Business Process Management (BPM)
Business Process Management (BPM) is the discipline that combines knowledge from information technology and knowledge from management sciences and applies this to operational business processes. It has received considerable attention in recent years due to its potential for significantly increasing productivity and saving costs. Moreover, today there is an abundance of BPM systems. These systems are generic software systems that are driven by explicit process designs to enact and manage operational business processes.
BPM can be seen as an extension of Workflow Management (WFM). WFM primarily focuses on the automation of business processes, whereas BPM has a broader scope: from process automation and process analysis to operations management and the organization of work. On the one hand, BPM aims to improve operational business processes, possibly without the use of new technologies. For example, by modeling a business process and analyzing it using simulation, management may get ideas on how to reduce costs while improving service levels. On the other hand, BPM is often associated with software to manage, control, and support operational processes. This was the initial focus of WFM. However, traditional WFM technology aimed at the automation of business processes in a rather mechanistic manner without much attention for human factors and management support.
See Business Process Management: A Comprehensive Survey. ISRN Software Engineering, pages 1-37, 2013. doi:10.1155/2013/507984.
Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. The research we started in the late 1990-ties in Eindhoven, led to a wave of process mining tools, including Celonis, Disco, ProcessGold, MyInvenio, Minit, PuzzleData, Everflow, PAFnow, ARIS Process Mining, Stereologic, Logpickr, Mehrwerk, QPR, Lana Labs, Signavio, etc. On the one hand, conventional Business Process Management (BPM) and Workflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. Here, the challenge is to turn torrents of event data ("Big Data") into valuable insights related to process performance and compliance. Fortunately, process mining results can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks.
See slides on how to get started, the process mining book, the process mining website, the process mining tool ProM, the IEEE Task Force on Process Mining, and my publications for loads of information.
Petri Nets and Other Models of Concurrency
Petri nets are the oldest and best investigated process modeling language allowing for concurrency. Although the graphical notation is intuitive and simple, Petri nets are executable and many analysis techniques can be used to analyze them.
Petri nets have a strong theoretical basis. Moreover, a wide range of powerful analysis techniques and tools exists. Petri nets take concurrency as a starting point rather than an afterthought. Obviously, this succinct model has problems capturing data-related and time-related aspects. Therefore, various types of high-level Petri nets have been proposed. Colored Petri nets (CPNs) are the most widely used Petri-net based formalism that can deal with data-related and time-related aspects. Tokens in a CPN carry a data value and have a timestamp. The data value, often referred to as color, describes the properties of the object modeled by the token. The timestamp indicates the earliest time at which the token may be consumed. Transitions can assign a delay to produced tokens. This way waiting and service times can be modeled. A CPN may be hierarchical, i.e., transitions can be decomposed into subprocesses. This way large models can be structured. CPN Tools is a toolset providing support for the modeling and analysis of CPNs (www.cpntools.org).
See Petri Nets World and the book W.M.P. van der Aalst and C. Stahl. Modeling Business Processes: A Petri Net Oriented Approach. MIT press, Cambridge, MA, 2011 for more on Petri nets.
Workflow Patterns and WFM Systems
The Workflow Patterns initiative is a joint effort of Eindhoven University of Technology and Queensland University of Technology which started in 1999. The aim of this initiative is to provide a conceptual basis for process technology. In particular, the research provides a thorough examination of the various perspectives (control flow, data, resource, and exception handling) that need to be supported by a workflow language or a business process modelling language. The results can be used for examining the suitability of a particular process language or workflow system for a particular project, assessing relative strengths and weaknesses of various approaches to process specification, implementing certain business requirements in a particular process-aware information system, and as a basis for language and tool development.
The web site www.workflowpatterns.com provides detailed descriptions of patterns for the various perspectives relevant for process-aware information systems: control-flow, data, resource, and exception handling. In addition you will find detailed evaluations of various process languages, (proposed) standards for web service compositions, and workflow systems in terms of this patterns.
Based on a rigorous analysis of existing workflow management systems and workflow languages, we have developed the workflow language: YAWL (Yet Another Workflow Language). YAWL is a powerful workflow language based on the workflow patterns and Petri nets that is supported by an open source environment developed in part in collaboration with industry.
Simulation provides a flexible approach to analyzing business processes. Through simulation experiments various what if questions can be answered and redesign alternatives can be compared with respect to key performance indicators. Increasingly, simulation techniques will need to incorporate actual event data. Moreover, there will be a shift from off-line analysis at design time to on-line analysis at run-time. Hence, the link between process mining and simulation is obvious.
See my Business Process Simulation Survival Guide for more information on modern forms of simulation and the pitfalls people typically make when using simulation.