METRICS: a system architecture for design process optimization
Proceedings of the 37th Annual Design Automation Conference
Process-Integrated Learning: The ADVISOR Approach for Corporate Development
LSO '01 Proceedings of the Third International Workshop on Advances in Learning Software Organizations
Incremental Network Optimization: Theory and Algorithms
Operations Research
On-line SPC with consideration of learning curve
Computers and Industrial Engineering
Mitigating Supply Risk: Dual Sourcing or Process Improvement?
Manufacturing & Service Operations Management
Single machine scheduling with autonomous learning and induced learning
Computers and Industrial Engineering
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Continuous improvement (CI) unceasingly strives to improve the performance of production and service firms. The learning curve (LC) provides a means to observe and track that improvement. At present, however, the concepts of CI are abstract and imprecise and the rationale under pinning the LC is obscure. For managers to improve processes effectively, they need a more scientific theory of CI and the LC. This paper begins to develop such a theory. Our approach is based on learning cycles, that is, in each period management takes an action to improve the process, observes the results, and thereby learns how to improve the process further over time. This analysis suggests a differential equation that not only characterizes continuous improvement but also reveals how learning might occur in the learning curve. This differential equation might help management to evaluate the effectiveness of various procedures and to improve and enhance industrial processes more quickly.