Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and busines

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal

Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and busines

Identifying the Out of Control Variable in a Multivariate Control Chart

The identification of the out of control variable, or variables, after a multivariate control chart signals, is an appealing subject for many researchers in the last years. In this paper we propose a new method for approaching this problem based on principal components analysis. Theoretical control limits are derived and a detailed investigation of the properties and the limitations of the new method is given. A graphical technique which can be applied in some of these limiting situations is also provided

Identification and Antimicrobial Resistance of Campylobacter Species Isolated from Animal Sources

Background: Campylobacter spp. are together with Salmonella spp. the leading causes of human bacterial gastroenteritis worldwide. The most commonly isolated species in humans are Campylobacter jejuni and C. coli. The isolation, identification, and antimicrobial resistance of Campylobacter spp. from poultry and raw meat from slaughterhouses, has been investigated for the first time in Greece. During the period from August 2005 to November 2008 a total of 1080 samples were collected: (a) 830 fecal samples from five poultry farms, (b) 150 cecal samples from chicken carcasses in a slaughterhouse, and (c) 100 fecal samples from one pig farm near the region of Attica. The identification of the isolates was performed with conventional (sodium hippurate hydrolysis and commercial identification system (Api CAMPY system, bioMerieux, France), as well as with and molecular methods based on 16S rRNA species specific gene amplification by PCR and subsequent sequence analysis of the PCR products. Results: Sixteen Campylobacter strains were isolated, all collected from the poultry farms. None of the strains was identified as C. jejuni. Antimicrobial susceptibility to six antimicrobials was performed and all the strains were susceptible to ciprofloxacin, amoxicillin–clavulanic acid, and gentamicin. Thirteen out of 14 C. coli were resistant to erythromycin and all C. coli strains were resistant to ampicillin. Conclusion: Our results emphasize the need for a surveillance and monitoring system with respect to the prevalence and antimicrobial resistance of Campylobacter in poultry, as well as for the use of antimicrobials in veterinary medicine in Greece

Incidence and antimicrobial susceptibilities of genital mycoplasmas in outpatient women with clinical vaginitis in Athens, Greece

Objectives: The incidence and antimicrobial susceptibilities of Ureaplasma urealyticum and Mycoplasma hominis, isolated from vaginal and endocervical swabs collected from 369 outpatient women, were determined. Methods: Isolation, identification and typing of the pathogens were performed by means of conventional methods. The antimicrobial susceptibilities of the genital mycoplasmas were determined with commercially available kits and evaluated according to the CLSI. Results and conclusions: In 65 (47.44%) out of the 137 positive specimens, U. urealyticum was grown as a single pathogen, in 0.72% M. hominis was grown as a single pathogen and in 2.92% both urogenital mycoplasmas were grown. In the remaining specimens (48.90%), there was a mixed growth with other microbes. Of the isolated U. urealyticum strains, 87.4% and 98.2% were susceptible to tetracycline and doxycycline, respectively, 79.2% were susceptible to josamycin, 48.6% were susceptible to clarithromycin and 91.8% were susceptible to pristinamycin, while erythromycin, azithromycin, ciprofloxacin and ofloxacin proved to be inactive against most of the strains. M. hominis isolates were 100% susceptible to tetracycline, doxycycline and pristinamycin, while susceptibilities to the other antimicrobial agents varied mainly in the range of ‘intermediate’ or ‘resistant’. As results originating from similar studies from various countries are very controversial, the simplest way to avoid therapeutic failures would be the implementation of rational treatment regimens based on culture isolation and the in vitro determination of the antimicrobial susceptibility of genital mycoplasmas in each clinical case