Statistical approach in a system level methodology to deal with process variation

2010 ◽  
Author(s):  
Concepción Sanz Pineda ◽  
Manuel Prieto ◽  
Jose Ignacio Gómez ◽  
Christian Tenllado ◽  
Francky Catthoor
Keyword(s):  
Statistical Approach ◽  
Process Variation ◽  
System Level

Statistical Approach to Structural Damage Diagnosis under Uncertainty

2015 ◽  
pp. 153-170
Author(s):  
Shankar Sankararaman ◽  
Sankaran Mahadevan
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Statistical Approach ◽  
Likelihood Function ◽  
System Level ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Damage Diagnosis ◽  
Sources Of Uncertainty ◽  
Classical Statistics

This chapter presents a statistical methodology for structural damage diagnosis (detection, localization and estimation), in the context of continuous online monitoring. There are several sources of uncertainty such as physical variability, measurement uncertainty and model errors that affect structural damage diagnosis, and therefore, it may not be possible to precisely detect, localize, and estimate damage. Hence, a statistical approach can help to identify these sources of uncertainty, quantify their combined effect on diagnosis, and thereby, provide an estimate of the confidence in the results of diagnosis. Damage detection is based on residuals between nominal and damaged system-level responses, using statistical hypothesis testing whose uncertainty can be captured easily. Localization is based on the comparison of damage signatures derived from the system model. Both classical statistics-based methods and Bayesian statistics-based methods are investigated to quantify the uncertainty in all the three steps of diagnosis, i.e. detection, localization, and quantification. While classical statistics-based methods use the concept of least squares-based optimization, Bayesian methods make use of likelihood function and Bayes theorem. The uncertainties in damage detection, isolation and quantification are combined to quantify the overall uncertainty in diagnosis. The proposed methods are illustrated using the example of a structural frame.

scholarly journals Approaching Inpatient Glycemic Control Using Six Sigma Methodology

2021 ◽  
Vol 6 (04) ◽  
pp. 288-290
Author(s):  
Rodhan Khthir, MD, CPHQ, CCD
Keyword(s):  
Medical Errors ◽  
Six Sigma ◽  
Statistical Approach ◽  
Process Variation ◽  
Healthcare Quality ◽  
Defect Rate ◽  
The Mean ◽  
Healthcare Processes ◽  
Six Sigma Methodology ◽  
Six Sigma Approach

To Err is Human: Building a Safer Health System [1] is a report issued by the U.S. Institute of Medicine (IOM) in November 1999 and has resulted in increasing awareness regarding high medical errors in healthcare industry. The report, which was based on an analysis of adverse health outcomes by a variety of organisations, concluded that between 44,000 and 98,000 people die each year as a result of preventable medical errors. Since its publication; there has been a significant enthusiasm to improve patient safety and to improve healthcare outcome. As a result of that, we have witnessed the introduction of many new innovations and reengineered processes. In spite of that medical error rate remains high, and possibly higher as shown by a more recent medical error analysis [2]. In other industries, any measurement falling outside of industry standards is regarded as a defect. This is how quality is defined, at least in companies that have adopted the Six Sigma approach which is a statistical approach for quality improvement.  Processes that operate with "six sigma quality" over the short term are assumed to produce long-term defect levels below 3.4 defects per million opportunities (DPMO) [3]. Six Sigma's implicit goal is to improve all processes, but not to the 3.4 DPMO level necessarily. Its main philosophy it to reduce process variation to minimum level using a well-defined statistical approach. Many large companies use Six Sigma methodology to reduce the defect rate to its lowest possible value. The Six Sigma attempts to reduce the number of defects to below 3.4 per million opportunities; industries such as aviation target and achieve an even lower defect rate (less than 1 defect per 2 million opportunities). Simply, the Six Sigma concept is a statistical approach to improving the quality and performance of a specific process by focusing on the “Critical to Quality Step” as identified by the “Voice of the Customer”. It aims to maintain the mean result within a target range (i.e., between upper and lower specification limits) and focuses on reducing the variation in the outcome to the lowest possible level. The variation in outcome is usually measured as the standard deviation around the mean (i.e., Sigma). The Six Sigma method aims to fit six standard deviations around the mean without crossing the lower or upper specification targets. This process yields high performance and high potential [3]. Healthcare processes have usually high defect rate and wide variations (low sigma level). Six Sigma approach can be used in healthcare to improve specific processes using the same methodology used by other industries. The purpose of this analysis is to illustrate how to use Six Sigma approach in healthcare processes to decrease variation, using inpatient glycemic control as an example. Introducing this approach in medical education can help medical students, residents and other professionals to approach healthcare quality and patient safety in standardized way and can allow them to use recent advances in medical technology and artificial intelligence to achieve better healthcare outcomes. Another purpose of this article is to illustrate how to study the process variation using Six Sigma approach and how to use it in teaching and approaching healthcare quality. Education on process variation using six sigma methodology is a valuable way to teach the new generation of physicians the statistical approach to quality.

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