Statistical Process Control Methods from the Viewpoint of Industrial Application

Anghel, Constantin

doi:10.1515/eqc.2001.49

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…A Pareto chart can help to focus efforts on achieving the greatest improvements. It is perhaps the diagram which is most often used in management presentations (Anghel 2001;Duicu and Dumitrascu 2011;Gibra 1973).…”

Section: Fig 1 Representation Of Defects Depending On Their Weight mentioning

confidence: 99%

Valorization of Wood Resources for the Cutting of Decorative Veneer in the Context of Sustainable Development of Romanian Forests

2013

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The overall aim of the research was to better understand the influence of raw material characteristics on the quality of decorative veneer, to identify phenotypes of oak and sessile oak valuable for veneer use, and to promote quality in relationship with sustainable development of Romania's forests. This paper describes specific aspects regarding wood defects and quality conditions imposed on raw materials for veneer slicing. The experiments focused on identifying and analyzing defects in oak veneer found in six regions from Romania. Taking into account the dimensional and quality requirements, and obtaining a sufficient quantity of decorative oak veneer, Pareto diagrams corresponding to each region were created. The Pareto analysis allowed for a hierarchy of main defects and also enabled adequate decision-making for improving the quality of the studied products in accordance with specific international standards. Also, by knowing the types of defects, decisions can be made to conserve natural resources and to utilize wood resources by sustainable reuse of waste.

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Section: Fig 1 Representation Of Defects Depending On Their Weight mentioning

confidence: 99%

Valorization of Wood Resources for the Cutting of Decorative Veneer in the Context of Sustainable Development of Romanian Forests

2013

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“…For instance, applications to hydrology are studied by Arsenault and Ashkar (62) and Chebana and Ouarda (63) , and other environmental applications of quantile risk assessments include climate change (Katz et al (64) and Diodato and Bellocchi (65) ) wind power (66) , and nuclear power (67) . In statistical process control, quantiles are used for computing capability indices (68) , for measuring efficiency (69) and for reliability analysis (70) .…”

Section: Application To Nonfinancial Problemsmentioning

confidence: 99%

Quantile Uncertainty and Value‐at‐Risk Model Risk

Alexander¹,

Sarabia²

2012

Risk Analysis

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This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks.

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“…It follows that any prediction interval A (p) X for X given by the quantiles q ( ) 1] covers at least a proportion of 100p% of the lot. It follows that any prediction interval A (p) X for X given by the quantiles q ( ) 1] covers at least a proportion of 100p% of the lot.…”

Section: Prediction Intervals In Quality Controlmentioning

confidence: 99%

Prediction Intervals, Tolerance Intervals and Standards in Quality Control - Part 1

Collani¹,

Baur²

2002

Economic Quality Control

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Industrial quality control is necessary at least for two reasons:• Monitoring product or process quality in order to perform interventions for assuring a desired quality level.• Monitoring product or process quality in order to meet certain requirements (imposed by law or customers) for documentation.Especially in the second case legal requirements or consumers demand that "professional" methods are used. Therefore, industry relies on the methods offered by "professional" bodies like the International Organization for Standardization (ISO) or its national counterparts for example the German DIN Deutsches Institut für Normung, because they are widely acknowledged as professional.One of the standards which are in use in German industries is DIN 55303 (Teil 5): "Statistische Auswertung von Daten -Bestimmung eines statistischen Anteilsbereichs" of February 1987 which corresponds to ISO 3207-1975 "Statistical interpretation of data: determination of a statistical tolerance interval." This paper introduces in detail statistical tolerance intervals and subsequently examines the standard critical. This first part is devoted to the case that the normal approximation is used and results in the recommendation not to use the methods offered in the standard. A second part will investigate the case that the normal approximation is not made.

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Statistical Process Control Methods from the Viewpoint of Industrial Application

Cited by 7 publications

References 5 publications

Valorization of Wood Resources for the Cutting of Decorative Veneer in the Context of Sustainable Development of Romanian Forests

Valorization of Wood Resources for the Cutting of Decorative Veneer in the Context of Sustainable Development of Romanian Forests

Quantile Uncertainty and Value‐at‐Risk Model Risk

Prediction Intervals, Tolerance Intervals and Standards in Quality Control - Part 1

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