1. 1 Chapter 13 Forecasting  Demand Management  Qualitative Forecasting Methods  Simple & Weighted Moving Average Forecasts  Exponential Smoothing  Simple Linear Regression  Collaborative Planning, Forecasting, and Replenishment (CPFR)

2. 2 Demand Management A Independent Demand: Finished Goods B(4) C(2) D(2)E(1) D(3)F(2) Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc.

3. 3 Independent Demand: What a firm can do to manage it.  Can take an active role to influence demand – –  Can take a passive role and simply respond to demand –

4. 4 What Is Forecasting?  Process of predicting a future event  Underlying basis of all business decisions –Production –Inventory –Personnel –Facilities  Sales will be $200 Million!

5. 5 Types of Forecasts by Time Horizon  Short-range forecast – –Job scheduling, worker assignments  Medium-range forecast – –Sales & production planning, budgeting  Long-range forecast – –New product planning, facility location

6. 6 Types of Forecasts by Item Forecast  Economic forecasts –Address business cycle –e.g., inflation rate, money supply etc.  Technological forecasts –Predict technological change –Predict new product sales  Demand forecasts –Predict existing product sales

7. 7 Types of Forecasts  Qualitative (Judgmental)  Quantitative – Time Series Analysis – Causal Relationships – Simulation

8. 8 Components of Demand  Average demand for a period of time  Trend  Seasonal element  Cyclical elements  Random variation  Autocorrelation

9. 9 Finding Components of Demand 1234 x x x x x x xx x x x xxx x x x x x xx x x x xxx x x x x x x x x x x x x x x x x x x x x Year Sales Seasonal variation Linear Trend

10. 10 Cyclical Component  Repeating up & down movements   Usually 2-10 years duration Mo., Qtr., Yr. Response Cycle

11. 11 Random Component  Erratic, unsystematic, unpredictable ‘residual’ fluctuations  Due to random variation or unforeseen events –  Short duration & nonrepeating © 1984-1994 T/Maker Co.

12. 12 Qualitative Methods Grass Roots Market Research Panel Consensus Executive Judgment Historical analogy Delphi Method Qualitative Methods

13. 13 Delphi Method l. Choose the experts to participate. There should be a variety of knowledgeable people in different areas. 2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants. 3. Summarize the results and redistribute them to the participants along with appropriate new questions. 4. Summarize again, refining forecasts and conditions, and again develop new questions. 5. Repeat Step 4 if necessary. Distribute the final results to all participants.

14. 14 Causal Models Quantitative Forecasting Methods Quantitative Forecasting Time Series Models Linear Regression Exponential Smoothing Trend Projection Moving Average

15. 15 Time Series Analysis  Time series forecasting models try to predict the future based on past data.  You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel

16. 16 Simple Moving Average Formula  The simple moving average model assumes an average is a good estimator of future behavior.  The formula for the simple moving average is: F t = Forecast for the coming period n = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods

17. 17 Forecasting Example # 1 Weekly Video Rentals

18. 18 Forecasting Video Rentals With Moving Averages  Question: What are the 2-week and 4-week moving average forecasts for video rentals?  Which forecast would you prefer?

19. Calculating the moving averages gives us: © The McGraw-Hill Companies, Inc., 2000 19

20. 20 Which Forecast Would You Prefer?

21. 21 Forecasting Example # 2 Quarterly Sales Data (Acme Tool Company)

22. 22 Forecasting Quarterly Sales With Moving Averages  Question: What are the 2-week and 4-week moving average forecasts for Quarterly Sales  Which forecast would you prefer?

23. 23 Calculating the moving averages gives us:

24. 24 Which Forecast Would You Prefer?

25. 25 Weighted Moving Average Formula While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods. w t = weight given to time period “t” occurrence. (Weights must add to one.) The formula for the weighted average is:

26. 26 Weighted Moving Average Problem (1) Data Weights: t-1.5 t-2.3 t-3.2 Question: Given the weekly demand and weights, what is the forecast for the 4 th period or Week 4? Note that these weights place more emphasis on the most recent data, that is time period “t-1”.

27. 27 Weighted Moving Average Problem (1) Solution

28. 28 Weighted Moving Average Problem (2) Data Weights: t-1.7 t-2.2 t-3.1 Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5 th period or week?

29. 29 Weighted Moving Average Problem (2) Solution

30. 30 Exponential Smoothing Model F t = F t-1 +  (A t-1 - F t-1 ) F t =  A t-1 +(1-  )F t-1 Or, Equivalently  = smoothing constant Where, F t = Forecast for period t A t = Actual value in period t Note: A higher value of  places more weight on more recent observations

31. 31 Forecasting Weekly Video Rentals With Exponential Smoothing  Question: Given the weekly video rental data, what are the exponential smoothing forecasts for periods 2-13 using  =0.10 and  =0.60?  Assume F 1 =A 1

32. 32 Calculating the Exponential smoothing forecasts gives us:

33. 33 Which Forecast Would You Prefer?

34. 34 Forecasting Quarterly Sales for the Acme Tool Company With Exponential Smoothing  Question: Given the quarterly sales data, what are the exponential smoothing forecasts for periods 2-13 using  =0.10 and  =0.60?  Assume F 1 =A 1

35. 35 Calculating the Exponential smoothing forecasts gives us:

36. 36 Which Forecast Would You Prefer?

37. 37 The MAD Statistic to Determine Forecasting Error  The ideal MAD is zero. That would mean there is no forecasting error.  The larger the MAD, the less the desirable the resulting model.  Note that by itself, MAD only lets us know the mean error in a set of forecasts.

38. 38 Weekly Video Rentals

39. 39 Quarterly Sales (Acme Tool Company)

40. 40 A Comparison of Exponential Smoothing Forecasts (Video Rentals)

41. 41 A Comparison of Exponential Smoothing Forecasts (Acme Tool)

42. 42 Tracking Signal Formula  The TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand.  The TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts.  Generally, good TS fall between -4 and +4  The TS formula is:

43. 43 Calculating Tracking Signals for the Exponential Smoothing Forecasts From the Acme Tool Company Example

44. 44 Tracking Signal Charts

45. 45 Linear Trend Projection  Used for forecasting linear trend line  Assumes relationship between response variable Y & time X is a linear function  Estimated by least squares method –Minimizes sum of squared errors

46. 46 Linear Regression Model Observed value YX YabX ii   YabX ii  Error Error Regression line

47. 47 Web-Based Forecasting: CPFR Defined  Collaborative Planning, Forecasting, and Replenishment (CPFR) a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners.  Used to integrate the multi-tier or n-Tier supply chain, including manufacturers, distributors and retailers.  CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain.  CPFR uses a cyclic and iterative approach to derive consensus forecasts.

48. 48 Web-Based Forecasting: Steps in CPFR 1. Creation of a front-end partnership agreement 2. Joint business planning 3. Development of demand forecasts 4. Sharing forecasts 5. Inventory replenishment