Knowledge that Transforms

The future of the global industry lies in the continuous improvement of both products and processes, a renewed commitment to competition, and an aggressive approach to satisfying customers needs in quality, quantity, and timing. In quality management, the degree of customer satisfaction for a given product may be measured in the form of the loss to society. This loss is formulated as a function of the deviation from the target for each of the product's quality characteristics. The greater the variability of uncontrolled factors during manufacturing or production the larger will be that loss. In this paper, we develop a form of the loss function that takes into account the variability of a production process, the decision loss, and the costs of sampling and inspection. Specifically, we consider monitoring a production process, which may undergo continuous mean shift and variance deterioration during a production run. We then examine decision rules for continuing production or stopping and adjusting the production process.

Testing and cross‐validation of theories and paradigms are necessary to advance the field of manufacturing strategy. When the findings of one study are also obtained in other studies, using entirely different databases, we become more confident in the results. Replication alleviates concerns about spurious results and is one motivation for this study. We examine aspects of the tradeoffs concept, production competence paradigm, and a manufacturing strategy taxonomy framework. In regard to the tradeoffs concept, we found evidence of tradeoffs between some, but certainly not all, manufacturing capabilities of quality, cost, delivery, and customization. The relationships get sharper when controlling for process choice. For example, the tradeoff between cost and customization is particularly strong between plants that have different process choices. We find that such tradeoffs can change, or even disappear, however, once the process choice is in place. With respect to the production competence paradigm, our analysis shows a statistically significant correlation between production competence and operations performance in batch shops, but not in plants with other process choices. Finally, using variables similar to those of Miller and Roth, our data produced three similar clusters even though their unit of analysis was much more macro than ours. Controlling for process choice is consistent with the current manufacturing strategy literature that emphasizes dynamic development of capabilities within the context of path dependencies. A major argument of this strand of research is that operations decisions not only affect current capabilities, but also set the framework for development of capabilities in the future. That being the case, controlling for process choice (or other factors such as industry or markets) should contribute to the understanding of capability‐development paths adopted by different manufacturing plants. In short, we found at least partial support for each of the theories examined here, even though the theories seem on the surface to be contradictory and mutually exclusive. Controlling for process choice or other measures of dependency goes a long way in uncovering consistency across different theories and empirical studies in operations management.