1. Mr. David P. Blain. C.Q.E. Management Department UNLV
Chapter 4 - Forecasting
Mr. David P. Blain. C.Q.E.
Management Department
UNLV

2. Outline Global Company Profile: Tupperware What is Forecasting?
Types of Forecasts
Seven Steps in the Forecasting System
Forecasting Approaches
Overview of Qualitative & Quantitative Methods
Time-Series Forecasting
Monitoring and Controlling Forecasts

3. Forecasting at Tupperware
Each of 50 profit centers around the world is responsible for computerized monthly, quarterly, and 12-month sales projections
These projections are aggregated by region, then globally, at Tupperware’s World Headquarters
Tupperware uses techniques discussed in text
This slide introduces the topic of forecasting at Tupperware. The next several slides elaborate.
One might ask students several questions:
- “What does a useful forecast consist of at Tupperware?
- What problems might a company such as Tupperware experience in developing a useful forecast.
As you move through the following two slides, you could point out the application of multiple forecasting techniques to help solve some of the problems identified.

4. Three Key Factors for Tupperware
The number of registered “consultants” or sales representatives
The percentage of currently “active” dealers (this number changes each week and month)
Sales per active dealer, on a weekly basis
Ask students: “Why does the number of ‘active’ dealers change so often?” and
“If the number of ‘active’ dealers changes so often, should not this problem be addressed before attempting to forecast sales?” This question raises the issue of the impact of the distribution chain on one’s ability to forecast.

5. Tupperware - Forecast by Consensus
Although inputs come from sales, marketing, finance, and production, final forecasts are the consensus of all participating managers.
The final step is Tupperware’s version of the “jury of executive opinion”
You might take the notion of “problems” one step further and ask students why Tupperware uses a “jury of executive opinion” as part of its forecasting process.

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

7. Forecasting Time Horizons
Process of predicting a future event
Short-range forecast
Job scheduling, worker assignments
Medium-range forecast
Sales & production planning, budgeting
Long-range forecast
New product planning, facility location
At this point, it may be useful to point out the “time horizons” considered by different industries. For example, some colleges and universities look 30 to fifty years ahead, industries engaged in long distance transportation (steam ship, railroad) or provision of basic power (electrical and gas utilities, etc.) also look far ahead (20 to 100 years). Ask them to give examples of industries having much shorter long-range horizons.
Short-range forecast
Up to 1 year; usually less than 3 months
Job scheduling, worker assignments
Medium-range forecast
3 months to 3 years
Sales & production planning, budgeting
Long-range forecast
3+ years
New product planning, facility location
Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes.
Short-term forecasting usually employs different methodologies than longer-term forecasting
Short-term forecasts tend to be more accurate than longer-term forecasts.

8. Types of Forecasts Economic forecasts Technological 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
One can use an example based upon one’s college or university. Students can be asked why each of these forecast types is important to the college. Once they begin to appreciate the importance, one can then begin to discuss the problems. For example, is predicting “demand” merely as simple as predicting the number of students who will graduate from high school next year (i.e., a simple counting exercise)?

9. Forecasting Approaches
Qualitative Methods
Quantitative Methods
Used when situation is vague & little data exist
New products
New technology
Involves intuition, experience
e.g., forecasting sales on Internet
Used when situation is stable & historical data exist
Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color televisions
This slide distinguishes between Quantitative and Qualitative forecasting. If you accept the argument that the future is one of perpetual, and perhaps significant change, you may wish to ask students to consider whether quantitative forecasting will ever be sufficient in the future - or will we always need to employ qualitative forecasting also. (Consider Tupperware’s ‘jury of executive opinion.’)

10. Overview of Qualitative Methods
Jury of executive opinion
Pool opinions of high-level executives, sometimes augment by statistical models
Sales force composite
Estimates from individual salespersons are reviewed for reasonableness, then aggregated
Delphi method
Panel of experts, queried iteratively
Consumer Market Survey
Ask the customer
This slide outlines several qualitative methods of forecasting. Ask students to give examples of occasions when each might be appropriate.
Jury of Executive Opinion
Involves small group of high-level managers
Group estimates demand by working together
Combines managerial experience with statistical models
Relatively quick
‘Group-think’ disadvantage
Sales Force Composite
Each salesperson projects their sales
Combined at district & national levels
Sales rep’s know customers’ wants
Tends to be overly optimistic
Delphi Method
Iterative group process
3 types of people
Decision makers
Staff
Respondents
Reduces ‘group-think’
Consumer Market Survey
Ask customers about purchasing plans
What consumers say, and what they actually do are often different
Sometimes difficult to answer

