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In the mechanical cutting industry, trial production is used for predicting and evaluating the quality of product processes before batch production, and it can be expressed through the qualification rate. However, it cannot objectively and comprehensively evaluate the quality of product processes. This study optimizes the analysis of outliers and stability in mathematical statistics to better apply it in the mechanical cutting industry; then, it combines them with process capability analysis. Simultaneously, considering the non-normal distribution of process parameters, a batch production-prediction model is proposed. The reliability of batch production-prediction model is verified by the diameter, roundness and roughness of structural common samples. Meanwhile, for other mechanical parts in the mechanical cutting industry, the model proposed in this paper can be used to quickly and accurately predict and evaluate batch production.

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In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a process to produce output within specification limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15-30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre-licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data-driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian-based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre-defined commercialization stage-gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.

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Besides achieving high quality products, statistical techniques are applied in many fields associated with health such as medicine, biology and etc. Adhering to the quality performance of an item to the desired level is a very important issue in various fields. Process capability indices play a vital role in evaluating the performance of an item. In this paper, the larger-the-better process capability index for the three-parameter Omega model based on progressive type-II censoring sample is calculated. On the basis of progressive type-II censoring the statistical inference about process capability index is carried out through the maximum likelihood. Also, the confidence interval is proposed and the hypothesis test for estimating the lifetime performance of products. Gibbs within Metropolis-Hasting samplers procedure is used for performing Markov Chain Monte Carlo (MCMC) technique to achieve Bayes estimation for unknown parameters. Simulation study is calculated to show that Omega distribution's performance is more effective. At the end of this paper, there are two real-life applications, one of them is about high-performance liquid chromatography (HPLC) data of blood samples from organ transplant recipients. The other application is about real-life data of ball bearing data. These applications are used to illustrate the importance of Omega distribution in lifetime data analysis.

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Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate Cpk integrated Bootstrap-t. The Cpk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.

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Process capability analysis is the main tool of statistical process control. It is used for the ongoing monitoring of product compliance with imposed requirements. The main objective and novelty of the study were to determine the capability indices for a precision milling process of AZ91D magnesium alloy. Machining was performed in terms of variable technological parameters and using end mills with protective TiAlN and TiB2 coatings intended for the machining of light metal alloys. The Pp and Ppk process capability indices were determined based on the measurements of the dimensional accuracy of the shaped components that were taken on a machining centre with a workpiece touch probe. Obtained results demonstrated that the type of tool coating and variable machining conditions had a significant impact on the machining effect. The selection of appropriate machining conditions enabled a terrific level of capability to be achieved at a tolerance of 12 µm, several times lower than under unfavourable conditions where the tolerance was up to 120 µm. Improvements in process capability are mainly achieved by adjusting the cutting speed and feed per tooth. It was also shown that process estimation based on improperly selected capability indices might lead to an overestimation of the actual process capability.

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Specification limits are the competence regulatory agencies, whereas the release limit is a manufacturer's internal specification to be applied at the time of batch release to assure that quality attributes will remain within the specification limits until the expiry time. The aim of this work is to propose a method to set the shelf life from drug manufacture process capacity and degradation rate, using a modified version of the proposed method by Allen et al. (1991) Two different data sets were used to do this. The first data set corresponds to analytical method validation to measure the insulin concentration in order to estimate the specification limits, whereas the latter set gathered information on stability data of six batches of human insulin pharmaceutical preparation. In this context, the six batches were divided into two groups: Group 1 (batches 1, 2, and 4) was used to estimate shelf life; Group 2 (batches 3, 5, and 6) was used to test the estimated lower release limit (LRL). The ASTM E2709-12 approach was applied to verify that the future batches fulfill the release criterium. The procedure has been implemented in R-code.

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Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate Cpk integrated Bootstrap-t. The Cpk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.

