1. Slides in student pack may differ from slides used in class Forecasting and Demand Management POM 522 copyright 2004 by Frank Montabon

2. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon2 Opening Thoughts

3. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon3 Before we begin, what are we after? i.e., what do we want out of our forecast?

4. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon4 Basic Forecasting Methods –What happens when you change the number of periods used? Moving Average Exponential Smoothing (16.4) ESF t =  (Actual Demand t ) + (1-  )ESF t-1 (16.6) What happens when you change  ? Trend Seasonal Trend + Seasonal

5. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon5 Aggregating and Disaggregating Forecasts What is it? Which “end” is more accurate?

6. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon6 Some measures of error

7. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon7 Focusing/Averaging

8. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon8 10 Myths of Forecasting from Jain, C. L., “What’s the Forecast?” APICS-the Performance Advantage, Oct 2001 1.Forecasts should be 100 percent accurate 2.The more sophisticated the model, the better 3.There is a magic model – it’s just a matter of finding it 4.Once the best model is discovered, it will work all the time 5.A well-trained forecaster can prepare a forecast without anyone’s help

9. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon9 10 Myths of Forecasting from Jain, C. L., “What’s the Forecast?” APICS-the Performance Advantage, Oct 2001 6.Forecasting is a science 7.Separate forecasts should be prepared for each stockkeeping unit (SKU) 8.Accurate forecasts are the be all and end all of forecasting 9.Shipment forecasts drive business plans 10.To be a good forecaster, one must be a high-powered mathematician or statistician

10. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon10 Demand Management Why?

11. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon11 Demand Management as a Pipe of Capacity Adapted from VBWJ 5e, p. 25 OrdersKnowledgeForecast Capacity Time

12. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon12 Organizing for Demand Management VBWJ -> unified organizational home for DM less important than integrated data base

13. Slides in student pack may differ from slides used in class © 2004 by Frank Montabon13 Using the System