The Journal of Reliability Engineering Association of Japan

Online ISSN : 2424-2543
Print ISSN : 0919-2697
ISSN-L : 0919-2697

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Volume 38, Issue 6

Studies of Reliability Modeling for the Information and Communication Systems

Displaying 1-6 of 6 articles from this issue

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A Case Study on Reliability Evaluation with Markov Regenerative Stochastic Petri Nets

Hiroyuki OKAMURA, Tadashi DOHI

2016 Volume 38 Issue 6 Pages 340-349
Published: 2016
Released on J-STAGE: August 01, 2019

DOIhttps://doi.org/10.11348/reajshinrai.38.6_340

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Stochastic Modeling and Analysis for Quantitative Evaluation of Non-Functional System Requirement

Kumiko TADANO

2016 Volume 38 Issue 6 Pages 350-357
Published: 2016
Released on J-STAGE: August 01, 2019

DOIhttps://doi.org/10.11348/reajshinrai.38.6_350

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Stochastic Modelling of Communication Protocols and Server Operation

Satoshi MIZUTANI, Mitsutaka KIMURA, Mitsuhiro IMAIZUMI

2016 Volume 38 Issue 6 Pages 358-365
Published: 2016
Released on J-STAGE: August 01, 2019

DOIhttps://doi.org/10.11348/reajshinrai.38.6_358

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The Role of Modeling in Reliability Design of Telecommunication Networks

Hitoshi WATANABE

2016 Volume 38 Issue 6 Pages 366-371
Published: 2016
Released on J-STAGE: August 01, 2019

DOIhttps://doi.org/10.11348/reajshinrai.38.6_366

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Application of Software Comlexity and Test Environment Data into Software Reliability Assessment

Shinji INOUE, Shigeru YAMADA

2016 Volume 38 Issue 6 Pages 372-377
Published: 2016
Released on J-STAGE: August 01, 2019

DOIhttps://doi.org/10.11348/reajshinrai.38.6_372

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Analyzing claim data for detecting major quality problems and examining effectiveness of actions taken

Meena Watcharathiansakul, Watalu Yamamoto, Kazuyuki Suzuki

2016 Volume 38 Issue 6 Pages 389-406
Published: 2016
Released on J-STAGE: August 01, 2019

DOIhttps://doi.org/10.11348/reajshinrai.38.6_389

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Claims on quality problems should be handled carefully and should be resolved individually. However, claims are also valuable sources of information for claim management system. Statistics on claims for monitoring and improving the performance of the claim management function should be gathered. This paper presents two statistical methods for analyzing claim data with use of Quality Claim Management Matrix (QCMM) effectively. The first method is proposed for detecting major quality problems from monthly claim counts. Companies can apply this method to identify major quality problems in a timely manner. The second method is designed for examining the effectiveness of actions taken against quality problems, based also on claim count data. The properties of proposed methods are investigated numerically. These methods are applied to two example cases from industry.

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