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Process capability indices (PCIs) are most effective devices/techniques used in industries for determining the quality of products and performance of manufacturing processes. In this article, we consider the PCI Cpc which is based on the proportion of conformance and is applicable to normally as well as non-normally and continuous as well as discrete distributed processes. In order to estimate the PCI Cpc when the process follows exponentiated exponential distribution, we have used five classical methods of estimation. The performances of these classical estimators are compared with respect to their biases and mean squared errors (MSEs) of the index Cpc through simulation study. Also, the confidence intervals for the index Cpc are constructed using five bootstrap confidence interval (BCIs) methods. Monte Carlo simulation study has been carried out to compare the performances of these five BCIs in terms of their average width and coverage probabilities. Besides, net sensitivity (NS) analysis for the given PCI Cpc is considered. We use two data sets related to electronic and food industries and two failure time data sets to illustrate the performance of the proposed methods of estimation and BCIs. Additionally, we have developed PCI Cpc using aforementioned methods for generalized Rayleigh distribution.

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Oral solid dosage forms that contain APIs in the amorphous state have become commonplace because of many drug substances exhibiting poor water solubility, which negatively impacts their absorption in the human GI tract. While micronization, solvent spray-drying, and hot-melt extrusion can address solubility issues, spray coating of the APIs onto beads and tablets offers another option for producing amorphous drug products. High-level comparisons between bead and tablet coating technologies have the potential for simpler equipment and operation that can reduce the cost of development and manufacturing. However, spray coating directly onto tablets is not without challenges, especially with respect to meeting uniformity acceptance value (AV) criteria, comprising accuracy (mean) and precision (variance) objectives. The feasibility of meeting AV criteria is examined, based on mathematical models for accuracy and precision. The results indicate that the main difficulty in manufacturing satisfactory drug-layered tablets by spray coating is caused by the practical limitations of achieving the necessary coating precision. Despite this limitation, it is shown that AV criteria can be consistently met by appropriate materials monitoring and control as well as processing equipment setup, operation, and maintenance.

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PURPOSE: To assess the effectiveness of SGRT in clinical applications through statistical process control (SPC). METHODS: Taking the patients' positioning through optical surface imaging (OSI) as a process, the average level of process execution was defined as the process mean. Setup errors detected by cone-beam computed tomography (CBCT) and OSI were extracted for head-and-neck cancer (HNC) and breast cancer patients. These data were used to construct individual and exponentially weighted moving average (EWMA) control charts to analyze outlier fractions and small process shifts from the process mean. Using the control charts and process capability indices derived from this process, the patient positioning-related OSI performance and setup error were analyzed for each patient. RESULTS: Outlier fractions and small shifts from the process mean that are indicative of setup errors were found to be widely prevalent, with the outliers randomly distributed between fractions. A systematic error of up to 1.6 mm between the OSI and CBCT results was observed in all directions, indicating a significantly degraded OSI performance. Adjusting this systematic error for each patient using setup errors of the first five fractions could effectively mitigate these effects. Process capability analysis following adjustment for systematic error indicated that OSI performance was acceptable (process capability index Cpk = 1.0) for HNC patients but unacceptable (Cpk < 0.75) for breast cancer patients. CONCLUSION: SPC is a powerful tool for detecting the outlier fractions and process changes. Our application of SPC to patient-specific evaluations validated the suitability of OSI in clinical applications involving patient positioning.

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BACKGROUND: To evaluate the utility of the process capability indices Cp and Cpk for assessing the quality control processes at chain laboratory facilities. METHODS: In April 2020, the minimum Cp and Cpk values for 33 assays of a laboratory chain with 19 facilities were collected for further analysis and a total of 627 datasets (Cp and Cpk ) were compared. In addition, standard values for Cp and Cpk , defined as the lowest of the top 20%, were obtained for comparison and the indices were used to determine whether precision or trueness improvements were required for the corresponding assay. RESULTS: A total of 627 datasets of 33 assays from 19 laboratory facilities were collected for further analysis. Based on the Cp results, 329 (52.5%), 211 (33.7%), 65 (10.3%), and 22 (3.5%) were rated as excellent, good, marginal, and poor, respectively. While the corresponding results for Cpk were 300 (47.8%), 216 (34.4%), 79 (12.6%), and 32 (5.1%). In addition, it was noteworthy that eight (Cp criteria) and six assays (Cpk criteria) were rated as excellent or good at all 19 facilities. Comparison of the process capability indices at the Jinan KingMed Center with the standard values revealed that total protein, albumin, and urea showed trueness individual improvement, precision individual improvement, and precision common improvement, respectively, while the results of other assays were stable. CONCLUSION: Process capability indices are useful for evaluating the quality control procedures in laboratory facilities and can help improve the precision and trueness of laboratory tests.

