OPM101Chapter.ppt

© Wiley 2010 1
Chapter 8 - Forecasting
Operations Management
by
R. Dan Reid & Nada R. Sanders
4th Edition © Wiley 2010

© Wiley 2010 2
Learning Objectives
 Identify Principles of Forecasting
 Explain the steps in the forecasting
process
 Identify types of forecasting methods
and their characteristics
 Describe time series and causal models

© Wiley 2010 3
Learning Objectives con’t
 Generate forecasts for data with
different patterns: level, trend,
seasonality, and cyclical
 Describe causal modeling using linear
regression
 Compute forecast accuracy
 Explain how forecasting models should
be selected

© Wiley 2010 4
Principles of Forecasting
Many types of forecasting models that
differ in complexity and amount of
data & way they generate forecasts:
1. Forecasts are rarely perfect
2. Forecasts are more accurate for
grouped data than for individual items
3. Forecast are more accurate for shorter
than longer time periods

© Wiley 2010 5
Types of Forecasting Methods
 Decide what needs to be forecast
 Level of detail, units of analysis & time horizon
required
 Evaluate and analyze appropriate data
 Identify needed data & whether it’s available
 Select and test the forecasting model
 Cost, ease of use & accuracy
 Generate the forecast
 Monitor forecast accuracy over time

© Wiley 2010 6
Types of Forecasting Methods
 Forecasting methods are classified into
two groups:

© Wiley 2010 7
Types of Forecasting Models
 Qualitative methods – judgmental methods
 Forecasts generated subjectively by the
forecaster
 Educated guesses
 Quantitative methods – based on
mathematical modeling:
 Forecasts generated through mathematical
modeling

© Wiley 2010 8
Qualitative Methods
Type Characteristics Strengths Weaknesses
Executive
opinion
A group of managers
meet & come up with
a forecast
Good for strategic or
new-product
forecasting
One person's opinion
can dominate the
forecast
Market
research
Uses surveys &
interviews to identify
customer preferences
Good determinant of
customer preferences
It can be difficult to
develop a good
questionnaire
Delphi
method
Seeks to develop a
consensus among a
group of experts
Excellent for
forecasting long-term
product demand,
technological
changes, and
Time consuming to
develop

© Wiley 2010 9
Quantitative Methods
 Time Series Models:
 Assumes information needed to generate a
forecast is contained in a time series of data
 Assumes the future will follow same patterns as
the past
 Causal Models or Associative Models
 Explores cause-and-effect relationships
 Uses leading indicators to predict the future
 Housing starts and appliance sales

© Wiley 2010 10
Time Series Models
 Forecaster looks for data patterns as
 Data = historic pattern + random variation
 Historic pattern to be forecasted:
 Level (long-term average) – data fluctuates around a constant
mean
 Trend – data exhibits an increasing or decreasing pattern
 Seasonality – any pattern that regularly repeats itself and is of a
constant length
 Cycle – patterns created by economic fluctuations
 Random Variation cannot be predicted

© Wiley 2010 11
Time Series Patterns

© Wiley 2010 12
Time Series Models
 Naive:
 The forecast is equal to the actual value observed during
the last period – good for level patterns
 Simple Mean:
 The average of all available data - good for level
patterns
 Moving Average:
 The average value over a set time period
(e.g.: the last four weeks)
 Each new forecast drops the oldest data point & adds a
new observation
 More responsive to a trend but still lags behind actual
data
t
A

1
t
F
n
/
A
F t
1
t 


n
/
A
F t
1
t 



© Wiley 2010 13
Time Series Models con’t
 Weighted Moving Average:
 All weights must add to 100% or 1.00
e.g. Ct .5, Ct-1 .3, Ct-2 .2 (weights add to 1.0)
 Allows emphasizing one period over others; above
indicates more weight on recent data (Ct=.5)
 Differs from the simple moving average that weighs
all periods equally - more responsive to trends


 t
t
1
t A
C
F

© Wiley 2010 14
Time Series Models con’t
 Exponential Smoothing:
Most frequently used time series method because of
ease of use and minimal amount of data needed
 Need just three pieces of data to start:
 Last period’s forecast (Ft)
 Last periods actual value (At)
 Select value of smoothing coefficient, ,between 0 and 1.0
 If no last period forecast is available, average the
last few periods or use naive method
 Higher values (e.g. .7 or .8) may place too much
weight on last period’s random variation
  t
t
1
t F
α
1
αA
F 







