Operations and Supply Chain Management The Core

50 OPERATIONS AND SUPPLY CHAIN MANAGEMENT
exhibit 3.3
A Guide to Selecting an Appropriate Forecasting Method
FORECASTING METHOD AMOUNT OF HISTORICAL DATA DATA PATTERN FORECAST HORIZON
Simple moving average
Weighted moving
average and simple
exponential smoothing
Exponential smoothing
with trend
6 to 12 months; weekly
data are often used
5 to 10 observations
needed to start
5 to 10 observations
needed to start
Stationary only (i.e., no
trend or seasonality)
Stationary only
Stationary and trend
Linear regression 10 to 20 observations Stationary, trend, and
seasonality
Trend and seasonal
models
2 to 3 observations per
season
Stationary, trend,
seasonality
Short
Short
Short
Short to medium
Short to medium
they are used. However, in business forecasting short term usually refers to under three
months; medium term, three months to two years; and long term, greater than two years.
We would generally use short-term forecasts for tactical decisions such as replenishing
inventory or scheduling employees in the near term and use medium-term forecasts for
planning a strategy for meeting demand over the next six months to a year and a half. In
general, the short-term models compensate for random variation and adjust for short-term
changes (such as consumers’ responses to a new product). They are especially good for
measuring the current variability in demand, which is useful for setting safety stock levels
or estimating peak loads in a service setting. Medium-term forecasts are useful for capturing
seasonal effects, and long-term models detect general trends and are especially useful
in identifying major turning points.
Which forecasting model a firm should choose depends on:
1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified personnel
In selecting a forecasting model, there are other issues such as the firm’s degree of flexibility.
(The greater the ability to react quickly to changes, the less accurate the forecast
needs to be.) Another item is the consequence of a bad forecast. If a large capital investment
decision is to be based on a forecast, it should be a good forecast.
Moving average
A forecast based
on average past
demand.
Simple Moving Average
When demand for a product is neither growing nor declining rapidly, and if it does not
have seasonal characteristics, a moving average can be useful in removing the random
fluctuations for forecasting. The idea here is to simply calculate the average demand
over the most recent periods. Each time a new forecast is made, the oldest period is
discarded in the average and the newest period included. Thus, if we want to forecast June
with a five-month moving average, we can take the average of January, February, March,
April, and May. When June passes, the forecast for July would be the average of February,
March, April, May, and June. An example using weekly demand is shown in Exhibit 3.4.