: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives
: The main objectives of this thesis are to provide:
- A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner;
- A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and
- A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context.
: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN
) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our ap-proach by performing a systematic literature review, by assessing it against Zellner' mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail).
: The two examples show that through the framework's iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN
allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN
hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN
provided in this thesis as well as the discussed improvement patterns.
- 01 May 2014
- Thesis available online at http://hdl.handle.net/10393/30958
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