© Wiley 2010 15
Time Series Problem
 Determine forecast for
periods 7 & 8
 2-period moving average
 4-period moving average
 2-period weighted moving
average with t-1 weighted 0.6
and t-2 weighted 0.4
 Exponential smoothing with
alpha=0.2 and the period 6
forecast being 375
Period Actual
1 300
2 315
3 290
4 345
5 320
6 360
7 375
8

© Wiley 2010 16
Time Series Problem Solution
Period Actual 2-Period 4-Period 2-Per.Wgted. Expon. Smooth.
1 300
2 315
3 290
4 345
5 320
6 360
7 375 340.0 328.8 344.0 372.0
8 367.5 350.0 369.0 372.6

© Wiley 2010 17
Forecasting trend problem: a company uses exponential smoothing with
trend to forecast usage of its lawn care products. At the end of July the
company wishes to forecast sales for August. July demand was 62. The
trend through June has been 15 additional gallons of product sold per
month. Average sales have been 57 gallons per month. The company uses
alpha+0.2 and beta +0.10. Forecast for August.
 Smooth the level of the series:
 Smooth the trend:
 Forecast including trend:
      14.8
15
0.9
57
70
0.1
β)T
(1
)
S
β(S
T 1
t
1
t
t
July 






 

      70
15
57
0.8
62
0.2
)
T
α)(S
(1
αA
S 1
t
1
t
t
July 






 

gallons
84.8
14.8
70
T
S
FIT t
t
August 





© Wiley 2010 18
Linear Trend Line
A time series technique that computes a
forecast with trend by drawing a straight line
through a set of data using this formula:
Y = a + bx where
Y = forecast for period X
X = the number of time periods from X = 0
A = value of y at X = 0 (Y intercept)
B = slope of the line

© Wiley 2010 19
Forecasting Trend
 Basic forecasting models for trends compensate for the lagging
that would otherwise occur
 One model, trend-adjusted exponential smoothing uses a
three step process
 Step 1 - Smoothing the level of the series
 Step 2 – Smoothing the trend
 Forecast including the trend
)
T
α)(S
(1
αA
S 1
t
1
t
t
t 
 



1
t
1
t
t
t β)T
(1
)
S
β(S
T 
 



t
t
1
t T
S
FIT 



© Wiley 2010 20
Forecasting Seasonality
 Calculate the average demand per season
 E.g.: average quarterly demand
 Calculate a seasonal index for each season of
each year:
 Divide the actual demand of each season by the
average demand per season for that year
 Average the indexes by season
 E.g.: take the average of all Spring indexes, then
of all Summer indexes, ...

© Wiley 2010 21
Seasonality con’t
 Forecast demand for the next year & divide
by the number of seasons
 Use regular forecasting method & divide by four
for average quarterly demand
 Multiply next year’s average seasonal demand
by each average seasonal index
 Result is a forecast of demand for each season of
next year

© Wiley 2010 22
Seasonality problem: a university must develop forecasts for the
next year’s quarterly enrollments. It has collected quarterly
enrollments for the past two years. It has also forecast total
enrollment for next year to be 90,000 students. What is the
forecast for each quarter of next year?
Quarter Year 1 Seasonal
Index
Year
2
Seasonal
Index
Avg.
Index
Year3
Fall 24000 1.2 26000 1.238 1.22 27450
Winter 23000 22000
Spring 19000 19000
Summer 14000 17000
Total 80000 84000 90000
Average 20000 21000 22500

© Wiley 2010 23
Causal Models
 Often, leading indicators can help to predict
changes in future demand e.g. housing starts
 Causal models establish a cause-and-effect
relationship between independent and dependent
variables
 A common tool of causal modeling is linear
regression:
 Additional related variables may require multiple
regression modeling
bx
a
Y 


© Wiley 2010 24
Linear Regression
 
 
 
 
 
 



X
X
X
Y
X
XY
b
2
 Identify dependent (y) and
independent (x) variables
 Solve for the slope of the
line
 Solve for the y intercept
 Develop your equation for
the trend line
Y=a + bX
X
b
Y
a 





 2
2
X
n
X
Y
X
n
XY
b

© Wiley 2010 25
Linear Regression Problem: A maker of golf shirts has been
tracking the relationship between sales and advertising dollars. Use
linear regression to find out what sales might be if the company
invested $53,000 in advertising next year.