11. Overview of Quantitative Approaches
Naïve approach
Moving averages
Exponential smoothing
Trend projection
Linear regression
Time-series models
Associative models

12. Time Series Forecasting
Trend
Seasonal
Cyclical
Random
Trend Component
Persistent, overall upward or downward pattern
Due to population, technology etc.
Several years duration
Seasonal Component
Regular pattern of up & down fluctuations
Due to weather, customs etc.
Occurs within 1 year
Cyclical Component
Repeating up & down movements
Due to interactions of factors influencing economy
Usually 2-10 years duration
Random Component
Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen events
Short duration & nonrepeating

13. Naive Approach
Assumes demand in next period is the same as demand in most recent period
If May sales were 48, then June sales will be 48
Sometimes can be cost effective & efficient
This slide introduces the naïve approach. Subsequent slides introduce other methodologies.

14.  Moving Average Method MA MA is a series of arithmetic means
Used if little or no trend
Used often for smoothing
Provides overall impression of data over time
Equation
At this point, you might discuss the impact of the number of periods included in the calculation. The more periods you include, the closer you come to the overall average; the fewer, the closer you come to the value in the previous period. What is the tradeoff? MA n  
Demand in
previous
periods

15. Weighted Moving Average Method
Used when trend is present
Older data usually less important
Weights based on intuition
Ranges between 0 & 1, & sum to 1.0
Equation
This slide introduces the “weighted moving average” method. It is probably most important to discuss choice of the weights.
Disadvantages of Moving Average Methods
Increasing n makes forecast less sensitive to changes
Do not forecast trend well
Require much historical data
Σ(Weight for period n) (Demand in period n)
WMA =
ΣWeights

16. Exponential Smoothing Method
Form of weighted moving average
Weights decline exponentially
Most recent data weighted most
Requires smoothing constant ()
Ranges from 0 to 1
Subjectively chosen
Involves little record keeping of past data
This slide introduces the exponential smoothing method of time series forecasting. The following slide contains the equations, and an example follows.

17. Exponential Smoothing Equations
Ft = Ft-1 + (At-1 - Ft-1)
Use for computing forecast
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 (based on lowest error).

18. Trend & Linear Regression Model
Shows linear relationship between dependent & explanatory variables
Example: Sales & advertising (not time)
Y-intercept
Slope ^ Y = a + b X
This slide introduces the linear regression model. This can be approached as simply a generalization of the linear trend model where the variable is something other than time and the values do not necessarily occur a t equal intervals. i i
Dependent (response) variable
Independent (explanatory) variable

19. Linear Regression ModelY a Y b X i = 
Error +  + i
Error
Regression line ^ Y = a  b X + i i X
Observed value

20. Linear Regression Equations
Slope:
Again, this is basically a repeat of the slide for the linear trend problem.
Y-Intercept:

21. Interpretation of Coefficients
Slope (b)
Estimated Y changes by b for each 1 unit increase in X
If b = 2, then sales (Y) is expected to increase by 2 for each 1 unit increase in advertising (X)
Y-intercept (a)
Average value of Y when X = 0
If a = 4, then average sales (Y) is expected to be 4 when advertising (X) is 0
This slide probably merits discussion - additional to that for the linear trend model.
You might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example.
You might also wish to note that setting x = 0 may not have a useful physical interpretation.

22. Selecting a Forecasting Model
You want to achieve:
No pattern or direction in forecast error
Error = (Yi - Yi) = (Actual - Forecast)
Seen in plots of errors over time
Smallest forecast error
Mean square error (MSE)
Mean absolute deviation (MAD)
This slide introduces overall guideline for selecting a forecasting model. You may also wish to re-emphasize the role of scatter plots, and discuss the role of “understanding what is going on” (especially in limiting one’s choice of model).

23. Forecast Error Equations
Mean Square Error (MSE)
Mean Absolute Deviation (MAD)
This slide illustrates the equations for two measures of forecast error. Students might be asked if there is an occasion when one method might be preferred over the other.

