Business processes can be classified according to their degree of repetition.
Examples of highly repetitive business processes include business processes
without human involvement, such as online airline ticketing. However, business
processes in which humans are involved can occur frequently, for example,
insurance claim processing. If the degree of repetition is high, then investments
in modelling and supporting the automatic enactment of these processes pay
off, because many process instances can benefit from these investments.
At the other end of the repetition continuum, there are business processes
that occur a few times only. Examples include large engineering efforts, such
as designing a vessel. For these processes it is questionable whether the effort
introduced by process modelling does in fact pay off, because the cost of
process modelling per process instance is very high.
Since improving the collaboration between the persons involved is at the
centre of attention, these processes are called collaborative business processes.
In collaborative business processes, the goal of process modelling and enactment
is not only efficiency, but also tracing exactly what has actually been
done and which causal relationships between project tasks have occurred.
This aspect is also present in the management of scientific experiments,
where data lineage is an important goal of process support. Since each experiment
consists of a set of activities, an increasing fraction of the experimentation
is performed by analyzing data using software systems. The data
is transformed in a series of steps. Since experiments need to be repeatable,
it is essential that the relationship of the data sets be documented properly.
Business processes with a low degree of repetition are often not fully automated
and have a collaborative character, so that the effort in providing
automated solutions is not required, which lowers the cost.
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