2. 3-2
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 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. 3-3
Forecast is a statement about the future
value of a variable of interest.
Forecasts are:
a basic input in the decision processes of
operations management because they
provide information on future demand.
Primary goal : matching supply to demand:
Having a forecast of demand is essential for
determining how much capacity or supply will
be needed to meet demand.
LO 3.1
4. 3-4
Two aspects of forecasts:
1. The expected level of demand;
The expected level of demand can be a function
of some structural variation, such as a trend or
seasonal variation.
2. The degree of accuracy that can be
assigned to a forecast ;
Forecast accuracy is a function of the
ability of forecasters to correctly model
demand, random variation, and
sometimes unforeseen events.
LO 3.1
5. 3-5
Time Horizon:
Short-range forecast: (e.g., an hour, day,
week, or month).
Short-term forecasts pertain to ongoing
operations
Long-range forecast: (e.g., the next six
months, the next year, the next five years,).
Long-range forecasts pertain to new
products or services, new equipment, new
facilities.
LO 3.1
6. 3-6
1. Techniques assume some underlying
causal system that existed in the past
will persist into the future
2. Forecasts are not perfect
3. Forecasts for groups of items are more
accurate than those for individual
items
4. Forecast accuracy decreases as the
forecasting horizon increases
LO 3.1
7. 3-7
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:
the benefit should outweigh the cost
LO 3.3
8. 3-8
1. Determine the purpose of the forecast
How will it be used and when will it be needed? This step will
provide an indication of the level of detail required in the
forecast, the amount of resources (personnel, computer time,
dollars) that can be justified, and the level of accuracy
necessary.
2. Establish a time horizon:
Keep in mind that accuracy decreases as the time horizon
increases.
3. Obtain, clean, and analyze appropriate data
4. Select a forecasting technique
5. Make the forecast
6. Monitor the forecast errors
LO 3.4
9. 3-9
1. Determine the purpose of the forecast
How will it be used and when will it be needed? This step will
provide an indication of the level of detail required in the
forecast, the amount of resources (personnel, computer time,
dollars) that can be justified, and the level of accuracy
necessary.
2. Establish a time horizon:
Keep in mind that accuracy decreases as the time horizon
increases.
3. Obtain, clean, and analyze appropriate data
4. Select a forecasting technique
5. Make the forecast
6. Monitor the forecast errors
LO 3.4
10. 3-10
1. Determine the purpose of the forecast
How will it be used and when will it be needed? This step will
provide an indication of the level of detail required in the
forecast, the amount of resources (personnel, computer time,
dollars) that can be justified, and the level of accuracy
necessary.
2. Establish a time horizon:
Keep in mind that accuracy decreases as the time horizon
increases.
3. Obtain, clean, and analyze appropriate data
4. Select a forecasting technique
5. Make the forecast
6. Monitor the forecast errors
LO 3.4
11. 3-11
Accuracy and control of forecasts is a vital aspect of
forecasting;
forecasters should minimize forecast errors.
Decision makers will want to include accuracy as a factor
when choosing among different techniques, along with
cost.
Accurate forecasts are necessary for the success of daily
activities of every business organization.
Forecasts are the basis for an organization’s schedules
LO 3.5
12. 3-12
Forecast error is the difference between the value that
occurs and the value that was predicted for a given time
period.
Error = Actual - Forecast:
LO 3.5
13. 3-13
Positive errors result when the forecast is too
low,
Negative errors result when the forecast is too
high.
For example, if actual demand for a week is
100 units and forecast demand was 90 units,
the forecast was too low;
The error is :
100 – 90 = 10.
LO 3.5
14. 3-14
n
t
t Forecast
Actual
MAD
2
t
t
1
Forecast
Actual
MSE
n
n
100
Actual
Forecast
Actual
MAPE t
t
t
MAD weights all errors
evenly
MSE weights errors according
to their squared values
MAPE weights errors
according to relative error
LO 3.5
18. 3-18
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
Sales force 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
LO 3.6
19. 3-19
Quantitative methods involve either the projection of
historical data or the development of associative
models that attempt to utilize causal (explanatory)
variables to make a forecast.
Quantitative techniques consist mainly of analyzing
objective, or hard, data. They usually avoid personal
biases that sometimes contaminate qualitative
methods.
In practice, either approach or a
combination of both approaches might
be used to develop a forecast.
LO 3.6
20. 3-20
Judgmental forecasts rely on analysis of subjective inputs
obtained from various sources, such as:
consumer surveys, the sales staff, managers and executives, and
panels of experts.
Time-series forecasts simply attempt to project past
experience into the future.
These techniques use historical data with the assumption that
the future will be like the past
Associative models use equations that consist of one or
more explanatory variables that can be used to predict
demand.
For example, demand for paint might be related to variables
such as the price per gallon and the amount spent on
advertising, as well as to specific characteristics of the paint (e.g.,
drying time, ease of cleanup).
LO 3.6
21. 3-21
A time series is a time-ordered sequence of observations
taken at regular intervals (e.g. hourly, daily, weekly,
monthly, quarterly, annually).
Assumption:
future values of the series can be estimated from past
values.
time-series data requires:
to identify the underlying behavior of the series. This
can often be accomplished by merely plotting the data
and visually examining the plot
LO 3.7
22. 3-22
Trend: long-term upward or downward movement in the data.
Population shifts, changing incomes, and cultural changes
often account for such movements.
Seasonality: short-term, fairly regular variations generally
related to factors such as the calendar or time of day.
Cycles: wavelike variations of more than one year’s duration.
These are often related to a variety of economic, political, and
even agricultural conditions.
Irregular variations: due to unusual circumstances such as
severe weather conditions, strikes, or a major change in a
product or service.
Random variations: residual variations that remain after all
other behaviors have been accounted for.
LO 3.7
23. 3-23
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
25. 3-25
Technique that averages a number of the most recent
actual values in generating a forecast
average
moving
in the
periods
of
Number
period
in
value
Actual
average
moving
period
MA
period
for time
Forecast
where
...
MA 1
2
1
n
i
t
A
n
t
F
n
A
A
A
n
A
F
i
t
n
t
t
t
n
t
n
i
i
t
n
t
LO 3.8
28. 3-28
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
etc.
,
1
period
for
value
actual
the
,
period
for
value
actual
the
etc.
,
1
period
for
weight
,
period
for
weight
where
)
(
...
)
(
)
(
1
1
1
1
t
A
t
A
t
w
t
w
A
w
A
w
A
w
F
t
t
t
t
n
t
n
t
t
t
t
t
t
LO 3.9
31. 3-31
A weighted averaging method that is based on the
previous forecast plus a percentage of the forecast
error
period
previous
the
from
sales
or
demand
Actual
constant
Smoothing
=
period
previous
for the
Forecast
period
for
Forecast
where
)
(
1
1
1
1
1
t
t
t
t
t
t
t
A
F
t
F
F
A
F
F
LO 3.10
32. 3-32
Example
Suppose the previous forecast was
42 units, actual demand was 40
units, and error was.10.
LO 3.10
35. 3-35
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
