Dickson K.W. Chiu PhD, SMIEEE Text: Ballou - Business Logistics Management, 5/E (Chapter 8 )

1. Demand and Forecast Dickson K.W. Chiu PhD, SMIEEE Text: Ballou - Business Logistics Management, 5/E (Chapter 8)

2. Learning Objectives • To understand some basic concept of demand and forecasting • To anticipate typical problems involved in demand and forecasting Dickson Chiu 2006

3. What’s Forecasted in the Supply Chain • Demand, sales or requirements • Purchase prices • Replenishment and delivery times Dickson Chiu 2006

4. Some Forecasting Method Choices • Historical projection • Moving average • Exponential smoothing • Causal or associative • Regression analysis • Qualitative • Surveys • Expert systems or rule-based • Collaborative Dickson Chiu 2006

5. Typical Time Series Patterns: Random Dickson Chiu 2006

6. 250 200 Sales 150 100 Actual sales Average sales 50 0 0 5 10 15 20 25 Time Typical Time Series Patterns: Random with Trend Dickson Chiu 2006

7. Typical Time Series Patterns: Random with Trend and Seasonal Dickson Chiu 2006

8. Sales Time Typical Time Series Patterns: Lumpy Dickson Chiu 2006

9. Is Time Series Pattern Forecastable? • Whether a time series can be reasonably forecasted often depends on the time series’ degree of variability. Forecast a regular time series, but use other techniques for lumpy ones. How to tell the difference: • A time series is lumpy if where regular, otherwise. Dickson Chiu 2006

10. Analysis Details • See textbook if you are interested • Moving Average • Exponential Smoothing Formulas • Regression Analysis • Combined Model Forecasting • Note data requirements and timeliness requirement • Tracking signal monitors the fit of the model to detect when the model no longer accurately represents the data => events Dickson Chiu 2006

11. Actions When Forecasting is Inappropriate • Seek information directly from customers • Collaborate with other channel members • Apply forecasting methods with caution (may work where forecast accuracy is not critical) • Delay supply response until demand becomes clear • Shift demand to other periods for better supply response • Develop quick response and flexible supply systems, e.g., order-to-build of Dell Dickson Chiu 2006

12. Collaborative Forecasting • Demand is lumpy or highly uncertain • Involves multiple participants each with a unique perspective—“two heads are better than one” • Goal is to reduce forecast error • The forecasting process is inherently unstable Dickson Chiu 2006

13. Collaborative Forecasting Key Steps • Establish a process champion • Identify the needed information and collection processes • Establish methods for processing information from multiple sources and the weights assigned to multiple forecasts • Create methods for translating forecast into form needed by each party • Establish process for revising and updating forecast in real time • Create methods for appraising the forecast • Show that the benefits of collaborative forecasting are obvious and real Dickson Chiu 2006

14. Again much domain knowledge is required. Note the data / information requirements and how IT helps to collect / integrate the data for calculations and decision making. Capture forecasting signals (either determined by a business analyst or automatically by a sub-system) as events / exceptions / alerts and forward them to the appropriate system and personnel for decision / action. Collaborative forecasting as well as quick response and flexible supply systems requires much new IT in the process and information integration. Summary Dickson Chiu 2006