The Future Role of Statistics in Quality Engineering and Management (with Discussion)

Bendell, A.; Disney, John; McCollin, C

doi:10.1111/1467-9884.00190

Cited by 20 publications

(23 citation statements)

References 55 publications

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“…In fact, the theory has been taken from a textbook which is extremely high level and would put off most engineers who would want to carry out the analysis. Bendell, Disney and McCollin (1999) and discussants Quigley and Walls from the same paper advocate the use of simple analyses as 'complex models generate ambiguity and are misleading to design makers' (Quigley).…”

Section: Discussionmentioning

confidence: 99%

An Availability Study for a SME

McCollin¹,

Disney²

2004

Total Quality Management & Business Excellence

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A case study of an availability analysis for a small commercial company is presented. The analysis was carried out to meet a customer requirement for the availability of an electronic ground based system in a benign environment. Availability calculations were based on failure data provided and an explanation of the methodology and problems encountered and dealt with are discussed. The methodology includes failure classification according to MIL-HDBK-781A and how it may be used to promote and develop internal processes. A commentary on the background to reliability/availability specification is provided and a number of recommendations for monitoring reliability and availability are given.

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Section: Discussionmentioning

confidence: 99%

An Availability Study for a SME

McCollin¹,

Disney²

2004

Total Quality Management & Business Excellence

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“…Although some of his techniques have now been rightly dismissed as inef®cient and in some cases spurious (Bendell et al, 1999;Box et al, 1988;Grove and Davis, 1992;Logothetis and Wynn, 1989) we should not forget his contribution particularly to the debate on interactions and experimental size.…”

Section: Anthony Atkinson (London School Of Economics and Political Smentioning

confidence: 99%

Detection of Interactions in Experiments on Large Numbers of Factors

Lewis

Dean

2001

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary. One of the main advantages of factorial experiments is the information that they can offer on interactions. When there are many factors to be studied, some or all of this information is often sacri®ced to keep the size of an experiment economically feasible. Two strategies for group screening are presented for a large number of factors, over two stages of experimentation, with particular emphasis on the detection of interactions. One approach estimates only main effects at the ®rst stage (classical group screening), whereas the other new method (interaction group screening) estimates both main effects and key two-factor interactions at the ®rst stage. Three criteria are used to guide the choice of screening technique, and also the size of the groups of factors for study in the ®rst-stage experiment. The criteria seek to minimize the expected total number of observations in the experiment, the probability that the size of the experiment exceeds a prespeci®ed target and the proportion of active individual factorial effects which are not detected. To implement these criteria, results are derived on the relationship between the grouped and individual factorial effects, and the probability distributions of the numbers of grouped factors whose main effects or interactions are declared active at the ®rst stage. Examples are used to illustrate the methodology, and some issues and open questions for the practical implementation of the results are discussed.

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“…The use of different tools or methodologies allowed for the development of new products, quality control processes, regulatory enforcement activities and problem-solving tasks that are based on the application of modern statistics concepts, experimental design and chemometrics where they become an integral part of the analytical process and analysis of foods (Bendell et al 1999;McGorrin 2009).…”

Section: Introductionmentioning

confidence: 99%

An Overview on the Application of Chemometrics in Food Science and Technology—An Approach to Quantitative Data Analysis

Roberts

Cozzolino

2016

Food Anal. Methods

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During the last 30 years, food scientists and technologists all over the world are dealing with massive amounts of data derived from the use of different measuring devices (e.g. instrumental and sensory data), the integration of different analytical techniques and processes during the analysis and production of foods. Therefore, complementary disciplines and tools to the traditional ones used in food science such as statistics, experimental design and chemometrics have become essential in modern sciences and are an integral component in the day-to-day analysis of foods and derived products. The aim of this paper is to introduce as well as provide with an overview of different concepts, methods, techniques and general steps used in the quantitative analysis of foods when chemometrics or multivariate analytical methods are applied.

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The Future Role of Statistics in Quality Engineering and Management (with Discussion)

Cited by 20 publications

References 55 publications

An Availability Study for a SME

An Availability Study for a SME

Detection of Interactions in Experiments on Large Numbers of Factors

An Overview on the Application of Chemometrics in Food Science and Technology—An Approach to Quantitative Data Analysis

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