • Identify the best α for Smooth.xls time series

  18. Guidelines for Selecting Forecasting Model • You want to achieve: • No pattern or direction in forecast error • Error = (Ai - Fi) = (Actual - Forecast) • Seen in plots of errors over time • Smallest forecast error • Mean absolute deviation (MAD) • Mean absolute percent deviation (MAPD) • Mean square error (MSE)

More Related