Statistical methods to the control of the production of blood components: principles and control charts for variables

Pereira, Paulo; Seghatchian, Jerard; Caldeira, Beatriz; Xavier, Sandra; Sousa, Gracinda de

doi:10.1016/j.transci.2018.02.022

Cited by 12 publications

(13 citation statements)

References 5 publications

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“…The atypical or special cause variation is different from common cause variation in that special cause variation in the process can be attributed to an assignable cause which can be removed through corrective actions leading to process improvement. A well-established set of rules (such as Nelson rules (10) or Western Electric rules (11)) can be used to detect patterns in the process monitoring data and indicate special cause variation.…”

Section: Stage 3-continued Process Verificationmentioning

confidence: 99%

Recommended Best Practices for Lyophilization Validation 2021 Part II: Process Qualification and Continued Process Verification

et al. 2021

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This work describes the lyophilization process validation and consists of two parts. Part one (Part I: Process Design and Modeling) focuses on the process design and is described in the previous paper, while the current paper is devoted to process qualification and continued process verification. The goal of the study is to show the cutting edge of lyophilization validation based on the integrated community-based opinion and the industrial perspective. This study presents best practices for batch size determination and includes the effect of batch size on drying time, process parameters selection strategies, and batch size overage to compensate for losses during production. It also includes sampling strategies to demonstrate batch uniformity as well as the use of statistical models to ensure adequate sampling. Based on the LyoHUB member organizations survey, the best practices in determining the number of PPQ runs are developed including the bracketing approach with minimum and maximum loads. Standard practice around CQA and CPP selection is outlined and shows the advantages of using control charts and run charts for process trending and quality control. The case studies demonstrating the validation strategy for monoclonal antibody and the impact of the loading process on the lyophilization cycle and product quality as well as the special case of lyophilization for dual-chamber cartridge system are chosen to illustrate the process validation. The standard practices in the validation of the lyophilization process, special lyophilization processes, and their impact on the validation strategy are discussed. Graphical Abstract

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Section: Stage 3-continued Process Verificationmentioning

confidence: 99%

Recommended Best Practices for Lyophilization Validation 2021 Part II: Process Qualification and Continued Process Verification

et al. 2021

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“…Tables 1-3 show the values of NARL for n N [2], [5], n N [5], [7], n N [8], [10], various values of c and various values of specifiedNARL 0N , say r 0N .…”

Section: Design Of the Proposed Chartmentioning

confidence: 99%

“…[5] designed the control chart for monitoring the variability in the process. More details on the applications of the control charts can be read in [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

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Design of X-Bar Control Chart for Resampling Under Uncertainty Environment

Aslam

2019

IEEE Access

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This paper presents the designing of the X-bar control chart using resampling scheme under the uncertainty environment. The necessary measures of the present chart are derived under the neutrosophic system. The neutrosophic average run length (NARL) when the process is in-control and out-of-control are derived under the neutrosophic statistical interval method (NSIM). The neutrosophic control chart coefficients are determined through the algorithm developed under NSIM. The comparative study shows that the proposed chart is better than the existing chart in NARL.INDEX TERMS Monitoring, Shewhart chart, neutrosophic average run length, statistical interval method, shift.

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“…Therefore, the Shewhart control charts do not detect a very small shift, and cause a high non-conforming product. The applications of such charts can be seen in [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average

2019

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The existing Shewhart X-bar control charts using the exponentially weighted moving average statistic are designed under the assumption that all observations are precise, determined, and known. In practice, it may be possible that the sample or the population observations are imprecise or fuzzy. In this paper, we present the designing of the X-bar control chart under the symmetry property of normal distribution using the neutrosophic exponentially weighted moving average statistics. We will first introduce the neutrosophic exponentially weighted moving average statistic, and then use it to design the X-bar control chart for monitoring the data under an uncertainty environment. We will determine the neutrosophic average run length using the neutrosophic Monte Carlo simulation. The efficiency of the proposed plan will be compared with existing control charts.

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Statistical methods to the control of the production of blood components: principles and control charts for variables

Cited by 12 publications

References 5 publications

Recommended Best Practices for Lyophilization Validation 2021 Part II: Process Qualification and Continued Process Verification

Recommended Best Practices for Lyophilization Validation 2021 Part II: Process Qualification and Continued Process Verification

Design of X-Bar Control Chart for Resampling Under Uncertainty Environment

A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average

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