Process modeling

About: Process modeling is a research topic. Over the lifetime, 11639 publications have been published within this topic receiving 223996 citations. The topic is also known as: process simulation.

Seven process modeling guidelines (7PMG)

Abstract: Business process modeling is heavily applied in practice, but important quality issues have not been addressed thoroughly by research A notorious problem is the low level of modeling competence that many casual modelers in process documentation projects have Existing approaches towards model quality might be of benefit, but they suffer from at least one of the following problems On the one hand, frameworks like SEQUAL and the Guidelines of Modeling are too abstract to be applicable for novices and non-experts in practice On the other hand, there are collections of pragmatic hints that lack a sound research foundation In this paper, we analyze existing research on relationships between model structure on the one hand and error probability and understanding on the other hand As a synthesis we propose a set of seven process modeling guidelines (7PMG) Each of these guidelines builds on strong empirical insights, yet they are formulated to be intuitive to practitioners Furthermore, we analyze how the guidelines are prioritized by industry experts In this regard, the seven guidelines have the potential to serve as an important tool of knowledge transfer from academia into modeling practice

Replaying history on process models for conformance checking and performance analysis

Abstract: Process mining techniques use event data to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand-made models. Event logs are used to learn and enrich process models. By replaying history using the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quantified. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Advanced Process Control

Abstract: This chapter explores both run-by-run and supervisory process control strategies. Such advanced process control techniques are required more and more for increasingly sophisticated modern semiconductor manufacturing applications. The chapter addresses control in two main categories. In this first, we deal with controllers that utilize polynomial process models, but limit their adaptation to the constant term of the model. Afterwards, we discuss controllers that can adapt the entire model.