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This study proposes the Spa-product capability analysis chart (Spa-PCAC), which can widely represent multiple process capabilities with asymmetric tolerances of Smaller-the-Better, Larger-the-Better, and Nominal-the-Best characteristics. Process capability index Spa is generated based on index Spk, which uses asymmetric tolerances to reasonably measure process capabilities. The interval estimates of the indices are derived to reliably assess process capabilities. The Six-Sigma-based quality-level and its connection with the process yield are introduced in the capability zone of Spa-PCAC to check if the process capabilities can meet the requirements. One example of an entire product is given for application.

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Micro polymer parts can be usually manufactured either by conventional injection moulding (IM) or by micro-injection moulding (µIM). In this paper, functional analysis was used as a tool to investigate the performances of IM and µIM used to manufacture the selected industrial component. The methodology decomposed the production cycle phases of the two processes and attributed functions to parts features of the two investigated machines. The output of the analysis was aimed to determine casual chains leading to the final outcome of the process. Experimental validation of the functional analysis was carried out moulding the same micro medical part in thermoplastic elastomer (TPE) material using the two processes by means of multi-cavity moulds. The produced batches were assessed using a precision scale and a high accuracy optical instrument. The measurement results were compared using capability indexes. The data-driven comparison identified and quantified the correlations between machine design and part quality, demonstrating that the µIM machine technology better meets the accuracy and precision requirements typical of micro manufacturing productions.

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Bangladesh's government had been constantly conscious of its health-care system. As a result, the health-care system had seen a significant rise in quality in the past few years. The Lean Six Sigma concept is used to boost up the productivity as well as the quality of products or services. The purpose of this article was to measure the process capability in the health-care system by lean tools. In this study, we selected the dialysis unit as our investigation area where we applied the Six Sigma DMAIC (Define, Measure, Analysis, Improve, and Control) model which had been structured step by step, respectively. Furthermore, different tools of Total Quality Management, Statistical Quality Control and Lean manufacturing like SIPOC (Supplier, Input, Process, Output, and Customer) diagram, P Control chart, Fishbone diagram, and Pareto analysis had been performed in different phases of the DMAIC model. Measuring process capability using the DMAIC model helped to identify the problems associated with the dialysis unit and also recommendations were developed based on investigation and analysis. These recommendations will help the hospital management to overcome all problems and increase service rate and patient safety in the dialysis unit.

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To design a reasonable dimensional tolerance of a transmission shaft, the higher product quality while lower manufacturing cost must be considered. This paper provides a mathematical model and a flowchart which elucidates the relationship between process capability index (PCI), reliability, tolerance and manufacturing cost, considering the characteristics of the shaft diameter, the material and manufacturing process. A 10.904% cost reduction under certain PCI range of a real case shows the effectiveness of the model and flowchart, thus it can be applied to those technical area when optimizing product design.

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PURPOSE: Applying statistical process control (SPC) to intensity-modulated radiotherapy (IMRT)/volumetric modulated arc therapy (VMAT) patient-specific quality assurance (PSQA) program was recommended by the American Association of Physics in Medicine Task Group 218 report, but a comprehensive analysis of PSQA processes with non-normal distributions is lacking. This study investigates SPC and process capability analysis (PCA) methods for non-normal IMRT/VMAT PSQA processes. METHODS: 1119 VMAT PSQAs were performed on three beam-matched linear accelerators (linacs), using gamma analysis. The Anderson-Darling statistic was used to test normality. The control charts for each PSQA process were obtained using three non-normal-based methods and compared with the conventional Shewhart method. The ability of each PSQA process to produce an output within the specification limit was measured using the C pk index; in this study, the C pk index was calculated using two transformation methods and compared with that calculated using the conventional method. The performances of the three linacs were assessed using SPC and PCA methods. RESULTS: All three PSQA processes were non-normal (P < 0.005). Compared to the non-normal-based SPC and PCA methods, the false alarm rates of the conventional method for linac1, linac2, and linac3 were 0.83%, 3.77%, and 4.95% respectively; the minimum overestimated C pk values were 0.59, 0.87, and 1.49, respectively. The process capabilities of the three beam-matched linacs were at different levels. CONCLUSION: For non-normal VMAT PSQA processes, the conventional SPC and PCA methods increase the false alarm rates and overestimate process capabilities. Instead, non-normal-based SPC and PCA methods are more reliable and accurate in non-normal PSQA processes. Statistical process control and PCA are useful tools for assessing the performance of beam-matched linacs.

