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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) [...]

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. Index Terms--Dynamics, implementation and disruption, knowledge management, managerial practice, process change, uncertainty.

A model is presented for the planning and scheduling of production batches in a flexible manufacturing system environment in which setup costs and times are nonnegligible and alternate routings are possible. The formulation is an integer program with a multicommodity flow network structure. A heuristic procedure based on price-directive decomposition using column generation is used to obtain solutions. Numerical experimentation is performed to assess the quality of the heuristic versus optimal solutions, and to determine the impact of routing flexibility on total cost, inventory levels, bottlenecks, capacity utilization, throughput time, number of setups and split lots. Important cost-benefit trade-off implications are shown for the design of flexible manufacturing systems.

: A multiproduct, multiperiod, multistage network model is presented for the planning of ord release and production scheduling in a flexible manufacturing system environment under the existence alternate routings. It is assumed that premanufacturing decisions such as machine grouping and tool loading have been made, so that setup costs and setup times are negligible and can be included in th processing times. The decision process addressed by the model is the disaggregation of weekly produc requirements to daily production requirements, the determination of production batch sizes for each operation of each part type, and the daily assignment of each batch to machine groups given the flexibility of alternate routings. The model also provides the interface and linkage between an MRP component planning system and the shop scheduling system. The model is solved using a price-directiv decomposition technique with column generation. Experimentation is performed with the model for varying problem sizes to determine the impact of shop flexibility on total cost, inventory levels, e of bottlenecks, shop utilization, and the number of setups and split lot production. The results ind important cost-benefit trade-off implications for system design and acquisition. For example, if in setup costs and times are nonnegligible, then it is shown that increasing the routing flexibility of without a parallel decrease in setup costs and times is unlikely to reap significant benefits., A multiproduct, multiperiod, multistage network model can enhance routing flexibility and production scheduling in a flexible manufacturing system. Assuming the existence of altrnate routings and no setup time considerations, this model breaks down weekly production requirements to daily total production needs, sets the daily production quota for each operation of each part type and allocates the production quotas to machine groups given the flexibility of alternate routings.

Rapid changes in technology and intense competition have led to shorter product life cycles in both high-technology industries and industries not commonly regarded as high-technology. As a result of short product life cycles, firms are faced with the challenge of planning for facilities whose useful lives are much longer than the life cycle of any individual product it manufactures. This paper develops a model to analyze the critical trade-offs associated with determining the best level of flexibility for a facility that manufactures a series of high-volume products, sequentially over each product's life cycle. Our analysis considers the benefits associated with more flexible facilities including reduced changeover costs and earlier market entry, versus the higher initial investment and higher production costs typically encountered. Keywords: Capacity planning flexibility; product life cycle, Increasingly shorter production life cycles have reached the point where the life cycles are shorter than the manufacturing facility life cycle. This requires that manufacturing facilities be endowed with the capability to produce other products with the minimum of modifications or costs. A model was developed for the evaluation of the optimum level of production machinery flexibility based on the product life cycle. The model considers other factors such as product values, average production costs, changeover costs and discount rates.

SETHI, SURESH P., GERALD L. THOMPSON. 2000. Optimal Control Theory: Applications to Management Science and Economics, 2nd ed. Kluwer Academic Publishers, Dordrecht, The Netherlands. 528 pp. $135.00. Optimal Control Theory, [...]

The successful launch of an innovative product or service is driven by an unexplored market opportunity or a technological breakthrough. Oftentimes, however, firms lack the necessary resource capabilities to exploit new market opportunities; alternately, markets may not exist for firms to successfully profit from technological breakthroughs. In other words, firms suffer from performance gaps between their market opportunities and their resource capabilities. In this study, we organize the growing literature on knowledge management to develop the concept of a knowledge value chain, which we characterize as the transformative engine by which a firm can close such performance gaps. In addition to motivating the framework through case studies and placing the existing research literature in knowledge management within this framework, we also identify several directions of future research. [ABSTRACT FROM AUTHOR]

Given the dynamic finished good requirements, a model is presented to derive the optimal levels of inventory, backlogging, and machine loading in a multi-stage, multi-period production environment characterized by non-identical machines in each stage. Dynamic capacity constraints on the level of inventory, backlogging, and machine loading are included. Furthermore, capacities and operating costs of machines performing the same tasks at any stage in the production process are permitted to vary in order to consider the individual characteristics of machines. Therefore, machine loads are obtained for non-identical machines capable of performing the same operations. The model is represented as a capacitated network with linear costs and is quickly solved using a standard network code. [ABSTRACT FROM AUTHOR]

We analyze a firm's investment in environmental process improvement (EPI) to reduce the environmental impact (EI) of its manufacturing processes in relation to various internal firm characteristics and in response to different external regulatory drivers. We provide a deep understanding of how these internal and external forces cause the firm to pursue EPI earlier or later in the planning horizon and at an increasing or a decreasing rate over time. In particular, we show how a regulator can drive different patterns of EPI over time through subsidies for EPI or penalties for EI. We also explore the impacts of two key operational capabilities of the firm—the production‐cost efficiency of EPI and the effectiveness of EPI in reducing EI—on the rate of EPI over time. We demonstrate that improvements in these operational capabilities contrastingly alter the timing of investments in EPI. Lastly, we demonstrate that a firm capable of leveraging EPI to enhance product functionality or command a reputational premium in the marketplace pursues a remarkably different pattern of EPI over time compared to a cost‐focused firm that only responds to regulatory forces. [ABSTRACT FROM AUTHOR]