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AI Magazine Volume 15 Number 4 (1994) (© AAAI)
Workshop Report
AI in Business-Process Reengineering
Walter Hamscher
s Business-process reengineering (BPR) is a generic term covering a variety of perspectives on how to change organizations. There are at least two distinct roles for AI in BPR. One role is as an enabling technology for reengineered processes. A second, less common but potentially important role is in tools to support the change process itself. The Workshop on AI in Business-Process Engineering, held during the national AI conference, allowed participants to learn about projects that are aimed at exploiting insights from AI.
V
irtually any business can be viewed as a collection of processes that, taken together, respond to customer demands by inventing, producing, delivering, and billing for goods and services. These processes vary from business to business, but in the over whelming majority of cases, these processes and the organizations that execute them have not been engineered in any meaningful sense; they have evolved over time in response to their business environments. Changing environments frequently destroy such companies unless they make a conscious and periodic, if not continuous, effort to reengineer these processes to exploit changes in suppliers, customer needs, and technological innovation. Viewing a business as a collection of customer-driven processes is the essence of businessprocess reengineering (BPR), a generic term covering a variety of perspectives, none of which is particularly rigorous, on how to change organizations. It is easy to dismiss BPR as hype, a management consultant’s marketing slogan, but the phenomenon is real and extremely important. In 1993, 60 percent of the management letters appearing with
Fortune 500 company annual reports explicitly discussed reengineering efforts that were currently under way. One analyst recently estimated the annual market for BPR services in U.S.-based companies at $1.8
Workshop Report
AI in Business-Process Reengineering
Walter Hamscher
s Business-process reengineering (BPR) is a generic term covering a variety of perspectives on how to change organizations. There are at least two distinct roles for AI in BPR. One role is as an enabling technology for reengineered processes. A second, less common but potentially important role is in tools to support the change process itself. The Workshop on AI in Business-Process Engineering, held during the national AI conference, allowed participants to learn about projects that are aimed at exploiting insights from AI.
V
irtually any business can be viewed as a collection of processes that, taken together, respond to customer demands by inventing, producing, delivering, and billing for goods and services. These processes vary from business to business, but in the over whelming majority of cases, these processes and the organizations that execute them have not been engineered in any meaningful sense; they have evolved over time in response to their business environments. Changing environments frequently destroy such companies unless they make a conscious and periodic, if not continuous, effort to reengineer these processes to exploit changes in suppliers, customer needs, and technological innovation. Viewing a business as a collection of customer-driven processes is the essence of businessprocess reengineering (BPR), a generic term covering a variety of perspectives, none of which is particularly rigorous, on how to change organizations. It is easy to dismiss BPR as hype, a management consultant’s marketing slogan, but the phenomenon is real and extremely important. In 1993, 60 percent of the management letters appearing with
Fortune 500 company annual reports explicitly discussed reengineering efforts that were currently under way. One analyst recently estimated the annual market for BPR services in U.S.-based companies at $1.8
References: ful change, for example, by helping to anticipate the reactions of process participants to proposed changes. Eric Yu and John Mylopoulos (University of Toronto) presented work on modeling organizations using a multilevel framework in which one level, the actor dependency model, makes the relationships between actors explicit in terms of their dependence on other actors to achieve their goals. In a somewhat different vein, Gary Klein (MITRE Center for Advanced Aviation System Development) presented work that explicitly models the complex behavior of individual actors within a changing business process; in particular, the tendency for individuals to adapt over time to changes in the sources and quality of information that they use to make their decisions. More generally, using rich repCohen, P., and Levesque, H. 1990. Intention Is Choice with Commitment. Artificial Intelligence 42(3): 213–262. Caldwell, Bruce. 1994. Missteps, Miscues. Information Week 480 (20 June): 50–60. Slade, S. 1991. Goal-Based Decision Strategies. In Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society. Chicago, Ill.: Cognitive Science Society. Yu, D. 1991. Achieving Excellence in the Global Marketplace Using KnowledgeBased Simulation. In Proceedings of the First International Conference on AI Applications on Wall Street, 103–108. Washington, D.C.: IEEE Computer Society Press. Walter Hamscher is affiliated with the Price Waterhouse Technology Centre in Menlo Park, California. 72 AI MAGAZINE