Redesigning Reengineering Through Measurement-Driven Inference

KOPeR is developed to automate three key intellectual activities required for process redesign--process measurement, pathology diagnosis, and transformation matching. This KBS tool is used in the laboratory to redesign a commercial process from the reengineering literature and then employed in the field to redesign operational procurement processes in the context of an "industrial strength" reengineering project. The study finds that KOPeRsupported redesign enables new reengineering efficiencies in terms of direct automation effects and indirect knowledge effects. Results of this investigation highlight new opportunities available to the IS manager--such as improving the return on investment from BPR, enhancing the capability for knowledge management and organizational memory, and achieving competitive advantage through knowledge integration--opportunities that do not necessarily require KBS automation to seize. This investigation also lays a research cornerstone and foundation for development of process redesign theory and investigation of reengineering effectiveness. This first decade of business process reengineering (BPR) is blemished by sporadic success, pathological performance, and inefficiency. Reengineering inefficiency is driven in part by cost and cycle time for process redesign, a process itself that requires deep reengineering knowledge and specialized expertise. However, such knowledge and expertise are not addressed by extant, first-generation redesign tools, so these intellectual activities must be performed manually at present, or provided through expensive BPR consulting services. Knowledge-based systems (KBS) address the requirements for knowledge and expertise directly, and they can augment firstgeneration tools to reduce redesign cost and cycle time, and hence increase reengineering efficiency. This study employs the methods and tools of reengineering recursively, to redesign the process of process redesign itself. Using measurement-driven inference, a second-generation KBS redesign tool called Sirkka Jarvenpaa was the accepting senior editor of this paper.