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Global competition, rapid changes in technology, and market fragmentation have resulted in shorter product life cycles. In order to remain viable, it is increasingly important for firms to introduce new products frequently. Product design is a complex process that involves coordination of activities among several functional disciplines within the company as well as the customers and the suppliers. Traditionally, the information flow among the various product development stages has been sequential. However, there is increasing evidence to suggest that an integrated approach that considers several stages simultaneously may be superior.This paper provides a decision support tool for implementing such an integrated approach. On the basis of given customer preferences, the paper presents a model for determining the number of new products to be introduced, the exact specifications of these products, and the production processes for efficiently delivering these specifications. These decisions are made in an integrated manner by simultaneously considering the interaction among the various choice variables. A decomposition-based solution procedure is developed that iterates between the product design and process selection decisions while maintaining an effective link between them.In addition to understanding the economic value of adopting the integrated approach to product design, the paper discusses how the proposed model can be used effectively to perform sensitivity analysis with respect to some of the important decision variables.

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Dynamic scheduling of manufacturing systems for due date based objectives has received considerable attention from practitioners and researchers due to the importance of meeting due dates in most industries. Research investigations have focused primarily on the relative effectiveness of various dispatching rules in job shops. These rules operate by prioritizing jobs using a “criticality index” based on job and system status. Jobs are then scheduled from most critical to least, with the indexes typically being updated as the system changes. This study considers two important issues which have not been addressed previously in the literature. First, we investigate the impact of unequal machine workloads on the relative effectiveness of dispatching rules. This is significant because workloads are likely to be unbalanced in most real systems. While it is clear, intuitively, that this imbalance in machine workloads is likely to deteriorate system performance, it is not obvious whether the superiority of certain dispatching rules established in earlier studies for balanced workloads is carried forward to this case. We show that the performance of different dispatching rules does indeed depend upon the degree of workload imbalance. We also propose and test a scheduling procedure which performs well in both balanced and unbalanced systems. Next, we develop a scheduling approach which shows promise as being an improved alternative to the use of dispatching rules. This approach decomposes the dynamic scheduling problem into a series of static problems. These static problems are then solved using an optimum‐seeking method, and the solutions are implemented on a rolling basis. We show through a simulation experiment that adopting this approach over dispatching rules leads to an improvement in the overall solution quality, even in a dynamic environment. The two very practical implications of our study are: (1) that commonly used dispatching rules in job shops or automated manufacturing systems may not be the best approach when capacity utilization is unbalanced (2) a job shop or automated manufacturing system would likely benefit from implementing optimal‐seeking scheduling rules instead of the traditional job dispatching rules.