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The International Journal of Flexible Manufacturing Systems, 16, 11–44, 2004 c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.

Time-Based Competition in Multistage Manufacturing: Stream-of-Variation Analysis (SOVA) Methodology—Review D. CEGLAREK darek@engr.wisc.edu W. HUANG huang@cae.wisc.edu S. ZHOU szhou@engr.wisc.edu Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI 53706-1572, USA Y. DING YuDing@iemail.tamu.edu Department of Industrial Engineering, Texas A&M University, College Station, TX 77843, USA R. KUMAR Y. ZHOU Dimensional Control Systems, Inc., Troy, MI 48084, USA kumarr@3dcs.com yzhou@3dcs.com

Abstract. Frequency of model change and the vast amounts of time and cost required to make a changeover, also called time-based competition, has become a characteristic feature of modern manufacturing and new product development in automotive, aerospace, and other industries. This paper discusses the concept of time-based competition in manufacturing and design based on a review of on-going research related to stream-of-variation (SOVA or SoV) methodology. The SOVA methodology focuses on the development of modeling, analysis, and control of dimensional variation in complex multistage assembly processes (MAP) such as the automotive, aerospace, appliance, and electronics industries. The presented methodology can help in eliminating costly trial-and-error fine-tuning of new-product assembly processes attributable to unforeseen dimensional errors throughout the assembly process from design through ramp-up and production. Implemented during the product design phase, the method will produce math-based predictions of potential downstream assembly problems, based on evaluations of the design and a large array of process variables. By integrating product and process design in a pre-production simulation, SOVA can head off individual assembly errors that contribute to an accumulating set of dimensional variations, which ultimately result in out-of-tolerance parts and products. Once in the ramp-up stage of production, SOVA will be able to compare predicted misalignments with actual measurements to determine the degree of mismatch in the assemblies, diagnose the root causes of errors, isolate the sources from other assembly steps, and then, on the basis of the SOVA model and product measurements, recommend solutions. Key Words: variation reduction, quality, root cause identification, manufacturing systems

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Introduction: Time-based-competition—New paradigm and challenges

The US automotive industry has dominated world auto markets for years. The mass production paradigm, initiated by Henry Ford and Frederick W. Taylor, has been the most powerful tool for the United States in global markets for almost half of the last century. However, the landscape has shifted dramatically from the old world of mass production, which was characterized by few standardized products, homogeneous markets, and long product life

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cycle and development lead time. A new standard is gradually emerging in which increased customization, product proliferation, heterogeneous markets, shorter product life cycle and development time, responsiveness, and related factors are increasingly becoming key features (Bollinger, 1998; Gervin and Barrowman, 2002). For example, in Japan, Toyota was reportedly offering customers five-day delivery from the time the customer designed a customized car on a CAD system (from modular options) to the actual product delivery. One of the characteristic features of the automotive industry is the frequency of model change and vast amounts of time and cost that are required to make a changeover. This trend has continuously gone up in the last two decades. Since 1980s, US car market’s total demand has essentially remained stable, but the number of nameplates has increased by 35% from 139 to 183, respectively. This increase continued during the 1990s...
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