Product lifecycle-oriented quality and productivity improvement based on stream of variation methodology

Authors:
Shichang Du;Lifeng Xi;Jun Ni;Pan Ershun;C. Richard Liu
Affiliations:
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China;Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China;Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109, USA;Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China;School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
Venue:
Computers in Industry
Year:
2008

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Abstract

Manufacturers in the 21st century will face increased customization, product proliferation, shorter product lifecycle and development time, responsiveness, frequent and unpredictable market changes. To gain the competitive advantage, manufacturing companies must be able to analyze and predict product quality during the product design phase and identify root causes of all faults for productivity improvement during ramp-up time and production time. Therefore, a new manufacturing strategy, namely, stream of variation (SoV) methodology, has been proposed, developed and applied. The SoV methodology is a generic math model for modeling, analysis, prediction and control of product quality and productivity improvement in complex multistage manufacturing systems such as automotive, aerospace, appliance, and electronics industries. The methodology integrates multivariate statistics, control theory and design/manufacturing knowledge into a unified framework and can help in eliminating costly trial-and-error fine-tuning of new-product manufacturing processes throughout the product design and manufacturing. The related issues of the state-of-the-art practice, goals, benefits and future directions related to SoV methodology are discussed, which include rationale of SoV, state space model, root causes identification, etc. An application example is also provided.