This exploratory research on a decade of Total Quality Management in one factory opens up the black box of the learning curve. Based on the organizational learning literature, we derive a quality learning curve that links different types of learning in quality improvement projects to the evolution of the factory's waste rate. Only 25% of the quality improvement projects---which acquired both know-why and know-how---accelerated waste reduction. The other 75% of the projects either impeded or did not affect waste reduction. In complex and dynamic production environments, locally acquired knowledge is difficult to disseminate. The combination of know-why and know-how facilitates its dissemination.learning-curve, organizational learning, quality, TQM, technological knowledge, experimentation, knowledge transfer

Little is known about the processes that make TQM effective. Why are some quality improvement projects more effective than others? We argue that TQM processes affect the way people create new knowledge, which in turn determines organizational effectiveness. We explore this by studying 62 quality improvement projects undertaken in one factory over a decade. Using a factor analysis we identify three learning constructs that characterize the learning process: scope, conceptual learning, and operational learning. We use OLS regressions to study the impact of these learning constructs on project performance. Conceptual and operational learning are found to play a crucial role in achieving goals, creating new technological knowledge, and changing factory personnel's attention. Contrary to the common practice of relying on operational learning, we suggest the application of conceptual learning as well, particularly if the technology is poorly understood. It facilitates the codification of knowledge, which enhances its dissemination for both present and future use.Quality, Organizational Learning, Technological Knowledge, Learning by Experimentation

Can a firm accelerate its learning curve if knowledge about the production function is incomplete? This article identifies a production line specifically set up to create technological knowledge about its production function through scientific experimentation (formal learning) as opposed to learning by doing. The organizational structure of this line was very successful in creating technological knowledge. Formal learning resulted in huge productivity improvements. Replication of this organizational structure on three production lines in other plants within the same firm fell short of expectations. Formal learning did not result in similar productivity improvements. Our research suggests two factors that may facilitate creation and transfer of technological knowledge: management buy-in and knowledge diversity to solve interdepartmental problems.Learning Curve, Total Factor Productivity, Learning by Doing, Formal Learning, Technological Knowledge, Knowledge Transfer, Replication

Several articles have been written during the past few years examining performance improvement paths and various forms of efficiency frontiers in operations strategy. These articles focus primarily on defining and describing these frontiers and raise questions concerning how to improve operations. In this paper, we provide one of the first empirical studies aimed at validating these earlier studies. Using a database on the 10 largest U.S. airlines for a period of 11 years, we test and validate some of the models presented in the operations literature. The 10 major airlines are separated into 2 groups for analysis: geographic specialists and geographic generalists. Our analysis shows that better performing airlines (in terms of cost‐quality position) in both groups confirm the predictions of the sand cone model when operating further away from their asset frontiers, although trade‐offs do occur when operating close to asset frontiers.

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