MGT530 M3-Discussion Forum: Forecasting

Forecasting

3-1

You should be able to:
LO 3.1
List features common to all forecasts
LO 3.2 Explain why forecasts are generally wrong
LO 3.3 List the elements of a good forecast
LO 3.4 Outline the steps in the forecasting process
LO 3.5 Summarize forecast errors and use summaries to make decisions
LO 3.6 Describe four qualitative forecasting techniques
LO 3.7 Use a naïve method to make a forecast
LO 3.8 Prepare a moving average forecast
LO 3.9 Prepare a weighted-average forecast
LO 3.10 Prepare an exponential smoothing forecast
LO 3.11 Prepare a linear trend forecast
LO 3.12 Prepare a trend-adjusted exponential smoothing forecast
LO 3.13 Compute and use seasonal relatives
LO 3.14 Compute and use regression and correlation coefficients
LO 3.15 Construct control charts and use them to monitor forecast errors
LO 3.16 Describe the key factors and trade-offs to consider when choosing a
forecasting technique
3-2

 Forecast – a statement about the future value of a

variable of interest
 We make forecasts about such things as weather,

demand, and resource availability
 Forecasts are important to making informed decisions

LO 3.1

3-3

 Expected level of demand
 The level of demand may be a function of some

structural variation such as trend or seasonal variation
 Accuracy
 Related to the potential size of forecast error

LO 3.1

3-4

• Accounting. New product/process cost estimates, profit projections,
cash management.
• Finance. Equipment/equipment replacement needs, timing and
amount of funding/borrowing needs.
• Human resources. Hiring activities, including recruitment,
interviewing, and training; layoff planning, including outplacement
counseling.
• Marketing. Pricing and promotion, e-business strategies, global
competition strategies.
• MIS. New/revised information systems, internet services.
• Operations. Schedules, capacity planning, work assignments and
workloads, inventory planning, make-or-buy decisions, outsourcing,
project management.
• Product/service design. Revision of current features, design of new
products or services.
LO 3.1

3-5

 Plan the system
 Generally involves long-range plans related to:
 Types of products and services to offer
 Facility and equipment levels
 Facility location
 Plan the use of the system
 Generally involves short- and medium-range plans related to:
 Inventory management
 Workforce levels
 Purchasing
 Production
 Budgeting
 Scheduling

LO 3.1

3-6

1.
2.
3.

4.

LO 3.1

Techniques assume some underlying causal system that
existed in the past will persist into the future
Forecasts are not perfect
Forecasts for groups of items are more accurate than
those for individual items
Forecast accuracy decreases as the forecasting horizon
increases

3-7

 Forecasts are not perfect:
 Because random variation is always present, there will

always be some residual error, even if all other factors
have been accounted for.

LO 3.2

3-8

The forecast

Should be timely

Should be accurate

Should be reliable

Should be expressed in meaningful units

Should be in writing

Technique should be simple to understand and use

Should be cost-effective

LO 3.3

3-9

1.
2.
3.
4.

5.
6.

LO 3.4

Determine the purpose of the forecast
Establish a time horizon
Obtain, clean, and analyze appropriate data
Select a forecasting technique
Make the forecast
Monitor the forecast errors

3-10

 Qualitative forecasting
 Qualitative techniques permit the inclusion of soft information
such as:
 Human factors
 Personal opinions
 Hunches
 These factors are difficult, or impossible, to quantify
 Quantitative forecasting
 These techniques rely on hard data
 Quantitative techniques involve either the projection of historical
data or the development of associative methods that attempt to use
causal variables to make a forecast

LO 3.6

3-11

 Forecasts that use subjective inputs such as opinions from consumer

surveys, sales staff, managers, executives, and experts

 Executive opinions
 A small group of upper-level managers may meet and collectively develop a
forecast
 Salesforce opinions
 Members of the sales or customer service staff can be good sources of
information due to their direct contact with customers and may be aware of
plans customers may be considering for the future
 Consumer surveys
 Since consumers ultimately determine demand, it makes sense to solicit input
from them
 Consumer surveys typically represent a sample of consumer opinions
 Other approaches
 Managers may solicit 0pinions from other managers or staff people or outside
experts to help with developing a forecast.
 The Delphi method is an iterative process intended to achieve a consensus

LO 3.6

3-12

 Forecasts that project patterns identified in recent

time-series observations
 Time-series – a time-ordered sequence of observations

taken at regular time intervals
 Assume that future values of the time-series can be

estimated from past values of the time-series

LO 3.6

3-13

 Trend
 Seasonality
 Cycles
 Irregular variations

 Random variation

LO 3.6

3-14

 Trend
 A long-term upward or downward movement in data
 Population shifts
 Changing income

 Seasonality
 Short-term, fairly regular variations related to the calendar or time
of day
 Restaurants, service call centers, and theaters all experience
seasonal demand

LO 3.6

3-15

 Cycle
 Wavelike variations lasting more than one year
 These are often related to a variety of economic, political, or even

agricultural conditions

 Irregular variation
 Due to unusual circumstances that do not reflect typical behavior
 Labor strike
 Weather event

 Random Variation
 Residual variation that remains after all other behaviors have been
accounted for

LO 3.6

3-16

 Naïve forecast
 Uses a single previous value of a time series as the basis

for a forecast
 The forecast for a time period is equal to the previous
time period’s value
 Can be used with
 A stable time series
 Seasonal variations
 Trend

LO 3.7

3-17

 These techniques work best when a series tends to vary

about an average
 Averaging techniques smooth variations in the data
 They can handle step changes or gradual changes in the

level of a series
 Techniques
1.
2.
3.

