The Business Process Execution Language (BPEL) is a process modeling language which uses standard control constructs to define a workflow. But, today‘s enterprises need to be agile to cope with increasing change, uncertainty and unpredictability. Therefore, automating agile business processes is still a challenge as they are normally knowledge intensive and can be planned to a limited degree. The execution order depends heavily on the case, which has to be performed. So instead of modeling all possible cases and situations which might occur in a knowledge intensive process we introduced an approach which uses semantic technologies and rules. Business rules can be utilized to allow for case-specific adaptation of process steps. A component was developed which allow during run-time rules to automatically detect the state of the case and to determine the necessary process adaptations.
TopIntroduction
Today's enterprises need to be responsive to
competitors, the market, organizational changes and changing
customer requirements and need to response immediately.
Agile enterprises are able to scope with increasing changes,
uncertainty and unpredictability in the business
environment. Although, the original concept of an agile
manufacturing was popularized 1991 by the Iacocca Institute
(Iacocca
1991),
there is still a lack in supporting agility by information
systems (Mutschler, Reichert et.al.
2006;
Rymer and Moore
2006).
Several workflow management systems exists, which mostly
provide process definition tools that presents the knowledge
about the process using standard modeling elements like
activities, roles or control flows elements (Mendling and
Neumann
2005).
This is suitable for production oriented processes, but it
is ill-suited to deal with changes (van der Aalst and Jablonski
2000).
Because, change is difficult, complex and risky according to
unintended side effects. Every change of one part of the
enterprise has an impact of another part, which leads to the
choice, whether to make a change or abandon the competitive
benefits of innovation because of the risk (Mitra and Gupta 2008).
However, change may range from complete restructuring of the
process definition to ad-hoc modification of a single
process instance. Especially, knowledge-intensive tasks are
performed in a fair degree of uncertainty in which they have
to deal with exceptional situations, unforeseeable events
and unpredictable situations. If possible at all, covering
all possible situations and events, the process model would
be highly complex and difficult to manage containing
multiple decision points. So, knowledge-intensive processes
can be planned to a limited degree (Faustmann 1998). The ability to have
more freedom while performing a process instance is called
flexibility (Sadiq, Sadiq, and Orlowska
2001).