How Quality Engineering in Japan Affects Global Product Performance

  • Rozzeta Dolah
  • Zenichi Miyagi
Conference paper


Quality Engineering (QE) is the most significant essence in Japan for product and process development. There is not much to argue about how Japan has become a world-leading country in various sectors, from automotive to semiconductor, from medicine to agriculture, and from macro to nano device fabrication. The most prominent reason that the rival would chase after is how Japan sustains its quality to zero defects. That is called Quality Engineering that the Japanese industry culture inherited since the birth of world-quality guru, the late Dr. Genichi Taguchi. This paper presents how QE is being practiced to design quality into products that provides the ability to produce high-quality, low-cost products that fully satisfy customer needs. The findings are useful for disseminating and improving quality practically by learning from Japan to achieve world-class quality product. The benefit from QE implementation is described from variation improvement that certainly increases the product and process performance. QE implementation is presented in two ways. Firstly, from an industrial environment in which a company has successfully implemented QE and become one of the world’s QE benchmarks. Secondly, QE implementation is explained practically by a case study of an optimization for T-peel adhesion test. Optimum condition achieved from QE method is tensile weight 4 g, peel angle 60°, peel speed 12 mm/s, data region 30 %, spring thickness 0.4 mm, module of spur gear 1.0, and drum diameter 40 mm. Good reproducibility is achieved which is less than 30 % difference between estimation and confirmation value. Finally, QE methodology framework is established through QE implementation practically in a company and laboratories. The similarities and linkage between these conditions are integrated in QE framework.


Orthogonal Array Taguchi Method Robust Design Noise Factor Spur Gear 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Japan 2015

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringSchool of Science and Technology, Meiji UniversityKawasakiJapan
  2. 2.UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi MalaysiaKuala LumpurMalaysia

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