Knowledge that Transforms

Design of experiments has been and continues to be employed successfully for the robust design of products/processes. A principle contention in these areas of application concerns whether or not interactive effects should be estimated. The arguments, for and against, hinge on an experimenter's understanding of the interaction concept and on the value added that is derived from the increased effort required for assessing such effects. We propose an interactive graphics approach that assesses whether and which interactive effects to estimate. Our methodology yields several additional benefits to the experimenter that include prototype simulation and unique learning opportunities for augmenting process knowledge in post‐analysis.

We model choice of dispatching rules in real time (system state dependent) as a pattern recognition problem, using a modified version of Data Envelopment Analysis. A data base of system state and performance values is created from extensive simulation, and this data base is used to train the pattern‐recognition model. Our results show that the model is very effective in choosing a mix of dispatching rules over a period of time, varying the mix with system objectives, and performing better than the strategy of using fixed rules. We show how “If‐Then” decision rules can be created from the model and portrayed in a decision‐tree‐like diagram. Since such decision rules are based on rigorous mathematical foundations, optimization will be ensured in our approach.

In this paper we present the results of an empirical study of the manufacturing sites of foreign electronics companies in Singapore and Taiwan. The manufacturing and R&D capabilities at the sites are assessed. The way in which host government policies interact with R&D development at the sites and the speed of the R&D development are also considered. The results suggest that Singapore and Taiwan are now host to some advanced manufacturing and R&D operations. We show, however, that the product design work at the sites visited is never leading edge for the companies concerned. The speed at which product design responsibilities were designated to the sites is shown to be rapid when compared to an earlier study of foreign sites in the United Kingdom. We indicate areas in which national strategies in Singapore and Taiwan may have contributed to this faster development. In Singapore, we identify tax holidays and R&D grants as contributing to the pace of development.

Price and design quality define value for customers and are often used by firms to position products in the marketplace. Setting price and quality level on a new product for the first time and making appropriate changes over time to these variables to reflect changing conditions in the market requires careful coordination of design, manufacturing, and marketing variables. We present a control theoretic model to study the complex interaction among price, quality, and cost during the life cycle of a product. Our model considers the major design‐manufacturing‐marketing tradeoffs and helps determine optimal pricing, design quality, and production strategies in a dynamic environment with convex production costs.

We study the dynamic routing problem for a flexible manufacturing system consisting of two unreliable machines and a finite buffer. One product‐type is produced which requires two operations in sequence. The demand rate is assumed to be constant. Each machine is capable of performing both operations. The objective is to trace the demand while keeping the work‐in‐process low and the cycle‐time short. An optimal control formulation is established for the dynamic routing problem. A production flow control algorithm is developed based on a combination of mathematical modeling and heuristics. The control policy is simulated and a comparison with the numerical optimal solution shows that it performs well for the instances under consideration.

This paper deals with a manufacturing system consisting of a single machine subject to random failures and repairs. The machine can produce two types of parts. When the production is switched from one part type to the other, a random setup time is incurred at a constant cost rate. The objective is to track the demand, while keeping the work‐in‐process as close as possible to zero for both products. The problem is formulated as an optimal stochastic control problem. The optimal policy is obtained numerically by discretizing the continuous time continuous state opti‐mality conditions using a Markov chain approximation technique. The discretized optimality conditions are shown to correspond to an infinite horizon, discrete time, discrete state dynamic programming problem. The optimal setup policy is shown to have two different structures depending on the parameters of the system. A heuristic policy is proposed to approximate the optimal setup policy. Simulation results show that the heuristic policy is a very good approximation for sufficiently reliable systems.