Continuous process improvement through inductive and analogical learning

This article presents a methodology for the continuous detection and definition of process performance improvement opportunities, especially as these pertain to the quality of operations (such as product quality). The problem is first reduced to an essentially pattern recognition formulation, for which an integrated and adaptive methodology combining analogical reasoning and symbolic induction is developed. The resulting classification of past records of data is used to support the construction of a decision support system for the generation/selection of operating suggestions leadin to performance improvements. The overall approach complements the usual set of statistical tools, commonly employed to address quality management problems. The basic methodology is also extended to handle fuzzy class definitions and function learning formulations. Case studies, covering both simulated and real industrial situations, illustrate the concepts and their practical utility.