1. 08/08/02SJSU Bus 140 - David Bentley1 Course Part 2 Supply and Demand Management

2. Rev. 09/19/05SJSU Bus 140 - David Bentley2 Course Organization Part 2: Supply & Demand Management Forecasting: Chapter 3 Inventory management: Chapter 11 Aggregate planning: Chapter 12 MRP…, ERP & JIT: Chapters 13, 14 Supply chain management: Chapter 16

3. Rev. 03/01/02SJSU Bus 140 - David Bentley3 Chapter 3 – Forecasting Demand behavior, approaches to forecasting, measures of forecast error

4. Rev. 09/19/05SJSU Bus 140 - David Bentley4 Why Forecast? You’re wrong more than you’re right Often ignored or used as scapegoat Thankless job! Examples of the downside of forecasting

5. Rev. 09/19/05SJSU Bus 140 - David Bentley5 Why Forecast – (the answer)

6. Rev. 02/12/02SJSU Bus 140 - David Bentley6 Demand Components Components or Elements or Behavior Trend – long-term linear movement up or down Seasonal – short term recurring variations Cyclical – long-term recurring variations Random & Irregular – doesn’t fit other three components

7. Rev. 09/19/05SJSU Bus 140 - David Bentley7 Forecasting Approaches Qualitative (“subjective) Judgment and Opinion Quantitative (“objective”) Associative External sources of data Historical Internal sources of data used

8. Rev. 10/01/02SJSU Bus 140 - David Bentley8 Judgment and Opinion - 1 Sources Executives Marketing & Sales Projections Customers Potential customers “Experts”

9. Rev. 10/01/02SJSU Bus 140 - David Bentley9 Judgment and Opinion - 2 Appropriate Use Irregular or random demand New products Absence of historical data Techniques Surveys, questionnaires, interviews, focus groups, observation Delphi method

10. Rev. 10/01/02SJSU Bus 140 - David Bentley10 Associative Sources External industry data Demographic and econometric data Appropriate use Cyclical demand Technique Leading indicator, and Linear regression, in conjunction with Correlation

11. Rev. 10/031/02SJSU Bus 140 - David Bentley11 Historical Sources Historical (“time series”) data Appropriate use Varies (see later slides) Technique types Multi-period pattern projection Single period patternless projection

12. Rev. 10/01/02SJSU Bus 140 - David Bentley12 Multi-period Pattern Projection Techniques - Trend Appropriate use Clear trend pattern over time Techniques Best fit (“eyeball”) Linear trend equation or least squares Y t = a + bt b = n  (ty) – (  t)(  y) n  t 2 – (  t) 2 a =  y - b  t n

13. Rev. 10/01/02SJSU Bus 140 - David Bentley13 Multi-period Pattern Projection Techniques - Seasonal Appropriate use Seasonal demand Related to weather, holidays, sports, school calendar, day of the week, etc. Techniques Seasonal indexes or relatives Seasonally adjusted trend Separate trend from seasonality

14. Rev. 10/01/02SJSU Bus 140 - David Bentley14 Single Period Patternless Projection - 1 Appropriate use Lack of clear data pattern Limited historical data Techniques Moving Average (older method) F t =  A n Weighted moving average F t = a(A t-1 ) + b(A t-2 ) + … + x(A t-n )

15. Rev. 10/01/02SJSU Bus 140 - David Bentley15 Single Period Patternless Projection - 2 Techniques (continued) Exponential Smoothing (newer method) F t = F t-1 +  ( A t-1 – F t-1 ) Naïve Forecast Simple = last period’s actual (often used with seasonality) F t = A t-1 Advanced F t-1 = A t-1 ± (A t-1 - A t-2 ) / 2

16. Rev. 10/01/02SJSU Bus 140 - David Bentley16 Single Period Patternless Projection - 3 Techniques (continued) Double exponential smoothing aka second order exponential smoothing Special case Incorporates some trend Uses exponential smoothing formula plus second formula with additional smoothing constant

17. Rev. 03/04/02SJSU Bus 140 - David Bentley17 Multiperiod Pattern Projection BehaviorTechniqueTools Trend Trend line Linear regression or Best fit (eyeball) SeasonalSeasonal calculations Seasonal relatives (aka indexes) Trend and seasonal Seasonally adjusted trend Linear regression and seasonal relatives

18. Rev. 03/04/02SJSU Bus 140 - David Bentley18 Single Period Patternless Projection BehaviorTechniqueTools Random and Irregular Time series (historical) Moving average, weighted average, exponential smoothing, or naïve Random with some trend Time series (historical) Double exponential smoothing (aka trend adjusted or second order exponential smoothing)

19. Rev. 09/19/05SJSU Bus 140 - David Bentley19 Other Forecasting Methods BehaviorTechniqueTools CyclicalAssociative Leading indicator, regression and correlation All behaviorsJudgment and opinion Executive opinion, sales and marketing estimates, and/or customer surveys

20. Rev. 10/04/01SJSU Bus 140 - David Bentley20 Measures of Forecast Error - 1 Forecast Error (e, E, or FE) E t = A t - F t Average Error (AE) AE =  E n Mean Absolute Deviation (MAD) MAD =  |E| n

21. Rev. 03/02/05SJSU Bus 140 - David Bentley21 Measures of Forecast Error - 2 Mean Squared Error (MSE) MSE =  E 2 n-1 Standard Deviation (SD) SD = square root of  E 2 n-1 Mean Absolute Percent Error (MAPE) MAPE =  (|E|/A) (100) n

22. Rev. 07/22/04SJSU Bus 140 - David Bentley22 Controlling the forecast Control charts Upper and lower control limits (remember SPC?) – See Figure 3-11 Tracking Signal (TS) Reflects “bias” in the forecast TS =  (A – F) MAD Look for values within ± 4

23. Rev. 09/19/05SJSU Bus 140 - David Bentley23 Forecast accuracy Aggregation Would you rather forecast sales of all Ford automobiles or forecast a specific model? Time Would you rather forecast Ford sales for 2005 or for 2010?

24. Rev. 09/19/05SJSU Bus 140 - David Bentley24 Choosing and … Choosing a forecasting technique (T3.4) Nature of data (pattern?) Forecast horizon Preparation time Experience (may want to try several) Choosing a measure of forecast error Ease of use Cost

25. Rev. 10/01/02SJSU Bus 140 - David Bentley25 … Using Using forecast information Proactive vs. reactive Look at reasonability Assure everyone works off same data “What – if”