LESSON 4: FORECASTING
Outline
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Forecasting Success Stories
Decisions Based on Forecasts
Characteristics of Forecasts
Components of demand
Evaluation of forecasts
Forecasting Success Stories
• Sharing forecasting information
along the supply chain is not very
common. The result is forecast error
as much as 60 percent of actual
demand. US economy alone can
save $179 billion in inventory
investment with a coordinated
forecasting. Wal-Mart has initiated
such a process with WarnerLambert, a manufacturer of
Listerine.
• Hewlett-Packard uses forecasting
method for new product
development.
Forecasting Success Stories
• Taco Bell developed a forecasting
method that gives customer demand
for every 15-minute interval. The
forecast is used to determine the
number of employees required. Taco
Bell achieved labor savings of more
than $40 million from 1993 to 1996.
• Compaq delayed the announcement
of several new Pentium-based
models in 1994. The decision was
based on a forecasting method and
contrary to the company belief.
Forecasting Success Stories
• Forecasting is necessary to determine the number of
reservations an airline should accept for a particular
flight - overbooking, traffic management, discount
allocation, etc.
Decisions Based on Forecasts
• Production
– Aggregate planning,
inventory control,
scheduling
• Marketing
– New product
introduction, salesforce allocation,
promotions
• Finance
– Plant/equipment
investment,
budgetary planning
• Personnel
– Workforce planning,
hiring, layoff
Decisions Based on Forecasts
The decisions should not be
segregated by functional area,
as they influence each other
and are best best made jointly.
For example, Coca-cola
considers the demand
forecast over the coming
quarter and decides on the
timing of various promotions.
The promotion information is
then used to update the
demand forecast. Based on
this forecast, Coca-Cola will
decide on a production plan
for the quarter.
This plan may require
additional investment, hiring,
or perhaps subcontracting of
production. Coke will make
these decisions based on the
production plan and existing
capacity, and it must make
them all in advance of actual
production.
Characteristics of Forecasts
• Forecasts are always
wrong; so consider both
expected value and a
measure of forecast error.
• Long-term forecasts are
less accurate than shortterm forecasts. For
example, 7-Eleven Japan
has a replenishment process
that enables it to respond to
an order within hours. If a
store manager places an
order by 10 am, the order is
delivered by 7 pm the same
day. The store manager thus
has to forecast what will sell
that night less than 12 hours
before the actual sale.
Characteristics of Forecasts
• Some time series (called aggregate series) are
obtained by summing up more than one time series
(called disaggregate series) . For example, annual
sales are obtained by adding 12 monthly sales. The
annual sales is an aggregate series and monthly sales
is a disaggregate series. Aggregate forecasts are more
accurate than disaggregate forecasts. Variation in GDP
of a country is much less than the annual earnings of a
company. Consequently, it is easy to forecast the GDP
of a country with less than 2% error. However, it is
much more difficult to forecast annual earning of a
company with less than a 2% error.
Components of Demand
• Average demand
• Trend
– Gradual shift in average demand
• Seasonal pattern
– Periodic oscillation in demand which repeats
• Cycle
– Similar to seasonal patterns, length and magnitude
of the cycle may vary
• Random movements
• Auto-correlation
Qantity
Components of Demand
Time
(a) Average: Data cluster about a horizontal line.
Quantity
Components of Demand
Time
(b) Linear trend: Data consistently increase or decrease.
Components of Demand
Quantity
Year 1
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J
F
M
A
M
J
J
A
S
O
N
D
Months
(c) Seasonal influence: Data consistently show
peaks and valleys.
Components of Demand
Quantity
Year 1
Year 2
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J
F
M
A
M
J
J
A
S
O
N
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Months
(c) Seasonal influence: Data consistently show
peaks and valleys.
Components of Demand
Quantity
Components of Demand
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1
2
3
4
5
6
Years
(c) Cyclical movements: Gradual changes over
extended periods of time.
Components of Demand
Suppose that a company
has institute a sales
incentive system that
provides a bonus for the
employee with the best
improvement in bookings
from one month to the next.
With such an incentive in
place, a month of poor sales
is often followed by a month
of good sales. Similarly, a
month of good sales would
usually be followed by a lull.
Components of Demand
This means that sales in
consecutive months tend to
be negatively correlated.
This information can be
used to improve sales
forecasts. Autocorrelation is
the correlation among
values of observed data
separated by a fixed
number of periods. In the
example above, we would
say that the series has a
negative autocorrelation of
order one.
Trend
Random
movement
Time
Demand
Demand
Components of Demand
Trend with
seasonal pattern
Time
Snow Skiing
Seasonal
Long term growth trend
Demand for skiing products increased
sharply after the Nagano Olympics
Evaluation of Forecast
There are many forecasting techniques and software. The
performance of a forecasting technique can be measured by
the error produced over time.
We shall now discuss various measures used to evaluate
forecasting techniques. Let,
Dt  Actual data in period t
F t  Forecast in period t
Et  Forecast error in period t  Ft  Dt
  Standard deviation of forecast errors
MSE  Mean Squared Error
MAD  Mean Absolute Deviation
MAPE  Mean Absolute Percentage Error
Evaluation of Forecast
Measures of Forecast Error
Et = Ft - Dt
MSE =

Et2
MAD =
n
MAPE =
 =
MSE
|Et |
n
[ |Et | (100) ] /Dt
n
Evaluation of Forecast
Month,
t
Demand,
Dt
Forecast,
Ft
1
2
3
4
5
6
7
8
200
240
300
270
230
260
210
275
225
220
285
290
250
240
250
240
-
Total
Error,
Et
Error
Squared,
Et2
Absolute
Absolute
Percent
Error,
Error,
|Et|
(|Et|/Dt)(100)
Evaluation of Forecast
Measures of Error
MSE =
MAD =
MAPE =
=
=
=
READING AND EXERCISES
Lesson 4
Reading:
Section 2.1-2.6, pp. 55-66 (4th Ed.), pp. 51-63 (5th
Ed.)
Exercises:
12, 13, 15, pp. 65-66 (4th Ed.), pp. 62-63 (5th Ed.)