Learning Objectives

1
Learning Objectives
When you complete this chapter, you should be
able to Identify or Define

• Forecasting
• Types of forecasts
• Time horizons
• Approaches to forecasts

2
Learning Objectives
When you complete this chapter, you should be
able to Describe or Explain

• Moving averages
• Exponential smoothing
• Trend projections
• Regression and correlation analysis
• Measures of forecast accuracy

3
What is Forecasting?

• Process of predicting a future event

4
Forecasting Time Horizons

• Short-range forecast
• Up to 1 year, generally less than 3 months
• Purchasing, job scheduling, workforce levels, job
  assignments, production levels
• Medium-range forecast
• 3 months to 3 years
• Sales and production planning, budgeting
• Long-range forecast
• 3 years
• New product planning, facility location, research
  and development

5
Types of Forecasts

• Economic forecasts
• Address business cycle inflation rate, money
  supply, housing starts, etc.
• Technological forecasts
• Predict rate of technological progress
• Impacts development of new products
• Demand forecasts
• Predict sales of existing product

6
Strategic Importance of Forecasting

• Human Resources Hiring, training, laying off
  workers
• Capacity Capacity shortages can result in
  undependable delivery, loss of customers, loss of
  market share
• Supply-Chain Management Good supplier relations
  and price advance

7
Seven Steps in Forecasting

• Determine the use of the forecast
• Select the items to be forecasted
• Determine the time horizon of the forecast
• Select the forecasting model(s)
• Gather the data
• Make the forecast
• Validate and implement results

8
The Realities!

• Forecasts are seldom perfect
• Most techniques assume an underlying stability in
  the system
• Product family and aggregated forecasts are more
  accurate than individual product forecasts

9
Forecasting Approaches
Qualitative Methods

• Used when situation is vague and little data
  exist
• New products
• New technology
• Involves intuition, experience
• e.g., forecasting sales on Internet

10
Forecasting Approaches
Quantitative Methods

• Used when situation is stable and historical
  data exist
• Existing products
• Current technology
• Involves mathematical techniques
• e.g., forecasting sales of color televisions

11
Overview of Qualitative Methods

• Jury of executive opinion
• Pool opinions of high-level executives, sometimes
  augment by statistical models
• Delphi method
• Panel of experts, queried iteratively

12
Overview of Qualitative Methods

• Sales force composite
• Estimates from individual salespersons are
  reviewed for reasonableness, then aggregated
• Consumer Market Survey
• Ask the customer

13
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-thinkdisadvantage

14
Sales Force Composite

• Each salesperson projects his or her sales
• Combined at district and national levels
• Sales reps know customers wants
• Tends to be overly optimistic

15
Delphi Method

• Iterative group process, continues until general
  agreement is reached
• 3 types of participants
• Decision makers
• Staff
• Respondents

16
Consumer Market Survey

• Ask customers about purchasing plans
• What consumers say, and what they actually do are
  often different
• Sometimes difficult to answer

17
Overview of Quantitative Approaches

• Naive approach
• Moving averages
• Exponential smoothing
• Trend projection
• Linear regression

18
Time Series Forecasting

• Set of evenly spaced numerical data
• Obtained by observing response variable at
  regular time periods
• Forecast based only on past values
• Assumes that factors influencing past and present
  will continue influence in future

19
Time Series Components
20
Components of Demand
Figure 4.1
21
Trend Component

• Persistent, overall upward or downward pattern
• Changes due to population, technology, age,
  culture, etc.
• Typically several years duration

22
Seasonal Component

• Regular pattern of up and down fluctuations
• Due to weather, customs, etc.
• Occurs within a single year

23
Cyclical Component

• Repeating up and down movements
• Affected by business cycle, political, and
  economic factors
• Multiple years duration
• Often causal or associative relationships

24
Random Component

• Erratic, unsystematic, residual fluctuations
• Due to random variation or unforeseen events
• Short duration and nonrepeating

25
Overview of Quantitative Approaches

• Naive approach
• Moving averages
• Exponential smoothing
• Trend projection
• Linear regression

26
Naive Approach

• Assumes demand in next period is the same as
  demand in most recent period
• e.g., If May sales were 48, then June sales will
  be 48
• Sometimes cost effective and efficient

27
Overview of Quantitative Approaches

• Naive approach
• Moving averages
• Exponential smoothing
• Trend projection
• Linear regression

28
Moving Average Method

• MA is a series of arithmetic means
• Used if little or no trend
• Used often for smoothing
• Provides overall impression of data over time

29
Moving Average Example
(12 13 16)/3 13 2/3 (13
16 19)/3 16 (16 19 23)/3 19 1/3
30
Graph of Moving Average
31
Weighted Moving Average

• Used when trend is present
• Older data usually less important
• Weights based on experience and intuition

