Chapter 15 Demand Management & Forecasting

1. Chapter 15Demand Management &Forecasting • Demand Management • Qualitative Forecasting Methods • Simple & Weighted Moving Average Forecasts • Exponential Smoothing • Simple Linear Regression

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

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. 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. 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. Types of Forecastsby 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. Types of Forecasts • Qualitative (Judgmental) • Quantitative • Time Series Analysis • Causal Relationships • Simulation

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

9. x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Finding Components of Demand Seasonal variation x Linear Trend Sales 1 2 3 4 Year

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

11. Random Component • Erratic, unsystematic, unpredictable ‘residual’ fluctuations © 1984-1994 T/Maker Co.

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

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

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. 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: Ft = 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. Forecasting Example # 1 Weekly Video Rentals

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

20. Which Forecast Would You Prefer?

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

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. Calculating the moving averages gives us:

24. Which Forecast Would You Prefer?

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. The formula for the weighted average is: wt = weight given to time period “t” occurrence. (Weights must add to one.)

26. Weighted Moving Average Problem (1) Data Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4? Weights: t-1 .5 t-2 .3 t-3 .2

27. Weighted Moving Average Problem (1) Solution

28. Weighted Moving Average Problem (2) Data Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week? Weights: t-1 .7 t-2 .2 t-3 .1

29. Weighted Moving Average Problem (2) Solution

30. Where, a = smoothing constant Ft = Forecast for period t At = Actual value in period t Exponential Smoothing Model Ft = aAt-1 +(1- )Ft-1 Or, Equivalently Ft = Ft-1 + a(At-1 - Ft-1) Note:

31. Exponential Smoothing Expansion • Ft = At - 1 + (1-)At - 2 + (1- )2·At - 3 + (1- )3At - 4 + ... + (1- )t-1·A0 • Ft = Forecast value • At = Actual value •  = Smoothing constant

32. 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 a=0.10 and a=0.60? • Assume F1=A1

33. Calculating the Exponential smoothing forecasts gives us:

34. Which Forecast Would You Prefer?

35. 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 a=0.10 and a=0.60? • Assume F1=A1

36. Calculating the Exponential smoothing forecasts gives us:

37. Which Forecast Would You Prefer?

38. Weights = Prior Period  2 periods ago (1 - ) 3 periods ago (1 - )2 = 0.10 = 0.90 Forecast Effects of Smoothing Constant  Ft =  At - 1 + (1- )At - 2 + (1- )2At - 3 + ...

39. The MAD Statistic to Determine Forecasting Error • The ideal MAD is zero. • The larger the MAD, the less the desirable the resulting model.

40. Weekly Video Rentals

41. Quarterly Sales (Acme Tool Company)

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

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

44. 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. • Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts. • The TS formula is:

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

46. Tracking Signal Charts

47. 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

48. Correlation • Answers ‘how strongis the linear relationship between 2 variables?’ • Coefficient of correlation used • Sample correlation coefficient denotedr • Values range from -1 to +1 • Measures degree of association • Used mainly for understanding

49. 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.

50. 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