Process monitoring using statistical stability metrics: Applications to biopharmaceutical processes

Britt, Keith A.; Ramirez, Brenda S.; Mistretta, Tom

doi:10.1080/08982112.2015.1094705

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…Britt et al [6] performed a great amount of simulation and compared the performance of control chart visual analysis with the SR and ANOVA methods for sample sizes of n = 30, 40, . .…”

Section: Brief Survey Of the Previous Resultsmentioning

confidence: 99%

“…"Sapere aude" "No amount of experimentation can ever prove me right; a single experiment can prove me wrong" Albert Einstein Quantitative metrics estimating process stability emerged more than twenty years ago in the works of Podolski [1] and Cruthis and Rigdon [2]. Ramirez and Runger [3], Gauri [4], Wooluru et al [5], Britt et al [6], and Sall [7] continued the search for an optimal measure and the best way of its application. At last, most recent publications include two papers by Jensen et al [8,9], White et al [10], and Kim Jeong-bae et al [11].…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the Real Meaning of the Stability Index and Its Empirical Analysis

Shper,

Sheremetyeva,

Sitnikova

et al. 2023

Processes

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Assessment of process stability is a key to ensuring the high quality of any product, service, activity, etc. The main tool for doing this is a Shewhart control chart. However, it becomes difficult to analyze hundreds or thousands of control charts at a time for some up-to-date complex processes. In order to solve this problem, various indicators of process stability have been recently proposed with the Stability Index (SI) being the latest one, and, in many opinions, the best one. We have thoroughly analyzed different suggestions and found many of them ambiguous and sometimes even harmful to practitioners. Some examples of possible problems with the SI are provided below. It is argued that abnormal values of the SI can be caused not only by process instability, but by non-homogeneity, non-randomness, nonnormality, and autocorrelation as well. It is proved that the SI can be helpful for practitioners as an index to attract primary attention to the process. A proposal to rename the SI and widen significantly its application is given. Finally, some ways that practitioners could use this index more effectively are discussed.

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“…Britt et al [6] performed a great amount of simulation and compared the performance of control chart visual analysis with the SR and ANOVA methods for sample sizes of n = 30, 40, . .…”

Section: Brief Survey Of the Previous Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of the Real Meaning of the Stability Index and Its Empirical Analysis

Shper,

Sheremetyeva,

Sitnikova

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…Gauri 5 has proposed a measure of process stability called the process stability index, which is based on shape features of a run chart pattern. Britt et al 6 have considered applications of the process stability techniques in Ramirez and Runger 4 to biopharmaceutical processes. Jensen et al 7 had a challenge on improvement of the SR test in Ramirez and Runger.…”

Section: Introductionmentioning

confidence: 99%

“…Britt et al. 6 have considered applications of the process stability techniques in Ramirez and Runger 4 to biopharmaceutical processes. Jensen et al.…”

Section: Introductionmentioning

confidence: 99%

Stability evaluation in process mean using Bayesian statistics and information theory

Takemoto

Arizono

2021

Quality & Reliability Eng

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At the start of new operation, a production process is commonly unstable. Then, a process condition is into being stable gradually over time. For the purpose of shifting to mass production, the stability in a process should be evaluated. This paper proposes a method of evaluating the process stability based on Bayesian statistics and information theory. In Bayesian statistics, the knowledge for a process is renewed by deriving posterior distribution based on current prior distribution and observations. Here, equivalency between prior and posterior distributions could be considered to be a criterion for the stability in a process. It is needed to define difference between prior and posterior distributions in order to evaluate the equivalency between prior and posterior distributions. We first formulate the relation between the prior distribution and the posterior distribution for process mean using Bayesian statistics. Secondly, we evaluate the difference between the both distributions based on Kullback‐Leibler (K‐L) divergence in information theory. Finally, some numerical examples in the method of evaluating the stability in process mean are illustrated. Also, we discuss about a decision rule for the stability in process mean through the numerical investigation.

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“…For example, [1] used Shewhart control charts supplemented with runs rules to detect shifts in process variance. In [2], two stability metrics were proposed to identify underlying variation as common or special cause in biopharmaceutical processes. Reference [3] developed control charts by use of batch statistical process monitoring to perceive the process trajectory.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

Hou¹,

Feng

Wang

2016

Computational Intelligence and Neuroscience

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Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

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Process monitoring using statistical stability metrics: Applications to biopharmaceutical processes

Cited by 7 publications

References 12 publications

Investigation of the Real Meaning of the Stability Index and Its Empirical Analysis

Investigation of the Real Meaning of the Stability Index and Its Empirical Analysis

Stability evaluation in process mean using Bayesian statistics and information theory

Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

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