This paper conceptualizes the process of innovation implementation in high technology manufacturing, a natural setting of multiple and ongoing innovation implementation. Building on the developments in organizational learning theory, we frame the process of innovation implementation in high technology manufacturing as a problem of balancing between exploitation and exploration. Through the application of a logistic difference equation, we provide insights into the dynamics of balancing between exploitation and exploration, and show that innovation implementation in high technology manufacturing can be conceptualized as a chaotic process, in a scientific sense. Using time series data from a wafer fabrication plant, the high technology manufacturing plant that served as our research site over a period of 125 weeks, we test this conceptualization. We find empirical support for the conceptualization of innovation implementation in high technology manufacturing as a chaotic process. We discuss the managerial implications of our study's findings, and the directions for the future research. q

This article examines demand, manufacturing, and supply factors proposed to inhibit manufacturer delivery execution. Extant research proposes many factors expected to harm delivery performance. Prior cross‐sectional empirical research examines such factors at the plant level, generally finding factors arising from dynamic complexity to be significant, but factors arising from detail complexity to be insignificant. Little empirical research examines the factors using product‐level operating data, which arguably makes more sense for analyzing how supply chain complexity factors inhibit delivery. For purposes of research triangulation, we use longitudinal product‐level data from MRP systems to examine whether the factors inhibit internal manufacturing on time job rates and three customer‐oriented measures of delivery performance: product line item fill rates, average delivery lead times, and average tardiness. Our econometric models pool product line item data across division plants and within distinct product families, using a proprietary monthly dataset on over 100 product line items from the environmental controls manufacturing division of a Fortune 100 conglomerate. The data summarize customer ordering events of over 900 customers and supply chain activities of over 80 suppliers. The study contributes academically by finding significant detail complexity inhibitors of delivery that prior studies found insignificant. The findings demonstrate the need for empirical research using data disaggregated below the plant‐level unit of analysis, as they illustrate how some factors previously found insignificant indeed are significant when considered at the product‐level unit of analysis. Managers can use the findings to understand better which drivers and inhibitors of delivery performance are important.

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We conceptualize strategic decision-making processes within a manufacturing firm as streams of resources allocated to short- and long-term changes. The analogous ecological model, referred to as the Lotka-Volterra model, captures this dynamic tension between decisions made by the firm and its manufacturing operations. This representation leads to evolutionarily stable manufacturing strategies (ESMSs), which contribute to a firm's competitive advantage in different ways. Using a random sample of 30 firms from the U.S. semiconductor industry, we estimate parameters of the model and arrive at four ESMSs or strategic manufacturing groups that reflect theoretically and empirically distinctive adaptation patterns through their dynamic resource allocations. We observe that a majority of the firms were classified in one of the four groups, with relatively fewer firms in the other three. Notably, our classification based on ecology models agrees well with taxonomies in manufacturing and business strategy theory. Furthermore, our analysis shows significant differences among manufacturing practices and competitive capabilities of the four strategic groups. Managerially, these insights could provide the foundation to implement strategic changes that enable firms to leapfrog from one ESMS to another. This study also paves the way for development of a meso theory of the dynamics of manufacturing strategy.manufacturing strategy, resources, dynamics, competitive capabilities, ecology, complexity