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Polymer-based additive manufacturing (AM) gathers a great deal of interest with regard to standardization and implementation in mass production. A new methodology for the system and process capabilities analysis in additive manufacturing, using statistical quality tools for production management, is proposed. A large sample of small specimens of circular shape was manufactured of photopolymer resins using polymer jetting (PolyJet) technology. Two critical geometrical features of the specimen were investigated. The variability of the measurement system was determined by Gage repeatability and reproducibility (Gage R&R) methodology. Machine and process capabilities were performed in relation to the defined tolerance limits and the results were analyzed based on the requirements from the statistical process control. The results showed that the EDEN 350 system capability and PolyJet process capability enables obtaining capability indices over 1.67 within the capable tolerance interval of 0.22 mm. Furthermore, PolyJet technology depositing thin layers of resins droplets of 0.016 mm allows for manufacturing in a short time of a high volume of parts for mass production with a tolerance matching the ISO 286 IT9 grade for radial dimension and IT10 grade for linear dimensions on the Z-axis, respectively. Using microscopy analysis some results were explained and validated from the capability study.

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Objective: The purpose of this study was to apply the rheological measurements to assess the flow properties of powders and granules and to compare the results with the standard pharmacopeial tests. Quality by design approach was utilized to better understand the compression of the solids into minitablets.Significance: Insights are provided regarding the methodology of rheological properties of powders and granules using powder flow analyzer (PFA). The 'six sigma' approach was presented as a tool for assessment of the minitablets manufacturing process.Methods: Pharmacopeial methods and rheological tests using PFA were performed to assess the flow properties of designed powder and fractionated granule mixtures - placebo and with benzodiazepines. Compression of 2.5 and 3 mm minitablets was carried out and the compression force registered during the process and weight uniformity were statistically analyzed by calculating the capability indices.Results: The flow rate measurement and cohesion test (PFA test) resulted in the best differentiation between mixtures. Higher values of capability indices were obtained for processes in which granule mixtures with better flow properties were compressed and 3 mm minitablets were produced and the usefulness of QbD tools in assessment of minitablets compression process was confirmed.Conclusion: Performed study showed that the flow properties are the critical quality attributes determining the performance of minitablets compression. The cohesion test is the most discriminative to distinguish the analyzed mixtures. Capability indices can be used to assess the manufacturing process as a useful tool in pharmaceutical development of minitablets.

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Product specifications are ideally based on knowledge of patient needs or requirements of subsequent manufacturing steps. However, in most applications, knowledge of patient needs is neither precise nor comprehensive enough to fully define specifications. The prevailing practice is to base specifications on process experience, setting limits to assure consistency of future results with initial results representative of clinical material. Developers of new medicines are often required to set initial product specifications and other limits when only small amounts of process experience have been accumulated. Product developers and health authority reviewers share the mandate to protect patients from harm and assure the effectiveness of medical products, which motivates a tendency to set limits very tight. But although tighter limits give the impression of tighter control, limits alone accomplish no reduction in the variation that exists in established processes and test methods. Limits that are too tight do not represent the natural variability of the process and test methods. Unnaturally tight limits will result in a high number of excursions beyond the limits, potentially causing discards, supply disruptions, and higher cost of goods sold. In this article, we demonstrate how to deliberately control the probability of having intervals that are too tight during the early manufacturing process.

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