24. Tracking Signal
Measures how well the forecast is predicting actual values
Ratio of running sum of forecast errors (RSFE) to mean absolute deviation (MAD)
Good tracking signal has low values
Should be within upper and lower control limits

25. Chap 4 Forecasting Learning Objectives
Ability to Identify or Define:
Forecasting and types of forecasts
Time horizons
Approaches to forecasts
Ability to Describe or Explain:
Moving averages
Exponential smoothing
Trend projections and regression
Measures of forecast accuracy
Tracking signal

26. Ch 4 Forecasting
4.6, 4.7

27. Ch 4 Forecasting Problem 4.6 Forecasting methods
Monthly Sales for Telco Batteries were as follows:
Month Sales (y)
January 20
February 21
March 15
April 14
May 13
June 16
July 17
August 16
September 20
October 20
November 21
December 23
Period (x) x xy
1 1 20
2 4 42
3 9 45
Sum
Average

28. Ch 4 Forecasting
P 4.6 a) Plotting this data:

29. Ch 4 Forecasting
P 4.6 b) Forecast January Sales using each of the following :
1) Naive Method
Demand in next period same as last period
Therefore January will be 23 since those were December’s sales
2) 3 month Moving Average
Using the last 3 months Oct, Nov, Dec = ( )/3 =21 .33
We would forecast January at 21 based on smoothing of 3 month average

30. Ch 4 Forecasting 3) 6-month weighted average
Using weighting of .1, .1, .1, .2, .2 and .3;
with the heavier weights applied to the most recent months
( Oct, Nov, Dec)
(0.1 * 17) + (0.1 * 18) + (0.1 * 20) + (0.2 * 20) + ( 0.2 * 21) + (0.3 * 23) = which gives us a January forecast of 21

31. 4) Exponential smoothing
Ch 4 Forecasting
4) Exponential smoothing
sophisticated weighted moving average forecasting method
New forecast = last periods forecast + alpha *
(last period's actual demand - last period's forecast)
Therefore with alpha of .3 and September’s forecast of 18
October forecast = Sept forecast + alpha times
(Sept. actual - Sept. forecast)
October forecast = (20-18) = 18.6
November forecast = ( ) = 19.02
December forecast = ( ) = 19.6
January forecast = ( ) = 20.6

32. Ch 4 Forecasting
5) Trend projection is a forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasting:
Ŷ = a + b*x (equation of the line)
Calculate slope and y-intecept
b = ∑xy – n x y a = y – bx
∑x² - nx²
∑ = summation sign
Ŷ = (“y hat”) computed value of the variable to be predicted
(dependant variable)
a = y-axis intercept
b = slope of regression line (rate of change in y given change in x)
x = the independent variable
X = known values of the independent variable
Y = known values of the dependant variable
x = average of value of x’s
y = average of value of y’s
n = number of data points or observations

33. Ch 4 Forecasting 5) Trend projection: X = 6.5 ∑ y = 218 Ŷ = 18.2
Based on earlier data supplied: ∑ x = 78
X = 6.5
∑ y = 218
Ŷ = 18.2
Calculate slope
b = ∑xy – n x y
∑x² - nx²
b = 1474-(12*6.5*18.2) = = 0.38
650 – (12*(6.5)2 )
and y-intercept
a = y – bx
a = 18.2 – (0.38*6.5) = 15.73
Forecast is Ŷ = a + b*x
= ( .38*13) = , where January is 13th month

34. Ch 4 Forecasting Mkt VP Oper VP
P Doug Moodie is the president of Garden Products Limited. Over the last 5 tears, he has asked both his vice president of marketing and his vice president of operations to provide sales forecasts. The actual sales and the forecasts are given below.
Mkt VP Oper VP
Error Error
2,675 7,325
5, ,322
7,464 2,536
23, , , ,325
Totals 50, ,816
VP VP
YEAR SALES Marketing Operations
1 167, , ,000
2 167, , ,000
3 167, , ,000
4 167, , ,000
5 176, , ,000
Using MAD which vice president is better at forecasting?
MAD VP Marketing = 50,094 /5 = 10,019
MAD VP Operations = 49,816 /5 =
Therefore, based on past data,
the VP of operations has been presenting better forecasts.