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, metrics were used to evaluate the production efficiency (η<sub>prod</sub>), utility (η<sub>util</sub>), and effectiveness (η<sub>eff</sub>) of the accumulated organizational knowledge across trials.
Existing algorithmic models fall to produce accurate software development effort estimates. To address this problem, a case-based reasoning model, called Estor, was developed based on the verbal protocols of a human expert solving a set of estimation problems. Estor was then presented with 15 software effort estimation tasks. The estimates of Estor were compared to those of the expert as well as those of the function point and COCOMO estimations of the projects. The estimates generated by the human expert and Estor were more accurate and consistent than those of the function point and COCOMO methods. In fact, Estor was nearly as accurate and consistent as the expert. These results suggest that a case-based reasoning approach for software effort estimation holds promise and merits additional research.