1. Forecasting is an Integral Part of Business Planning
Inputs:
Market,
Economic,
Other
Demand
Estimates
Forecast
Method(s)
Sales
Forecast
Management
Team
Business
Strategy
Production Resource
Forecasts

2. Examples of Production Resource Forecasts
Forecast Horizon
Time Span
Item Being Forecast
Units of Measure
Long-Range
Years
Product lines
Factory capacities
Planning for new products
Capital expenditures
Facility location or expansion
R&D
Dollars, tons, etc.
Medium-Range
Months
Product groups
Department capacities
Sales planning
Production planning and budgeting
Short-Range
Weeks
Specific product quantities
Machine capacities
Planning
Purchasing
Scheduling
Workforce levels
Production levels
Job assignments
Physical units of products 12

3. Qualitative Approaches Quantitative Approaches
Forecasting Methods
Qualitative Approaches
Quantitative Approaches

4. Qualitative Approaches
Usually based on judgments about causal factors that underlie the demand of particular products or services
Do not require a demand history for the product or service, therefore are useful for new products/services
Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events

5. Qualitative Methods Executive committee consensus Delphi method
Survey of sales force
Survey of customers
Historical analogy
Market research

6. Quantitative Forecasting Approaches
Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself
Analysis of the past demand pattern provides a good basis for forecasting future demand
Majority of quantitative approaches fall in the category of time series analysis

7. Quantitative Forecasting Applications Small and Large Firms
Technique
Low Sales (less than $100M)
High Sales (more than $500M)
Moving Average
29.6%
29.2
Simple Linear Regression
14.8%
14.6
Naive
18.5%
Single Exponential Smoothing
20.8
Multiple Regression
22.2%
27.1
Simulation
3.7%
10.4
Classical Decomposition
8.3
Box-Jenkins
6.3
Number of Firms 27 48
Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp
Note: More than one response was permitted. 12

8. Time Series Analysis
A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand
Analysis of the time series identifies patterns
Once the patterns are identified, they can be used to develop a forecast

9. Components of Time Series
What’s going on here? x x
Sales 1 2 3 4
Year 6

10. Components of Time Series
Trends are noted by an upward or downward sloping line
Seasonality is a data pattern that repeats itself over the period of one year or less
Cycle is a data pattern that repeats itself... may take years
Irregular variations are jumps in the level of the series due to extraordinary events
Random fluctuation from random variation or unexplained causes

11. Actual Data & the Regression Line

12. Seasonality Length of Time Number of Before Pattern Length of Seasons
Is Repeated Season in Pattern
Year Quarter 4
Year Month 12
Year Week 52
Month Week 4
Month Day
Week Day 7

13. Eight Steps to Forecasting
Determining the use of the forecast--what are the objectives?
Select the items to be forecast
Determine the time horizon of the forecast
Select the forecasting model(s)
Collect the data
Validate the forecasting model
Make the forecast
Implement the results

14. Quantitative Forecasting Approaches
Linear Regression
Simple Moving Average
Weighted Moving Average
Exponential Smoothing (exponentially weighted moving average)
Exponential Smoothing with Trend (double smoothing)

15. Simple Linear Regression
Relationship between one independent variable, X, and a dependent variable, Y.
Assumed to be linear (a straight line)
Form: Y = a + bX
Y = dependent variable
X = independent variable
a = y-axis intercept
b = slope of regression line

16. Simple Linear Regression Model
Yt = a + bx Y
x (weeks)
b is similar to the slope. However, since it is calculated with the variability of the data in mind, its formulation is not as straight-forward as our usual notion of slope 35

17. Calculating a and b 36

18. Regression Equation Example
Develop a regression equation to predict sales
based on these five points. 37

19. Regression Equation Example
Slide 18 of 55 38

20. y = 143.5 + 6.3t Regression Equation Example 180 175 170 165 160 Sales
155
Sales
Forecast
150
145
140
135
Period 1 2 3 4 5
Slide 19 of 55 39

21. Forecast Accuracy
Accuracy is the typical criterion for judging the performance of a forecasting approach
Accuracy is how well the forecasted values match the actual values

22. Monitoring Accuracy
Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach
Accuracy can be measured in several ways
Mean absolute deviation (MAD)
Mean squared error (MSE)

23. Mean Absolute Deviation (MAD)

24. Mean Squared Error (MSE)
MSE = (Syx)2
Small value for Syx means data points tightly grouped around the line and error range is small. The smaller the standard error the more accurate the forecast.
MSE = 1.25(MAD)
When the forecast errors are normally distributed

25. Example--MAD Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300
320 5
325
315
Determine the MAD for the four forecast periods 31

26. Solution Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210
205 4
300
320 20
325
315 10 40 32

27. Simple Moving Average An averaging period (AP) is given or selected
The forecast for the next period is the arithmetic average of the AP most recent actual demands
It is called a “simple” average because each period used to compute the average is equally weighted
. . . more

28. Simple Moving Average
It is called “moving” because as new demand data becomes available, the oldest data is not used
By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response)
By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response)

29. Simple Moving Average
Let’s develop 3-week and 6-week moving average forecasts for demand.
Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts 15

30. Simple Moving Average
Slide 29 of 55 16

31. Simple Moving Average
Slide 30 of 55 17

32. Weighted Moving Average
This is a variation on the simple moving average where instead of the weights used to compute the average being equal, they are not equal
This allows more recent demand data to have a greater effect on the moving average, therefore the forecast
. . . more

33. Weighted Moving Average
The weights must add to 1.0 and generally decrease in value with the age of the data
The distribution of the weights determine impulse response of the forecast

34. Weighted Moving Average
Determine the 3-period weighted moving average forecast for period 4
Weights (adding up to 1.0):
t-1: .5
t-2: .3
t-3: .2 20

35. Solution 21

36. Exponential Smoothing
The weights used to compute the forecast (moving average) are exponentially distributed
The forecast is the sum of the old forecast and a portion of the forecast error
Ft = Ft-1 + a(At-1 - Ft-1)
. . . more

37. Exponential Smoothing
The smoothing constant, , must be between 0.0 and 1.0 (excluding 0.0 and 1.0)
A large  provides a high impulse response forecast
A small  provides a low impulse response forecast

38. Exponential Smoothing Example
Determine exponential smoothing forecasts for periods 2 through 10 using =.10 and =.60.
Let F1=D1 25

39. Exponential Smoothing Example
Slide 38 of 55 26

40. Effect of  on Forecast 27

41. Criteria for Selecting a Forecasting Method
Cost
Accuracy
Data available
Time span
Nature of products and services
Impulse response and noise dampening

42. Reasons for Ineffective Forecasting
Not involving a broad cross section of people
Not recognizing that forecasting is integral to business planning
Not recognizing that forecasts will always be wrong (think in terms of interval rather than point forecasts)
Not forecasting the right things (forecast independent demand only)
Not selecting an appropriate forecasting method (use MAD to evaluate goodness of fit)
Not tracking the accuracy of the forecasting models

43. How to Monitor and Control a Forecasting Model
Tracking Signal
Tracking signal = =

44. Tracking Signal: What do you notice?34

45. Sources of Forecasting Data
Consumer Confidence Index
Consumer Price Index
Housing Starts
Index of Leading Economic Indicators
Personal Income and Consumption
Producer Price Index
Purchasing Manager’s Index
Retail Sales