Chapter 4-Forecasting Operations management

None

1. 4 - 1 © 2014 Pearson Education, Inc.
Forecasting
PowerPoint presentation to accompany
Heizer and Render
Operations Management, Eleventh Edition
Principles of Operations Management, Ninth Edition

PowerPoint slides by Jeff Heyl
4
© 2014 Pearson Education, Inc.

2. 4 - 2 © 2014 Pearson Education, Inc.
What is Forecasting?
►Process of predicting a
future event
►Underlying basis
of all business
decisions
►Production
►Inventory
►Personnel
►Facilities
??

3. 4 - 3 © 2014 Pearson Education, Inc.
1.Short-range forecast
►Up to 1 year, generally less than 3 months
►Purchasing, job scheduling, workforce levels,
job assignments, production levels
2.Medium-range forecast
►3 months to 3 years
►Sales and production planning, budgeting
3.Long-range forecast
►3
+
years
►New product planning, facility location,
research and development
Forecasting Time Horizons

4. 4 - 4 © 2014 Pearson Education, Inc.
Types of Forecasts
1.Economic forecasts
►Address business cycle – inflation rate, money
supply, housing starts, etc.
2.Technological forecasts
►Predict rate of technological progress
►Impacts development of new products
3.Demand forecasts
►Predict sales of existing products and services

5. 4 - 5 © 2014 Pearson Education, Inc.
Seven Steps in Forecasting
1.Determine the use of the forecast
2.Select the items to be forecasted
3.Determine the time horizon of the
forecast
4.Select the forecasting model(s)
5.Gather the data needed to make the
forecast
6.Make the forecast
7.Validate and implement results

6. 4 - 6 © 2014 Pearson Education, Inc.
Forecasting Approaches
►Used when situation is vague and
little data exist
►New products
►New technology
►Involves intuition, experience
►e.g., forecasting sales on Internet
Qualitative Methods

7. 4 - 7 © 2014 Pearson Education, Inc.
Forecasting Approaches
►Used when situation is ‘stable’ and
historical data exist
►Existing products
►Current technology
►Involves mathematical techniques
►e.g., forecasting sales of color
televisions
Quantitative Methods

8. 4 - 8 © 2014 Pearson Education, Inc.
Overview of Qualitative Methods
1.Jury of executive opinion
►Pool opinions of high-level experts,
sometimes augment by statistical
models
2.Delphi method
►Panel of experts, queried iteratively

9. 4 - 9 © 2014 Pearson Education, Inc.
Overview of Qualitative Methods
3.Sales force composite
►Estimates from individual salespersons
are reviewed for reasonableness, then
aggregated
4.Market Survey
►Ask the customer

10. 4 - 10 © 2014 Pearson Education, Inc.
►Involves small group of high-level experts
and managers
►Group estimates demand by working
together
►Combines managerial experience with
statistical models
►Relatively quick
►‘Group-think’
disadvantage
Jury of Executive Opinion

11. 4 - 11 © 2014 Pearson Education, Inc.
Delphi Method
►Iterative group
process, continues
until consensus is
reached
►3 types of
participants
►Decision makers
►Staff
►Respondents
Staff
(Administering
survey)
Decision Makers
(Evaluate responses
and make decisions)
Respondents
(People who can make
valuable judgments)

12. 4 - 12 © 2014 Pearson Education, Inc.
Sales Force Composite
►Each salesperson projects his or her
sales
►Combined at district and national
levels
►Sales reps know customers’ wants
►May be overly optimistic

13. 4 - 13 © 2014 Pearson Education, Inc.
Market Survey
►Ask customers about purchasing
plans
►Useful for demand and product
design and planning
►What consumers say, and what they
actually do may be different
►May be overly optimistic

14. 4 - 14 © 2014 Pearson Education, Inc.
Overview of Quantitative
Approaches

1.Moving averages
2.Exponential
smoothing
3.Linear regression

15. 4 - 15 © 2014 Pearson Education, Inc.
►Set of evenly spaced numerical data
►Obtained by observing response
variable at regular time periods
►Forecast based only on past values, no
other variables important
►Assumes that factors influencing past
and present will continue influence in
future
Time-Series Forecasting

