In the previous post, titled “Dynamic process modeling (part 2)“, we touched upon the possibility of modeling dynamic processes with the use of BPMN. We demonstrated the performance of one or more iterations of particular tasks or sub-processes modeled within an ad-hoc sub-process.
1. Knowledge-intensive business process management (kiBPM)
In effect, we can see that the fundamental aim of the modeling process has changed. It departs from the traditional aim of defining the sequence and the details of the performed actions. Instead, in dynamic BPM the aim of the modeling process is to define a standard sequence for the performed actions, according to the knowledge of the organization, as well as define the method of performing the process in non-standard conditions. We understand the term “method of performing a process in non-standard conditions” as defining:
- privileges to choose tasks specified in ad-hoc sub-processes (tasks should not be available to all individuals)
- privileges to perform tasks not specified in ad-hoc sub-processes (the freedom to perform limited experiments in the form of performing unforeseen tasks should not be available to all individuals)
- limitations of particular tasks (e.g. parallel / serial performance of ad-hoc tasks, time or resource limits)
- data objects containing knowledge available to the process performer during the performance of a task, e.g. tips on a particular subject, control lists, process templates, best practices, the option to consult with communities of expert practitioners (the equivalent of “phone-a-friend” or another such “lifeline”), etc…
- attributes and data objects representing the knowledge collected in the course of performing a task, e.g. selections from control lists, information on experts, tasks performed outside of the standard process and their fundamental parameters, etc.
The automatic logging of such data in the BPMS system log on the 4th or at the minimum 3rd maturity level for event logs enables us to fully recreate the execution of a given process (see: Process Mining Manifesto, 2012). In effect, it is possible to assess particular process executions. By assessing multiple executions of a process, it is possible to define:
- contextual scenarios for process execution,
- experts for selected scopes of the process,
- benchmarks which exceed simple statistics,
- new criteria affecting the efficiency of processes which need to be considered or codified.
2. Process-oriented knowledge management (pKM)
Knowledge on the broader context of the actual performance of processes is derived from the ongoing activities of the organization and the ongoing performance of its processes. In order to use such knowledge to raise efficiency or build competitive advantage, we first need to execute the standard knowledge management process. This requires us to combine dynamic BPM with knowledge management. The aim is not periodic jumps in improving processes, but rather, the ongoing, constant generation of knowledge on process execution, and its rapid implementation with the aim of process improvement (Figure 1).
Figure 1. The relationships between knowledge-intensive business processes, knowledge processes, and knowledge flows (Remus and Schub, 2003).
According to the concept of dynamic BPM, process execution can serve as an ongoing, virtually free source of knowledge. Perhaps even more crucial than tacit or explicit knowledge obtained from outside the organization.
Remus, U. and Schub, S. (2003). A Blueprint for the Implementation of Process-oriented Knowledge Management. pp 237–253. Knowledge and Process Management Volume 10 Number 4.
Process Mining Manifesto, (2012). Retrieved from http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_manifesto.