 2
2
X
n
X
Y
X
n
XY
b
Sales $
(Y)
Adv.$
(X)
XY X^2 Y^2
1 130 32 4160 2304 16,900
2 151 52 7852 2704 22,801
3 150 50 7500 2500 22,500
4 158 55 8690 3025 24964
5 153.85 53
Tot 589 189 28202 9253 87165
Avg 147.25 47.25
  
 
 
  153.85
53
1.15
92.9
Y
1.15X
92.9
bX
a
Y
92.9
a
47.25
1.15
147.25
X
b
Y
a
1.15
47.25
4
9253
147.25
47.25
4
28202
b 2

















© Wiley 2010 26
Correlation Coefficient
How Good is the Fit?
 Correlation coefficient (r) measures the direction and strength of the linear
relationship between two variables. The closer the r value is to 1.0 the better
the regression line fits the data points.
 Coefficient of determination ( ) measures the amount of variation in the
dependent variable about its mean that is explained by the regression line.
Values of ( ) close to 1.0 are desirable.
    
       
   
   
  .964
.982
r
.982
589
87,165
4
*
(189)
-
4(9253)
589
189
28,202
4
r
Y
Y
n
*
X
X
n
Y
X
XY
n
r
2
2
2
2
2
2
2
2
















2
r
2
r

© Wiley 2010 27
Multiple Regression
 An extension of linear regression but:
 Multiple regression develops a relationship
between a dependent variable and multiple
independent variables. The general
formula is:

© Wiley 2010 28
Measuring Forecast Error
 Forecasts are never perfect
 Need to know how much we should
rely on our chosen forecasting method
 Measuring forecast error:
 Note that over-forecasts = negative
errors and under-forecasts = positive
errors
t
t
t F
A
E 


© Wiley 2010 29
Measuring Forecasting Accuracy
 Mean Absolute Deviation (MAD)
 measures the total error in a
forecast without regard to sign
 Cumulative Forecast Error (CFE)
 Measures any bias in the forecast
 Mean Square Error (MSE)
 Penalizes larger errors
 Tracking Signal
 Measures if your model is working
 
n
forecast
-
actual
MSE
2


MAD
CFE
TS 
n
forecast
actual
MAD
 

 
 
 forecast
actual
CFE

© Wiley 2010 30
Accuracy & Tracking Signal Problem: A company is comparing the
accuracy of two forecasting methods. Forecasts using both methods are
shown below along with the actual values for January through May. The
company also uses a tracking signal with ±4 limits to decide when a
forecast should be reviewed. Which forecasting method is best?
Month Actual
sales
Method A Method B
F’cast Error Cum.
Error
Tracking
Signal
F’cast Error Cum.
Error
Tracking
Signal
Jan. 30 28 2 2 2 27 2 2 1
Feb. 26 25 1 3 3 25 1 3 1.5
March 32 32 0 3 3 29 3 6 3
April 29 30 -1 2 2 27 2 8 4
May 31 30 1 3 3 29 2 10 5
MAD 1 2
MSE 1.4 4.4

© Wiley 2010 31
Selecting the Right Forecasting Model
1. The amount & type of available data
 Some methods require more data than others
2. Degree of accuracy required
 Increasing accuracy means more data
3. Length of forecast horizon
 Different models for 3 month vs. 10 years
4. Presence of data patterns
 Lagging will occur when a forecasting model
meant for a level pattern is applied with a trend

© Wiley 2010 32
Forecasting Software
 Spreadsheets
 Microsoft Excel, Quattro Pro, Lotus 1-2-3
 Limited statistical analysis of forecast data
 Statistical packages
 SPSS, SAS, NCSS, Minitab
 Forecasting plus statistical and graphics
 Specialty forecasting packages
 Forecast Master, Forecast Pro, Autobox, SCA

© Wiley 2010 33
Guidelines for Selecting Software
 Does the package have the features you want?
 What platform is the package available for?
 How easy is the package to learn and use?
 Is it possible to implement new methods?
 Do you require interactive or repetitive forecasting?
 Do you have any large data sets?
 Is there local support and training available?
 Does the package give the right answers?

