The Computer Integrated Manufacturing Laboratory is a constituent laboratory of the Institute for Systems Research at the University of Maryland.
This material is based upon work supported by the National Science Foundation under Grant Number DMI 9713718. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Dr. Chincholkar performed this work as a graduate research assistant at the University of Maryland.
Successful new product development requires the ability to predict, early in the product development process, the life-cycle impacts of a product design. Ignoring downstream issues (or producing poor estimates) leads to poor decisions and product designs that cause unforeseen problems. These products must be redesigned. Accurate predictions allow a product development team to create a superior design that performs satisfactorily in all ways. This reduces the number of redesign iterations, the time-to-market, and the development costs. Consequently, manufacturing companies (and solution providers) have developed many design decision support tools that form the class of Design for X (DFX) methodologies. An extremely important, though often overlooked, issue during product design is the performance of the manufacturing system at all levels, from supply chain to production line. The performance of these systems is disregarded because it is considered hard to model and designers don't know much about the manufacturing system. However, practical manufacturing system models are becoming more available. Moreover, the rapid introduction of new products means that existing facilities outlive new products. Instead of designing the manufacturing system around the product, the product must be designed to fit the facility.
Design for Production (DFP) refers to methods that evaluate manufacturing system performance. For example, does the production line have enough capacity to achieve the desired production rate? How long will it take the factory to complete customer orders? How much inventory will be required to maintain superior customer service in an international supply chain? Answering such questions requires information about product design, manufacturing requirements, and production quantities along with information about the manufacturing system that will create the product.
DFP, like Design for Manufacture (DFM) and Design for Assembly (DFA), is related to the product's manufacture. In general, DFM and DFA evaluate the materials, the required manufacturing processes, and the ease of assembly. That is, DFM and DFA study the feasibility and cost of manufacturing the product at the operation level. On the other hand, DFP evaluates manufacturing system performance at the production line, factory, or supply chain level. Like DFM and DFA, DFP can lead a product development team to consider changing the product design to avoid problems or improve profitability. In addition, DFP can provoke suggestions to improve the manufacturing system.
The following Arena simulation models simulate a flow shop with three stations. The first workstation undergoes process drift, which affects the number of good and bad parts that it produces. The second workstation inspects the parts and discards the bad ones. The third workstation processes the good parts. In the second model, the inspection station is last.
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Last updated by Jeffrey W. Herrmann, May 20, 2004.
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