Recent empirical literature describes an industry's clockspeed as a measure of the evolutionary life cycle, which captures the dynamic nature of the industry. Among other factors, the rate of new product development is found to be associated with an industry's clockspeed. Yet the notion of an industry clockspeed and the essential factors driving suitable decision making in this area have remained relatively unexplored. We develop a simple definition and a corresponding analytic model which explains the interdependent relationship between a firm's own new product development activities and an industry clockspeed. Results from the single firm model show the conditions under which particular firms have an incentive to accelerate their new product development activities. Moreover, we link the single firm's NPD clockspeed decisions to the industry level by creating appropriate metrics; which characterize different types of industries. Examples from high-tech industries such as the personal computer and aerospace industries are included to illustrate our findings. Our intention is not only to offer analytical insights into factors driving the clockspeed for these industries, but also to establish a fundamental structured decision making approach, thereby stimulating future research on this important topic.

A firm's ability to manage its knowledge-based resource capabilities has become increasingly important as a result of performance threats triggered by technology change and intense competition. At the manufacturing plant level, we focus on three repositories of knowledge that drive performance. First, the physical production or information systems represent knowledge embedded in the plant's technical systems. Second, the plant's workforce has knowledge, including diverse scientific information and skills, to effectively operate the technical systems. Third, the firm's managerial systems embody knowledge in the form of goals, reward systems, and control and coordination systems. Taken together, we consider the technical systems, workforce knowledge, and the managerial systems as the plant's knowledge-based resource capability. Two normative models are introduced offering insight on how plant performance is impacted by investments in workforce knowledge (training) or the technical systems (process change). The models explicitly recognize that the outcome of investments in knowledge-based change is uncertain due to factors including technical problems, worker resistance, and limited financial resources. Also, we recognize that workforce knowledge may be deployed to mitigate the outcome uncertainty encountered with process change. Investments in knowledge-based change cannot be fully understood in isolation of the managerial systems. In one model, the plant manager is motivated by an incentive system that rewards the realization of a threshold goal, whereas in the other model the incentive system emphasizes the realization of meeting a particular target goal. We also investigate the impact of the manager's view of uncertainty (her willingness to absorb risk), which is influenced by the managerial systems. Results show that different characterizations of the managerial systems have a profound effect on managerial behavior and plant-level performance.

Forces such as technology change and increased competition provide opportunities and challenges that drive a firm to continuously evaluate and modify its resource capabilities. As a consequence, a firm's process change strategy is of paramount importance for sustained manufacturing success. However, fundamental elements of process change strategy are not well understood. Long term performance benefits associated with potential process change alternatives are often unclear. Moreover, uncertainty exists regarding the actual benefits that may be attained from various types of process change. Critical issues impacting the proper implementation of process change are frequently underestimated or largely ignored. Therefore, despite the improved performance sought, process change often leads to lower productivity, excessive equipment downtime, and deterioration in quality. As the authors review the relevant empirical and normative literature, a framework emerges that characterizes the salient features of a firm's process change strategy. The underlying dynamics of process change are explored and strategies are discussed to reduce the short-term disruption and enhance the long-term gain. In particular, the authors demonstrate the importance of creating and applying knowledge to improve the outcome of process change. They describe managerial actions that can be taken to reduce various sources of uncertainty associated with process change. Moreover, they identify key contributions as well as limitations of the existing normative literature on process change. Insights from the empirical literature are given that both support elements of the existing normative models and provide direction for future normative research. Thus, the authors seek to aid practicing managers and researchers alike to better understand the full scope and implications of process change.

A model is introduced to guide a profit maximizing firm in its quest to enhance performance through process change. The key benefit sought from process change is a long term increase in effective capacity. However, realizing success from process change is not trivial. First, while process change may increase effective capacity in the long run, the disruptions during implementation typically reduce short term capacity. Second, competitive forces such as decreasing revenue streams and shrinking product life cycles complicate the implementation of process change. Third, while knowledge may enhance the ultimate benefits derived from process change, the correct timing and means of knowledge creation are difficult to discern. Lastly, a variety of trade-offs must be evaluated when selecting the particular process change to pursue. For example, choices range from hardware and software replacements to modification of manufacturing procedures. The model introduced here explicitly considers both the short term loss due to disruption and the long term gain in effective capacity associated with the process change. In addition, investments in the accumulation of knowledge are investigated for their potential to enhance process change effectiveness. Knowledge is generated from investment in preparation and training learning-before-doing and as a by-product of process change learning-by-doing. Analysis of the model provides managerial recommendations for several key decisions relating to process change implementation including: i the selection of an appropriate process change alternative, ii the rate and timing for investment in process change, and iii the rate and timing for investment in preparation and training. New results are reported reflecting the important relationship between process change and knowledge. For example, we show that under certain conditions, a firm should optimally delay investment in process change until sufficient accumulation of knowledge is achieved. More generally, we identify conditions whereby investment in process change occurs at an increasing rate over time. This result is particularly important since it demonstrates a limitation of the existing literature where process change always occurs at a decreasing rate.

Empirical literature defines an industry's clockspeed as a measure of the dynamic nature of the industry. Among other things, the rate of new product development is found to be associated with an industry's clockspeed. Using a simple analytic model, an optimal industry clockspeed is derived, and competitive dynamics are analyzed relative to the derived industry standard

Summary form only given. In this paper, the critical link between successful process change and knowledge creation is examined. A dynamic profit maximizing model is introduced to investigate the impact of knowledge creation that enhances process change effectiveness. We also explore the trade off between the long term increase in capacity sought and the short term disruption during implementation. We consider process change in terms of its effect on the maximum volume of output generated (effective capacity) over time. As a result, we can assess the impact of both process change and knowledge creation on the firm's ability to generate revenue from output over time. Overall, the firm's profit maximizing objective embodies:(i) the net revenue earned from effective capacity, and (ii) the costs of process change and preparation/training

Although firms must upgrade their manufacturing capabilities to remain competitive over time, successful process improvement implementation is uncertain. Furthermore, several internal characteristics such as the firm's size and level of knowledge affect the magnitude and the certainty of the benefits realized. A comprehensive stochastic model is introduced which yields managerial insights concerning the impact of firm size on appropriate process improvement and knowledge acquisition strategies. Analytic results are derived linking firm size to relevant cost and efficiency advantages. Manufacturing managers can utilize the model to tailor appropriate process improvement and knowledge acquisition strategies for their firms.