© Wiley 2010 34
Other Forecasting Methods
 Focus Forecasting
 Developed by Bernie Smith
 Relies on the use of simple rules
 Test rules on past data and evaluate how they
perform
 Combining Forecasts
 Combining two or more forecasting methods can
improve accuracy

© Wiley 2010 35
Collaborative Planning Fore-
casting & Replenishment (CPFR)
 Establish collaborative relationships between buyers and
sellers
 Create a joint business plan
 Create a sales forecast
 Identify exceptions for sales forecast
 Resolve/collaborate on exception items
 Create order forecast
 Identify exceptions for order forecast
 Resolve/collaborate on exception items
 Generate order

© Wiley 2010 36
Forecasting within OM: How it
all fits together
Forecasts impact not only other business functions
but all other operations decisions. Operations
managers make many forecasts, such as the
expected demand for a company’s products.
These forecasts are then used to determine:
 product designs that are expected to sell (Ch 2),
 the quantity of product to produce (Chs 5 and 6),
 the amount of needed supplies and materials (Ch
12).

© Wiley 2010 37
Forecasting within OM con’t
Also, a company uses forecasts to
 determine future space requirements (Ch
10),
 capacity and
 location needs (Ch 9), and
 the amount of labor needed (Ch 11).

© Wiley 2010 38
Forecasting within OM con’t
Forecasts drive strategic operations decisions, such
as:
 choice of competitive priorities, changes in
processes, and large technology purchases (Ch 3).
 Forecast decisions serve as the basis for tactical
planning; developing worker schedules (Ch 11).
Virtually all operations management decisions are
based on a forecast of the future.

© Wiley 2010 39
Forecasting Across the
Organization
 Forecasting is critical to management of all
organizational functional areas
 Marketing relies on forecasting to predict demand
and future sales
 Finance forecasts stock prices, financial performance,
capital investment needs..
 Information systems provides ability to share
databases and information
 Human resources forecasts future hiring
requirements

© Wiley 2010 40
Chapter 8 Highlights
 Three basic principles of forecasting are: forecasts are rarely
perfect, are more accurate for groups than individual items, and
are more accurate in the shorter term than longer time
horizons.
 The forecasting process involves five steps: decide what to
forecast, evaluate and analyze appropriate data, select and test
model, generate forecast, and monitor accuracy.
 Forecasting methods can be classified into two groups:
qualitative and quantitative. Qualitative methods are based on
the subjective opinion of the forecaster and quantitative
methods are based on mathematical modeling.

© Wiley 2010 41
Chapter 8 Highlights con’t
 Time series models are based on the assumption that all information
needed is contained in the time series of data. Causal models assume
that the variable being forecast is related to other variables in the
environment.
 There are four basic patterns of data: level or horizontal, trend,
seasonality, and cycles. In addition, data usually contain random
variation. Some forecast models used to forecast the level of a time
series are: naïve, simple mean, simple moving average, weighted
moving average, and exponential smoothing. Separate models are
used to forecast trends and seasonality.
 A simple causal model is linear regression in which a straight-line
relationship is modeled between the variable we are forecasting and
another variable in the environment. The correlation is used to
measure the strength of the linear relationship between these two
variables.

© Wiley 2010 42
Highlights con’t
 Three useful measures of forecast error are mean
absolute deviation (MAD), mean square error (MSE)
and tracking signal.
 There are four factors to consider when selecting a
model: amount and type of data available, degree of
accuracy required, length of forecast horizon, and
patterns present in the data.

Homework Help
8.4: (a) forecasts using 3 methods. (b) compare forecasts
using MAD. (c) choose the “best” method and forecast
July.
8.7: use 3 methods to forecast and use MAD and MSE to
compare. (notes: will not have forecasts for all periods
using 3-period MA; use actual for period 1 as forecast to
start exp smoothing.
8.10: determine seasonal indices for each day of the week,
and use them to forecast week 3.
8.12: simple linear regression (trend) model.
NOTE: Spreadsheets might be very useful for working these
problems.

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