A modeling framework for business process reengineering using big data analytics and a goal orientation
A business process is a collection of activities to create more business values and its continuous improvement aligned with business goals is essential to survive in fast changing business environment. However, it is quite challenging to find out whether a change of business processes positively affects business goals or not, if there are problems in the changing, what the reasons of the problems are, what solutions exist for the problems and which solutions should be selected. Big data analytics along with a goal orientation which helps find out insights from a large volume of data in a goal concept opens up a new way for an effective business process reengineering. In this paper, we suggest a novel modeling framework which consists of a conceptual modeling language, a process and a tool for effective business processes reengineering using big data analytics and a goal oriented approach. The modeling language defines important concepts for business process reengineering with metamodels and shows the concepts with complementary views: Business Goal Process Big Analytics Alignment View, Transformational Insight View and Big Analytics Query View. Analyzers hypothesize problems and solutions of business processes by using the modeling language, and the problems and solutions will be validated by the results of Big Analytics Queries which supports not only standard SQL operation, but also analytics operation such as prediction. The queries are run in an execution engine of our tool on top of Spark which is one of big data processing frameworks. In a goal oriented spirit, all concepts not only business goals and business processes, but also big analytics queries are considered as goals, and alternatives are explored and selections are made among the alternatives using trade off analysis. To illustrate and validate our approach, we use an automobile logistics example, then compare previous work.
2017 11th International Conference on Research Challenges in Information Science (RCIS)