Institute for Systems Research Technical Reports
Permanent URI for this collectionhttp://hdl.handle.net/1903/4376
This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.
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Item Design for Production: Using Manufacturing Cycle Time Information to Improve Product Development(2002) Chincholkar, Mandar; Herrmann, Dr. Jeffrey W.; ISRProduct development teams employ many methods and tools as they design, test, and manufacture a new (or improved) product. It is important that the product development team understand how their design decisions affect manufacturing system performance. Having this feedback early in the design process avoids re-work loops needed to solve problems of manufacturing capacity or cycle time. The team can incorporate this information and associated costs into a design decision problem aimed at choosing the best possible product design.It is clear that the product design, which requires a specific set of manufacturing operations, has a huge impact on the manufacturing cycle time. Reducing manufacturing cycle time has many benefits, including but not limited to lower inventory, reduced costs, improved product quality faster response to customer orders, increased flexibility and a reduced time-to-market.
Design For Production (DFP) refers to methods that evaluate a product design by comparing its manufacturing requirements to available capacity and estimating manufacturing cycle time. DFP can be used to design the product in a way that decreases required capacity, reduces the manufacturing cycle time, or otherwise simplifies production.
To understand how a product design impacts manufacturing system performance, this research develops analytical (not simulation) models to quantify how introducing a new product increases congestion in the manufacturing system. It presents approaches that use this information intelligently and make suggestions on product redesign and manufacturing system improvements. Similar models are also developed for manufacturing systems with process drift, a condition causing a process to deviate from expected processing parameters resulting in a reduced yield at that station. This work presents models for evaluating how embedding passives into a printed circuit board affects not only the processing times at each step in the manufacturing process but also the overall manufacturing system behavior. Finally, this dissertation demonstrates the importance of the DFP approach by presenting a comprehensive perspective on the economic impacts of reducing manufacturing cycle time. Through these models and relationships, this research aims to understand the issues and impacts associated with the design for production approach and provide better tools that improve product development.
Item Reducing Manufacturing Cycle Time during Product Design(1999) Herrmann, Jeffrey W.; Chincholkar, Mandar; ISRThis paper describes an approach that can reduce manufacturing cycle time during product design. Design for production (DFP) determines how manufacturing a new product design affects the performance of the manufacturing system. This includes design guidelines, capacity analysis, and estimating manufacturing cycle times. Performing these tasks early in the product development process can reduce product development time. Previous researchers have developed various DFP methods for different problem settings. This paper discusses the relevant literature and classifies these methods. The paper presents a systematic DFP approach and a manufacturing system model that can be used to estimate the manufacturing cycle time of a new product. This approach gives feedback that can be used to eliminate cycle time problems. This paper focuses on products that are produced in one facility. We present an example that illustrates the approach and discuss a more general approach for other multiple-facility settings.