How to Analyzing and Optimizing Process Performance
The hottest trend now in Business Process Management suites is integrated performance management. While Business Process Management System and its workflow ancestors have always featured basic process monitoring and work statistics, the kind of performance management now emphasized is more closely aligned with metrics strategic to the business: the percentage of orders filled immediately, or conformance with service level targets, broken down by customer type.
These once were the sole province of business intelligence(BI)software, but performance management analytics and real-time business activity monitoring are now being brought inside the Business Process Management suite itself. In doing so, leading Business Process Management System vendors are trying to distinguish this new level of performance management functionality from both traditional process monitoring and the kind of deep analytics provided by BI tools.
To understand the differences, let’s first look at how these new systems work.
Business analysts start by defining business measures and relating them to process data. These measures may be based on aggregated counts, time intervals, dollar value, utilization rate, or other data types. Some aggregated business measures, called key performance indicators(KPIs),have in addition a user-defined target range or goal. Users can define notifications or automated actions triggered when measured KPI values go out of their target range. For analysis purposes, users can also define the dimensions of measurement, or how the measures can be broken out, e.g. by time period, by customer type, by product, etc. The combination of measures and dimensions defines the schema of an operational data store,
a form of data warehouse that allows performance data to be rapidly recalculated, queried, and displayed in graphical dashboards.
High-level KPIs strategic to the business may involve external business data in addition to process data. The performance management component of many Business Process Management suites is limited to business data managed or retrieved by the business process management itself. On the other hand, the ability to aggregate and analyze business data and events directly from diverse process management systems defines the kind of deep analytics found in BI tools. A new generation of Business Process Management System now seeks to unite these two sources of business performance data.
At runtime, the performance management component receives data in the form of events. Events are signals, typically in the form of messages, that a state change or transaction has occurred. Process events are generated automatically by the Business Process Management System process engine upon state changes such as completion of a process activity or task.
In some Business Process Management System, when a work item enters a task queue, is claimed or opened from the worklist, and is completed by the task participant are all considered separate state changes. The event is a signal that the state change has occurred, sometimes accompanied by context data providing additional information supporting the business measures and dimensions. In addition, external process management systems can be programmed to emit events, using database triggers
or integration adapters, that signal state changes such as a new or updated record in an orders table.
How those events are processed by the Business Process Management System performance management component defines some of the differences between vendor offerings. In leading products, events are processed by a real-time event correlation engine.
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