Process capability analysis for manufacturing processes based on the truncated data from supplier products

Yang, Jun; Meng, Fanbing; Huang, S.; Cui, Yanhe

doi:10.1080/00207543.2019.1675916

Cited by 15 publications

(10 citation statements)

References 28 publications

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“…Various researchers have studied different aspects of process capability evaluation in recent years as discussed in Geramian et al (2020). Yang et al (2019) propose a novel method to estimate the process capability indices based on the truncated data. Ahmed et al (2019) provide a detailed review of process capability studies in various processes.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of tools and techniques of quality by design in pharmaceutical process

Gijo

2021

IJPPM

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PurposeThe purpose of this article is to demonstrate the application of the tools and techniques of Quality by Design (QbD) approach in an Indian pharmaceutical drug product manufacturing company and to understand the challenges, managerial implications and lessons learned while implementing this initiative.Design/methodology/approachThis work adopts the Action Research methodology for impurity reduction in a drug product manufacturing company in India by using the tools and techniques of QbD approach. Various QbD tools like Design of experiments, process capability evaluation and control charts were effectively utilized for the study.FindingsAs a part of QbD implementation in the organization, a specific drug product was identified and the impurity level in the product was studied. Significant variables impacting the impurity were identified and the optimum levels for the significant variables were identified through design of experiments. The solutions were implemented and the impurity levels were reduced significantly.Research limitations/implicationsEven though the article is based on a single case study related to tools and techniques of QbD in a single organization in India, the identified problem is a generic product quality related issue for any pharmaceutical drug product manufacturing company. Hence the findings of this research are applicable to pharmaceutical drug manufacturing industry in general.Originality/valueThis article illustrates the systematic usage of various tools and techniques of QbD methodology in a pharmaceutical drug product manufacturing company. The usage of Design of Experiments for process optimization and application of other tools and techniques are ready references for the practitioners and novice users in the field.

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Section: Literature Reviewmentioning

confidence: 99%

Application of tools and techniques of quality by design in pharmaceutical process

Gijo

2021

IJPPM

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“…[5][6][7][8][9][10][11][12][13] More recent studies are considered by the references. [14][15][16][17] To model PCIs with uncertainty, Yalçin and Kaya 14 incorporated the fuzzy set theory. Haktanır and Kahraman 15 developed PCI models using penthagorean fuzzy sets, which is a new extension of intuitionistic fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

“…Meng et al 16 studied PCIs with non-normal data such as gamma quality characteristic data. Yang et al 17 considered PCI models based on the truncated normal distribution.…”

Section: Introductionmentioning

confidence: 99%

Confidence intervals of the process capability index Cpc$C_{pc}$ revisited via modified bootstrap technique and ROC curves

Ouyang

Dey²,

Byun

et al. 2023

Quality & Reliability Eng

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We consider the process capability index (PCI), a widely used quality‐related statistic used to assess the quality of products and performance of monitored processes in various industries. It is widely known that the conventional PCIs perform well when the quality process being monitored has a normal distribution. Unfortunately, using the indices to evaluate a non‐normally distributed process often leads to inaccurate results. In this article, we consider a new PCI, , that can be used in both normal and non‐normal scenarios. The objective of this article is threefold: (i) We provide a corrected form of the confidence interval for . (ii) We compare the performance of three nonparametric bootstrap confidence intervals (BCIs) for . Specifically, the standard bootstrap, percentile bootstrap, and bias‐corrected percentile bootstrap. Under various distributional assumptions such as the normal, chi‐square, Student t, Laplace, and two‐parameter exponential distributions, the estimated coverage probabilities and average width of the confidence intervals and BCIs for are compared. (iii) The power of the respective bootstrap approaches is evaluated by using the equivalence relation between confidence interval construction and two‐sided hypothesis testing. We also provide the receiver operating characteristic curves to evaluate their performance. Finally, as an illustrative example, an actual data set related to groove dimensions (in inches) measured from a manufacturing process of ignition keys is re‐analyzed to illustrate the utility of the proposed methods.

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“…Es en este caso, que combinando el enfoque holístico y las características del Seis Sigma que se plantea la aplicación en los inventarios, el almacenamiento y distribución de productos en la industria de retail para construcción, considerando que un mal manejo de información de la cadena logística disminuye la confianza entre proveedores y clientes, confundiendo también la toma de decisiones [12]. Tomando el caso de los inventarios, una mala planificación de las compras sin sustento de un modelo de gestión de inventarios puede crear dos escenarios: Mantener altos niveles de inventarios sin generar rotación de los productos y por ende incrementando los costos de mantenimiento de los mismos y de capital; y por otro lado, el desabastecimiento de determinado producto que el cliente requiere, lo que genera costo por ventas perdidas y en el peo r de los casos la pérdida del cliente y por consiguiente la reputación de la organización se puede ver afectada [13] [14].…”

Section: Introductionunclassified

Six Sigma and improvement of the logistics process in the construction retail industry

Ascón¹,

Natividad²,

Sotelo³

et al. 2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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Objective: Apply Six Sigma to improve the logistics process of products in the construction retail ind ustry. M ateria l a nd methods: The research design is pre-experimental with two observations of applied type, longitudinal, explanatory and quantitative. The population of objects was conformed by the porcelain and ceramic line. Observation cards, historical database and simulation withCrystal Ba ll software were applied. Results: Six Sigma reduced the process time from 788.49 seconds to 616.08 seconds, which was simulated 10 0 00 tim es to determine the sigma level (σ), registering the increa se fro m 1 . 96 a 3.99. The forecast was made with ARIMA (1,0,0) to project the demand and apply the EOQ model, taking into account that The coefficient of variation of the demand was 0.07548. The ord er q ua ntity a nd the reorder point, after simulating 15 situations of 10 000 times with Op t Quest, were 5800 and 600 units respectively, thus registering a d ecrease in S/. 29 144 to S/ 26 460. The new distribution of the warehouse was improved by reducing displacements from 41.05 to 31.78 meters. Deliveries improved errors from 2.36 to 0.97 per order. Conclusion: The application of Six Sigma improves the logistics process of products in the construction retail industry.

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Process capability analysis for manufacturing processes based on the truncated data from supplier products

Cited by 15 publications

References 28 publications

Application of tools and techniques of quality by design in pharmaceutical process

Application of tools and techniques of quality by design in pharmaceutical process

Confidence intervals of the process capability index Cpc$C_{pc}$ revisited via modified bootstrap technique and ROC curves

Six Sigma and improvement of the logistics process in the construction retail industry

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