32
Weighted Moving Average
(3 x 16) (2 x 13) (12)/6
141/3 (3 x 19) (2 x 16) (13)/6 17 (3
x 23) (2 x 19) (16)/6 201/2
33
Moving Average And Weighted Moving Average
Figure 4.2
34
Overview of Quantitative Approaches

• Naive approach
• Moving averages
• Exponential smoothing
• Trend projection
• Linear regression

35
Exponential Smoothing
New forecast last periods forecast a (last
periods actual demand last periods
forecast)
Ft Ft 1 a(At 1 - Ft 1)
where Ft new forecast Ft 1 previous
forecast a smoothing (or weighting)
constant (0 ? a ? 1)
36
Exponential Smoothing Example
Predicted demand 142 Ford Mustangs Actual
demand 153 Smoothing constant a .20
37
Exponential Smoothing Example
Predicted demand 142 Ford Mustangs Actual
demand 153 Smoothing constant a .20
38
Exponential Smoothing Example
Predicted demand 142 Ford Mustangs Actual
demand 153 Smoothing constant a .20
New forecast 142 .2(153 142) 142
2.2 144.2 144 cars
39
Exponential Smoothing

• 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

40
Impact of Different ?
41
Choosing ?
The objective is to obtain the most accurate
forecast no matter the technique
We generally do this by selecting the model that
gives us the lowest forecast error
Forecast error Actual demand - Forecast
value At - Ft
42
Common Measures of Error
43
Common Measures of Error
44
Comparison of Forecast Error
45
Comparison of Forecast Error
46
Comparison of Forecast Error
47
Comparison of Forecast Error
48
Comparison of Forecast Error
49
Overview of Quantitative Approaches

• Naive approach
• Moving averages
• Exponential smoothing
• Trend projection
• Linear regression

50
Trend Projections
Fitting a trend line to historical data points to
project into the medium-to-long-range
Linear trends can be found using the least
squares technique
51
Least Squares Method
Figure 4.4
52
Least Squares Method
Least squares method minimizes the sum of the
squared errors (deviations)
Figure 4.4
53
Least Squares Method
Equations to calculate the regression variables
54
Least Squares Example
55
Least Squares Example
56
Least Squares Example
57
Least Squares Requirements

• We always plot the data to insure a linear
  relationship
• We do not predict time periods far beyond the
  database
• Deviations around the least squares line are
  assumed to be random

58
Overview of Quantitative Approaches

• Naive approach
• Moving averages
• Exponential smoothing
• Trend projection
• Linear regression

59
Associative Forecasting
Used when changes in one or more independent
variables can be used to predict the changes in
the dependent variable
Most common technique is linear regression
analysis
We apply this technique just as we did in the
time series example
60
linear regression analysis
Forecasting an outcome based on predictor
variables using the least squares technique
61
Associative Forecasting Example
62
Associative Forecasting Example
63
Associative Forecasting Example
Sales 1.75 .25(payroll)
If payroll next year is estimated to be 600
million, then
Sales 1.75 .25(6) Sales 325,000
64
Standard Error of the Estimate

• To measure the accuracy of the regression
  estimates, we must compute the standard error of
  the estimate, Sy,x.
• This computation is called the standard deviation
  of the regression. It measures the error from the
  dependent variable y, to the regression line,
  rather than to the mean.

65
Standard Error of the Estimate
where y y-value of each data point yc compute
d value of the dependent variable, from the
regression equation n number of data points
66
Standard Error of the Estimate
Computationally, this equation is considerably
easier to use
We use the standard error to set up prediction
intervals around the point estimate
67
Associative Forecasting Example
68
Standard Error of the Estimate
Sy,x .306
The standard error of the estimate is 30,600 in
sales
69
coefficient of correlation

• Regression line merely describe the relationship
  among variables.
• Another way to evaluate the relationship between
  two variable is to compute the coefficient of
  correlation.

70
Correlation

• How strong is the linear relationship between the
  variables?
• Correlation does not necessarily imply causality!
• Coefficient of correlation, r, measures degree of
  association
• Values range from -1 to 1

71
Correlation Coefficient
72
Correlation Coefficient
73
Correlation

• Coefficient of Determination, r2, measures the
  percent of change in y predicted by the change in
  x
• Values range from 0 to 1
• Easy to interpret

For the Nodel Construction example r .901 r2
.81
74
Multiple Regression Analysis
If more than one independent variable is to be
used in the model, linear regression can be
extended to multiple regression to accommodate
several independent variables
Computationally, this is quite complex and
generally done on the computer
75
Multiple Regression Analysis
In the Nodel example, including interest rates in
the model gives the new equation
An improved correlation coefficient of r .96
means this model does a better job of predicting
the change in construction sales
Sales 1.80 .30(6) - 5.0(.12) 3.00 Sales
300,000
76

• Have nice day!