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The Auto Optimizer
by Robert M. Shapiro and Hartmann Genrich
Continuous Process Improvement is touted as a feature of many Business Process Management suites. Usually this means the provision of analytical techniques for measuring performance. These include Business Activity Monitoring, Balanced Score Cards, real-time measurement of Key Performance Indicators and the capture and analysis of event streams generated by the running system. Some BPM suites provide a what-if simulation capability which allows the evaluation of changes to the system. This leaves the task of coming up with proposed solutions to the user, an often daunting task.
In this paper we describe an automated, goal-driven technology for process improvement. By focusing on the common characteristics of business processes in typical BPM applications, we have developed an integrated set of algorithms for generating and evaluating proposed solutions. The user specifies the desired goals in terms of performance or cost or KPIs. The algorithms run until the goal is achieved or no further improvement is found.
Business Process Analytics
by Robert M. Shapiro and Michael zur Muehlen
Business Process Management Systems are a rich source of events that document the execution of processes and activities within these systems. Business Process Analytics is the family of methods and tools that can be applied to these event streams in order to support decision-making in organizations. The analysis of process events can focus on the behavior of completed processes, evaluate currently running process instances, or focus on predicting the behavior of process instances in the future. This chapter provides an overview of the different methods and technologies that can be employed in each of these three areas of process analytics.
We discuss the underlying format and types of process events as the common source of analytics information, present techniques for the aggregation and composition of these events, and outline methods that support backward- and forward-looking process analytics.
Analytics for Performance Optimization of BPMN2.0 Business Processes
by Robert M. Shapiro and Hartmann Genrich
We describe a new approach to process improvement based on the combined use of statistics and simulation to study the structural aspects of process models. Past efforts to use simulation focused on resource optimization have led to some significant successes when coupled with Workforce Management scheduling technology, but that approach has not been particularly successful in making structural improvements in the actual processes. The difficulty of preparing satisfactorily detailed schedules, combined with the structural complexities introduced in particular by the event and looping structures in BPMN, requires a fresh look at the problem.
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