| Business Process Reengineering represents a pervasive and important phenomenon in the fast-paced global economy, and has become an area of active interest and research in academics. However, reengineering in practice has been consultant-driven and problematic, as process redesign is only just beginning to advance from mysterious art to a learnable craft, much less an explainable, predictable, and replicatable science.;The focus of this dissertation research is upon the advancement of reengineering toward a science, with express objectives of increasing the explainability, predictability, and replicatability of process redesign, and alleviating practitioners' dependence upon the ever-growing external consulting industry. This research begins with a focused review of the reengineering literature. I also draw from a number of useful reference disciplines (e.g., Accounting, Artificial Intelligence, Industrial Engineering, Organizational Design) and an emerging Theory of Articulation to develop a model of organizational processes and their redesign through process transformation.;Operationalization is accomplished in a manner that enables computer-based representation and automated measurement of organizational processes, and substantial redesign knowledge is formalized in terms of a rulebase suitable for incorporation into a production system. These theory-building activities culminate in the logical design of a knowledge-based system for "intelligent" redesign support (named KOPeR).;The military procurement process is then selected for model instantiation and validation, and a multiple case study design is used to guide field research at two Navy procurement sites in Southern California. The field research also includes a funded action component, as the model is employed to guide the redesign of three Navy procurement processes.;Results from this field research include the diagnosis of five, serious shortcomings and flaws in the procurement process, and the induction of several domain-dependent and instance-specific process flow measures with strong diagnostic performance. The model also proves to be effective in terms of both explanation and prediction, and knowledge gained through its usage provides fresh evidence to dispel a number of myths and misconceptions that are prevalent in the practice of process redesign. This investigation also sets forth an agenda for future research along these lines. |
