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

2. Demand Management Independent Demand: Finished Goods Dependent Demand:
B(4)
C(2)
D(2)
E(1)
D(3)
F(2)
Dependent Demand:
Raw Materials,
Component parts,
Sub-assemblies, etc. 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 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. 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. Finding Components of Demand
Seasonal variation x
Linear
Trend x
Sales 1 2 3 4
Year

10. B 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
© T/Maker Co.

12. Qualitative Methods Grass Roots Qualitative Methods Market Research
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 ), 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 Methods
Time Series
Causal
Models
Models
Moving
Exponential
Trend
Linear
Regression
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. Calculating the moving averages gives us: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. Exponential Smoothing Model
Ft = aAt-1 +(1- )Ft-1
Or, Equivalently
Ft = Ft-1 + a(At-1 - Ft-1) a
= smoothing constant
Where,
Ft = Forecast for period t
At = Actual value in period t
Note:

31. Exponential Smoothing Expansion
Ft = At (1-)At (1- )2·At (1- )3At (1- )t-1·A0
Ft = Forecast value
At = Actual value
 = Smoothing constant
You may wish to discuss several points:
- this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time.
- the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point.
- we need a formal process and criteria for choosing the “best” 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. Forecast Effects of Smoothing Constant 
Ft =  At (1- )At (1- )2At
Weights
Prior Period 
2 periods ago
(1 - )
3 periods ago
(1 - )2 =
= 0.10
= 0.90

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. Linear Regression ModelY X a b i   
Error
Regression line
Observed value

49. Correlation
Answers ‘how strong is the linear relationship between 2 variables?’
Coefficient of correlation used
Sample correlation coefficient denoted r
Values range from -1 to +1
Measures degree of association
Used mainly for understanding

50. Coefficient of Correlation Values
Perfect Negative Correlation
Perfect Positive Correlation
No Correlation
-1.0
-.5
+.5
+1.0
Increasing degree of negative correlation
Increasing degree of positive correlation

51. 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. 33

52. 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 33