A data-driven, goal-oriented framework for process-focused enterprise re-engineering

Along with enterprise transformation, enterprise re-engineering is essential for maintaining the competitiveness of an enterprise. Enterprise re-engineering addresses (emergent) changes, re-organizing, outsourcing and re-aligning alike. Re-engineering itself has drawn traction in both academia and business. Most scholarly work in this area is confined to model-driven analysis, holistic frameworks for analyzing as-is/to-be enterprise models, and a few other conceptualization techniques. The practice of process redesign understandably takes the stage in re-engineering. Yet algorithmic techniques that insightfully point out how a process might be improved for proactively re-engineering process-intensive enterprise architecture are missing. Data science and business intelligence have brought a refreshingly new analysis to this mainstream problem by studying the operational history of a business process to facilitate most plausible changes. In this article, we investigate enterprise process redesign taking into account enterprise’s high-level strategy and data warehouse. More specifically, we propose an approach to reasoning about an enterprise’s strategy together with data mining rules extracted from the data warehouse of the enterprise. Our redesign algorithms suggest design-time changes to be made to its business processes, primarily by eliminating redundant tasks and re-ordering inefficiently-located tasks. We analyze the effectiveness of candidate to-be business processes with regard to business intelligence indicators. We report our work on the enterprise architecture developed for a retailer of low-cost domestic flights.