LO 3.7

Moving average
Weighted moving average
Exponential smoothing

3-18

 Technique that averages a number of the most recent

actual values in generating a forecast
n

Ft = MA n =

A

t −i

i =1

n

At − n + … + At − 2 + At −1
=
n

where
Ft = Forecast for time period t
MA n = n period moving average
At −i = Actual value in period t − i
n = Number of periods in the moving average
LO 3.8

3-19

 As new data become available, the forecast is updated

by adding the newest value and dropping the oldest
and then re-computing the average
 The number of data points included in the average
determines the model’s sensitivity
 Fewer data points used—more responsive
 More data points used—less responsive

LO 3.7

3-20

 The most recent values in a time series are given more

weight in computing a forecast
 The choice of weights, w, is somewhat arbitrary and

involves some trial and error
Ft = wt ( At ) + wt −1 ( At −1 ) + … + wt − n ( At − n )
where
wt = weight for period t , wt −1 = weight for period t − 1, etc.
At = the actual value for period t , At −1 = the actual value for period t − 1, etc.

LO 3.9

3-21

 A weighted averaging method that is based on the

previous forecast plus a percentage of the forecast
error
Ft = Ft −1 +  ( At −1 − Ft −1 )
where
Ft = Forecast for period t
Ft −1 = Forecast for the previous period

 = Smoothing constant
At −1 = Actual demand or sales from the previous period

LO 3.10

3-22

 A simple data plot can reveal the existence and nature

of a trend
 Linear trend equation
Ft = a + bt
where
Ft = Forecast for period t
a = Value of Ft at t = 0
b = Slope of the line
t = Specified number of time periods from

LO 3.11

t =0
3-23

 Slope and intercept can be estimated from historical

data
b=

n  ty −  t  y

( )

n t −  t
2

2

y − b t

a=
or y − bt
n

where
n = Number of periods
y = Value of the time series

LO 
3.11

3-24

 The trend adjusted forecast consists of two

components
 Smoothed error
 Trend factor

TAFt +1 = St + Tt
where
St = Previous forecast plus smoothed error
Tt = Current trend estimate



LO 3.12

3-25

 Alpha and beta are smoothing constants
 Trend-adjusted exponential smoothing has the ability

to respond to changes in trend

TAFt +1 = St + Tt
St = TAFt +  (At − TAFt )
Tt = Tt−1 +  (TAFt − TAFt−1 − Tt−1 )


LO 3.12

3-26

 Seasonality – regularly repeating movements in

series values that can be tied to recurring events
 Expressed in terms of the amount that actual values

deviate from the average value of a series
 Models of seasonality
 Additive

 Seasonality is expressed as a quantity that gets added to or

subtracted from the time-series average in order to
incorporate seasonality
 Multiplicative

 Seasonality is expressed as a percentage of the average (or

trend) amount which is then used to multiply the value of a
series in order to incorporate seasonality

LO 3.12

3-27

 Seasonal relatives
 The seasonal percentage used in the multiplicative seasonally
adjusted forecasting model
 Using seasonal relatives
 To deseasonalize data
 Done in order to get a clearer picture of the nonseasonal (e.g.,

trend) components of the data series
 Divide each data point by its seasonal relative
 To incorporate seasonality in a forecast
1.

2.

LO 3.13

Obtain trend estimates for desired periods using a trend
equation
Add seasonality by multiplying these trend estimates by the
corresponding seasonal relative

3-28

 Associative techniques are based on the

development of an equation that summarizes the
effects of predictor variables
 Predictor variables – variables that can be used to

predict values of the variable of interest
 Home values may be related to such factors as home and

property size, location, number of bedrooms, and number of
bathrooms

LO 3.14

3-29

 Regression – a technique for fitting a line to a set of

data points
 Simple linear regression – the simplest form of

regression that involves a linear relationship between
two variables
 The object of simple linear regression is to obtain an equation

of a straight line that minimizes the sum of squared vertical
deviations from the line (i.e., the least squares criterion)

LO 3.14

3-30

yc = a + bx
where
yc = Predicted (dependent ) variable
x = Predictor (independe nt) variable
b = Slope of the line
a = Value of yc when x = 0 (i.e., the height of the line at the y intercept)
and
b=

n( xy) − ( x )( y )

(

)

n  x 2 − ( x )

2

y − b x

a=
or y − b x
n

where
n = Number of paired observatio ns
LO 3.14

3-31

 Correlation, r
 A measure of the strength and direction of relationship between
two variables
 Ranges between -1.00 and +1.00

r=

n( xy) − ( x )( y )

n( x 2 ) − ( x )

2

n( y 2 ) − ( y )

2

 r2, square of the correlation coefficient
 A measure of the percentage of variability in the values of y that is
“explained” by the independent variable
 Ranges between 0 and 1.00

LO 3.14

3-32

Variations around the line are random
2. Deviations around the average value (the line)
should be normally distributed
3. Predictions are made only within the range of
observed values
1.