16. 4 - 16 © 2014 Pearson Education, Inc.
►MA is a series of arithmetic means
►Used if little or no trend
►Used often for smoothing
►Provides overall impression of data
over time
Moving Average Method Moving average=
demand in previous n periodså
n

17. 4 - 17 © 2014 Pearson Education, Inc.
►Used when some trend might be
present
►Older data usually less important
►Weights based on experience and
intuition
Weighted Moving Average =
Weight for period n( )Demand in period n( )( )å
Weightså
Weighted
moving
average

18. 4 - 18 © 2014 Pearson Education, Inc.
►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
Exponential Smoothing

19. 4 - 19 © 2014 Pearson Education, Inc.
Exponential Smoothing
New forecast = Last period’s forecast
+  (Last period’s actual demand
– Last period’s forecast)
F
t = F
t – 1 + (A
t – 1 - F
t – 1)
where F
t = new forecast
F
t – 1 = previous period’s forecast
 = smoothing (or weighting) constant (0 ≤  ≤ 1)
A
t – 1 = previous period’s actual demand

20. 4 - 20 © 2014 Pearson Education, Inc.
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
= A
t – F
t

21. 4 - 21 © 2014 Pearson Education, Inc.
Least Squares Method
Figure 4.4
Deviation
1
(error)
Deviation
5
Deviation
7
Deviation
2
Deviation
6
Deviation
4
Deviation
3
Actual observation
(y-value)
Trend line, y = a + bx
^
Time period
Values of Dependent Variable (
y
-
values)

| | | | | | |
1 2 3 4 5 6 7
Least squares method minimizes the
sum of the squared errors (deviations)

22. 4 - 22 © 2014 Pearson Education, Inc.
Least Squares Method
Equations to calculate the regression variables ˆy=a+bx b=
xy-nxyå
x
2
-nx
2
å a=y-bx

23. 4 - 23 © 2014 Pearson Education, Inc.
Least Squares Requirements
1.We always plot the data to insure a
linear relationship
2.We do not predict time periods far
beyond the database
3.Deviations around the least squares
line are assumed to be random

24. 4 - 24 © 2014 Pearson Education, Inc.
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

25. 4 - 25 © 2014 Pearson Education, Inc.
Associative Forecasting
Forecasting an outcome based on predictor
variables using the least squares technique
y = a + bx
^
where y = value of the dependent variable (in our example,
sales)
a = y-axis intercept
b = slope of the regression line
x = the independent variable
^

26. 4 - 26 © 2014 Pearson Education, Inc.
►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
Correlation

27. 4 - 27 © 2014 Pearson Education, Inc.
Correlation Coefficient r=
nxy-xyååå
nx
2
-xå()
2
å
é
ë
ê
ù
û
ú
ny
2
-yå()
2
å
é
ë
ê
ù
û
ú

28. 4 - 28 © 2014 Pearson Education, Inc.
Correlation Coefficient
y
x
(a) Perfect negative
correlation
y
x
(c) No correlation
y
x
(d) Positive correlation
y
x
(e) Perfect positive
correlation
y
x
(b) Negative correlation
High Moderate Low
Correlation coefficient values
High Moderate Low
| | | | | | | | |
–1.0 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1.0

Figure 4.10

29. 4 - 29 © 2014 Pearson Education, Inc.
Correlation Coefficient r=
(6)(51.5)–(18)(15.0)
(6)(80)–(18)
2é
ë
ù
û
(16)(39.5)–(15.0)
2é
ë
ù
û
y x x
2
xy y
2

2.0 1 1 2.0 4.0
3.0 3 9 9.0 9.0
2.5 4 16 10.0 6.25
2.0 2 4 4.0 4.0
2.0 1 1 2.0 4.0
3.5 7 49 24.5 12.25
Σy = 15.0 Σx = 18 Σx
2
= 80 Σxy = 51.5 Σy
2
= 39.5 =
309-270
(156)(12)
=
39
1,872
=
39
43.3
=.901

30. 4 - 30 © 2014 Pearson Education, Inc.
►Coefficient of Determination, r
2
,
measures the percent of change in y
predicted by the change in x
►Values range from 0 to 1
►Easy to interpret
Correlation
For the Nodel Construction example:
r = .901
r
2
= .81