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.process change, knowledge management, optimal control theory

The first step in transforming strategy from a hopeful statement about the future into an operational reality is to allocate resources to innovation and new product development (NPD) programs in a portfolio. Resource allocation and NPD portfolio decisions often span multiple levels of the organization's hierarchy, leading to questions about how much authority to bestow on managers and how to structure incentives for NPD. In this study, we explore how funding authority and incentives affect a manager's allocation of resources between existing product improvement (relatively incremental projects) and new product development (more radical projects). Funding may be either fixed or variable depending on the extent to which the manager has the authority to use revenue derived from existing product sales to fund NPD efforts. We find that the use of variable funding drives higher effort toward improving existing products and developing new products. However, variable funding has a subtle side effect: it induces the manager to focus on existing product improvement to a greater degree than new product development, and the relative balance in the NPD portfolio shifts toward incremental innovation. In addition, we highlight a substitution effect between explicit incentives (compensation parameters) and implicit incentives (career concerns). Explicit incentives are reduced as career concerns become more salient.innovation, new product development, authority, incentives, resource allocation, portfolio

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.knowledge-based resource capabilities, process change, workforce training, knowledge management, managerial systems, uncertainty

W e highlight many of the traditional research themes in the management of technology as well as research themes on emerging topics such as those that appear in this focused issue. The discussion demonstrates the breadth and multidisciplinary nature of management of technology as well as the variety of methods employed in management of technology research. We conclude by offering a list of research themes that are of particular interest to the Management of Technology Department of Production and Operations Management.

We consider a manager who invests in knowledge development of a product and a process design team as well as knowledge transfer between teams throughout a new product development (NPD) project. Knowledge development at a particular time (e.g., prototyping and experimentation) increases a team’s level of knowledge at that time. In contrast, the recipient’s benefits from knowledge transfer may be lagged because of the difficulties in articulating and documenting knowledge as well as the challenges regarding its interpretation and application. Over time, as each team embeds knowledge in the NPD project, the levels of product and process performance increase, thereby increasing the net revenue earned at the product launch time. In a key contribution to the literature, analytic conditions are given that characterize the dynamic rates at which knowledge development and knowledge transfer occur throughout the project. We show that the investment in knowledge development for each team and knowledge transfer between teams may be constant, front-loaded, back-loaded, U-shaped, or the peak rate may be delayed over time. As such, we show how concurrent engineering is optimally pursued throughout the NPD project.