Some operations can predict demand with more certainty than others. For example, consider
an operation providing professional decorating and refurbishment services which has as its
customers a number of large hotel chains. Most of these customers plan the refurbishment
and decoration of their hotels months or even years in advance. Because of this, the decoration
company can itself plan its activities in advance. Its own demand is dependent upon the
relatively predictable activities of its customers. By contrast, a small painter and decorator
serves the domestic and small business market. Some business also comes from house construction
companies, but only when their own painters and decorators are fully occupied.
In this case, demand on the painting and decorating company is relatively unpredictable.
To some extent, there is a random element in demand which is virtually independent of any
factors obvious to the company.
Dependent demand, then, is demand which is relatively predictable because it is dependent
upon some factor which is known. For example, the manager who is in charge of ensuring
that there are suffi cient tyres in an automobile factory will not treat the demand for tyres
as a totally random variable. He or she will not be totally surprised by the exact quantity
of tyres which are required by the plant every day. The process of demand forecasting is
relatively straightforward. It will consist of examining the manufacturing schedules in the car
plant and deriving the demand for tyres from these. If 200 cars are to be manufactured on
a particular day, then it is simple to calculate that 1,000 tyres will be demanded by the car
plant (each car has fi ve tyres) – demand is dependent on a known factor, the number of cars
to be manufactured. Because of this, the tyres can be ordered from the tyre manufacturer
to a delivery schedule which is closely in line with the demand for tyres from the plant (as
in Figure 10.3). In fact, the...