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Nonparametric Statistical Process Control Charts‐A Special Issue of QREI
Author(s) -
Chakraborti Subha
Publication year - 2013
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1527
Subject(s) - citation , nonparametric statistics , statistical process control , computer science , quality (philosophy) , library science , reliability (semiconductor) , control (management) , control chart , process (computing) , operations research , information retrieval , statistics , mathematics , artificial intelligence , programming language , philosophy , power (physics) , physics , epistemology , quantum mechanics
S tatistical process control (SPC) charts are widely used in the industry for monitoring the stability and the efficacy of processes (e.g., manufacturing processes, health care systems, internet traffic flow, and so forth) based on observed time-ordered data. Traditional control charts require the assumption that the process distribution follows a parametric form (e.g. normal). In practice, however, this assumption may not hold, in other words, the process may not follow the pre-specified parametric distribution. In the literature, it has been well demonstrated that results from the traditional control charts using the pre-specified distribution in their design may not be reliable because their actual false alarm rates could be substantially larger or smaller than the nominal false alarm rate. A direct consequence of this could be thatmuch labor andmany resources arewasted, or thatmany defective products aremanufacturedwithout notice. Therefore, in cases when no parametric form of the process distribution is available or when no parametric form is validated properly beforehand, control charts without requiring the specification of a parametric form for the process response distribution, or simply nonparametric (distribution-free) statistical process control (NSPC) charts, should be considered. There has been a huge growth in NSPC research in recent years. The goal of this special issue is to review and highlight what has been done, bring to light the latest cutting edge research in the area and provide future directions. The special issue will cover all topics related to NSPC, including but not limited to (i) phase I NSPC, (ii) phase II NSPC, (iii) NSPC for monitoring discrete or categorical data, and (iv) new and innovative applications including case studies involving NSPC. Papers must contain high-quality original contributions, and be prepared in accordance with the QREI standards and guidelines. Submitted papers should be original, not previously published, and not under consideration for publication elsewhere. All papers will be reviewed following the regular review procedure of QREI.