When Processes Learn: Steps Toward Crafting an Intelligent Organization

Dan Zhu, Michael J. Prietula and Wen Ling Hsu
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Dan Zhu: Department of Management Sciences, College of Business, University of Iowa, Iowa City, Iowa 52242
Michael J. Prietula: Department of Commerce and Technology, Johns Hopkins University, Baltimore, Maryland 21201
Wen Ling Hsu: AT&T Laboratories, Murray Hill, New Jersey 07974

Information Systems Research, 1997, vol. 8, issue 3, 302-317

Abstract: Two trends in information systems research provide an opportunity to add an additional link between information technology and organizational learning. First, there is an increasing penetration of information technology into the firm's processes and structures. Second, research in artificial intelligence has given rise to the first generation of fully computational architectures of general intelligence. In this research note we explore a melding of these two trends. In particular, we present the crafting of an organizational process which can learn, and develop and apply a new set of organizational learning metrics to that process. The process is a simplification of a complex, parallel-machine production scheduling task performed in a local manufacturing firm. The system, Dispatcher-Soar, generally supports a symbolic, constraint propagation approach based, in part, on the reasoning methods of the human scheduler at the firm. The implementation of this process is based on a dispatching rule used by the expert. The behavior of Dispatcher-Soar centered around a small case study examining the effects of scheduling volume and learning on performance. Results indicated that the knowledge gained can reduce within-trial scheduling effort. An analysis of the generated knowledge structures (chunks) provided insight into how that learning was accomplished and contributed to process improvements. As the knowledge generated was in a form standardized to a common architecture, metics were used to evaluate the production efficiency ((eta) prod ), utility ((eta) util ) and effectiveness ((eta) eff ) of the accumulated organizational knowledge across trials.

Keywords: information systems; organizational learning; artificial intelligence; machine learning (search for similar items in EconPapers)
Date: 1997
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