LO 3.14

3-33

 Always plot the line to verify that a linear relationship

is appropriate
 The data may be time-dependent
 If they are
 use analysis of time series
 use time as an independent variable in a multiple regression
analysis

 A small correlation may indicate that other variables

are important

LO 3.14

3-34

 Allowances should be made for forecast errors
 It is important to provide an indication of the extent to

which the forecast might deviate from the value of the
variable that actually occurs
 Forecast errors should be monitored
 Error = Actual – Forecast
 If errors fall beyond acceptable bounds, corrective

action may be necessary

LO 3.5

3-35

Actual − Forecast

MAD =
t

MAD weights all errors
evenly

t

n

(Actual − Forecast )

MSE =
t

2

t

n −1

Actual t − Forecast t
 100

Actual t
MAPE =
n

LO 3.5

MSE weights errors according
to their squared values

MAPE weights errors
according to relative error
3-36

Period

Actual
(A)

Forecast
(F)

(A-F)
Error

|Error|

Error2

[|Error|/Actual]x100

1

107

110

-3

3

9

2.80%

2

125

121

4

4

16

3.20%

3

115

112

3

3

9

2.61%

4

118

120

-2

2

4

1.69%

5

108

109

1

1

1

0.93%

Sum

13

39

11.23%

n=5

n-1 = 4

n=5

MAD

MSE

MAPE

= 2.6

= 9.75

= 2.25%

LO 3.5

3-37

 Tracking forecast errors and analyzing them can provide useful

insight into whether forecasts are performing satisfactorily
 Sources of forecast errors:
 The model may be inadequate due to
a.
b.
c.

omission of an important variable
a change or shift in the variable the model cannot handle
the appearance of a new variable

 Irregular variations may have occurred
 Random variation
 Control charts are useful for identifying the presence of non-

random error in forecasts
 Tracking signals can be used to detect forecast bias
LO 3.15

3-38

1. Compute the MSE.
2. Estimate of standard deviation of the distribution of errors
s = MSE
3. UCL: 0 + z MSE
4. LCL: 0 – z MSE
where z = Number of standard deviations from the mean
LO 3.15

3-39

 Factors to consider
 Cost
 Accuracy
 Availability of historical data
 Availability of forecasting software
 Time needed to gather and analyze data and prepare a

forecast
 Forecast horizon

LO 3.16

3-40

 The better forecasts are, the more able organizations will

be to take advantage of future opportunities and reduce
potential risks
 A worthwhile strategy is to work to improve short-term forecasts
 Accurate up-to-date information can have a significant effect on

forecast accuracy:
 Prices
 Demand
 Other important variables

 Reduce the time horizon forecasts have to cover

 Sharing forecasts or demand data through the supply chain can

improve forecast quality

LO 3.16

3-41

7/5/23, 11:31 AM

Operations Management

Name

Discussion 25

Description

25 points

Rubric Detail
Levels of Achievement
Criteria

Exceeds
Expectations

Meets
Expectation

Some
Expectations

Unsatisfactory

Quantity

5 to 6 points

3 to 4 points

1 to 2 points

0 to 0 points

Initial post and
two other posts
of substance.

Initial post and
one other post
of substance.

Initial post only.

Did not
participate.

5 to 6 points

3 to 4 points

1 to 2 points

0 to 0 points

Demonstrates
excellent
knowledge of
concepts, skills,
and theories
relevant to the
topic.

Demonstrates
knowledge of
concepts, skills,
and theories.

Demonstrates
satisfactory
knowledge of
concepts, skills,
and theories.

Did not
participate.

5 to 6 points

3 to 4 points

1 to 2 points

0 to 0 points

Discussion
post(s) exceed
expectations in
terms of support
provided and
extend the
discussion.

Discussion
post(s) meet
expectations in
terms of
support
provided.

Statements are
satisfactory in
terms of
support
provided.

Did not
participate.

6 to 7 points

4 to 5 points

1 to 2 points

0 to 0 points

Writing is well
organized, clear,
concise, and
focused; no
errors.

Some significant
but not major
errors or
omissions in
writing
organization,
focus, and
clarity.

Numerous
significant
errors or
omissions in
writing
organization,
focus, and
clarity.

Did not
participate.

Content

Support

Writing

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