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Reliability
Sunday, May 25, 2025
Duration: 3 days   Introduction: Reliability has been involved deeply in the everyday lives of human beings and has gained increasing attention in many industries. JMP has dedicated for many years to develop a comprehensive suite of platforms for reliability data analysis and reliability engineering. The suite supports not only mature and traditional methodologies, but also leads the development in the research forefront. This course is to teach the audience how to analyze product life and accelerated testing.   Purposes: This course will cover the statistical methods used in the reliability analysis and provide hands-on practice of using software. During the course, the trainees will gain a comprehensive and deep understanding about statistical tools for various reliability problems and learn how to use JMP to analyze them. This course aims to equip trainees with ready-to-use skills for their daily tasks, enabling them to provide valuable inputs to management and business decisions.   Who Should Attend: This course is designed for those whose work is related to product reliability, including students, faculty members, design or test engineers, managers, and consultants. This course will cover basic statical theories, complex reliability concepts, and detailed guide to how to use the software that are all essential for accomplishing common or sophisticated reliability tasks. Trainees with different backgrounds may benefit from different aspects of the course. Trainees who attend this course should already be familiar with the basic use of JMP software, which includes understanding the structure of JMP data table, the concept of JMP platform, and the relationship between data column and variables in platform launch dialog. For statistical background, trainees must have a basic understanding of statistical distribution and linear regression.   Content: • Day 1 o Basis of reliability analysis o Types of reliability data and metrics of interest o Nonparametric estimates and confidence intervals o Weibull and lognormal distributions o Definition and applications of probability plot o Maximum likelihood inference, parametric estimates, and confidence intervals o Parametric model selection o Bayesian statistical methods for reliability o Failure modes and analysis o Warranty analysis (reliability forest) o Reliability demonstration and reliability test plan   • Day 2 o Analysis of accelerated life-test (ALT) data o Overview of ALT methods o Physics-based acceleration models o Temperature-accelerated life tests o ALT with two accelerating variables o Design of ALT o Varying-stress and step-stress models o Degradation modeling o Accelerated repeated measures degradation o Bayesian analysis of accelerated repeated measures degradation o Accelerated destructive degradation. o Fatigue Model（JMP 18 New Feature） o Overview of Fatigue Model (also known as S-N curve modeling) o Introduction of Fatigue Models o Fatigue data analysis   • Day 3 o Reliability analysis of non-repairable systems o Introduction to reliability block diagram (RBD) o Metrics of system reliability o Components of RBD and configuration settings o Analysis of system reliability o RBD based reliability allocation o Reliability analysis of repairable systems o Introduction to repairable systems simulation (RSS) o Components, events, and actions in RSS o RSS based outage time analysis o RSS based budget planning   Register
Practical Statistics for CMC in the Pharmaceutical Industry
Sunday, May 25, 2025
Duration: 3 days   Course Introduction: The rapid development of the pharmaceutical industry, the intensification of competition in the pharmaceutical market, and the increasing regulatory requirements have made the value of statistical analysis in pharmaceutical enterprises increasingly valued. The application of statistical analysis can enable pharmaceutical companies to conduct research and development, production, and quality control more accurately and scientifically, helping them better understand and control product quality, optimize production processes, reduce costs, and improve market competitiveness. This training course starts from practical applications and introduces commonly used statistical analysis methods in the CMC end of the pharmaceutical industry, including experimental design, quality process control, stability analysis, data management, and visualization data exploration. This will enable students to master practical statistical techniques and better apply these techniques to statistical analysis in the pharmaceutical industry.   Training objectives: • Systematically and comprehensively learn various types of experimental designs and other statistical analysis methods. • Ensure mastery of the application steps and precautions of statistical analysis methods and tools through practical case drills, and guide students to integrate statistical methods into practical work. • Help enterprises effectively improve research and development efficiency, optimize process flow, achieve quality improvement, and reduce improvement costs.   Participants: • Research personnel, R&D engineers, and managers who focus on process development and optimization, analytical method development and evaluation. • Engineers and managers responsible for enterprise quality management, process improvement, and process improvement.   Content： • Introduction to JMP • Data management: Efficient data integration and cleaning • Obtain • Integrate • Cleaning • Define • Data visualization: quality analysis and improvement, rapid/dynamic presentation of results; Deviation management; Quickly identify major quality issues, etc • Graph builder • Bubble chart • Fish bone chart • Pareto chart • Descriptive statistical analysis: Statistical summary of key parameters for product annual quality review, weekly and monthly reports • Calculation of basic statistics • Normal distribution test • Interactive analysis • Hypothesis testing: substrate/process/site/material change management; Instrument/equipment status verification, replacement; Determination of product storage period; Comparison of consistency between original and generic drugs; Comparison of consistency in method transfer, etc • Basic knowledge of hypothesis testing • Single sample mean test • Double sample mean test • Single factor analysis of variance • Equivalence test • Regression analysis (single/multiple factor): correlation between various parameters and key quality attributes; Verification management, etc • Basic principles • Construction • Diagnosis • Predictions • DOE: Process/analysis method development, optimization, method tolerance evaluation, design space determination, etc • Basic principles of DOE • Full factorial design • Classic screening design • Response surface design • Custom design • Definitive screening design • Stability analysis: • Stability test based on Q1E • Batch consistency comparison • Statistical process control: monitoring of key quality parameters/specification parameters; Formulation of internal control standards for enterprises, etc • Basic knowledge of control charts • Control chart drawing • Process capability analysis • Multivariate analysis: confirmation of scaled down models, process monitoring and fault diagnosis, batch consistency comparison • Principal Component Analysis (PCA) • Nonlinear fitting: Biological assay and comparison of similarity with dissolution curve • Parallelism test, F2 value calculation • Data Mining: Discovery of Gold Batch and Problem Batch • Partition (decision tree) • Introduction to workflow: Implementing automated data analysis • Question and Discussion   Course features: • Combining theory with practice, with a large number of examples to assist teaching, easy to understand. • The content is extensive, covering commonly used statistical analysis methods in the field of pharmaceutical CMC. • During the training period, enjoy free access to JMP genuine software to ensure the effectiveness of the training. • After training, you can choose to participate in JMP certification and obtain corresponding qualification certificates.   Register
Design of Experiments
Sunday, May 25, 2025
Duration: 3 days   Introduction： DOE (Design of Experiments) is a scientific method of researching and studying the relationship of many factors and response variables. By selecting reasonable experimental conditions, reducing the number of experiments for as much as possible, and efficient data analysis, DOE can find the optimal improvement scheme. Therefore, proper use of DOE can help enterprises shorten research and development time, reduce experimental costs, and predict product or process performance by the precise statistical model, thereby optimizing the setting of process parameters and improving key product performance.   Purposes： • Systematically, comprehensively, and step-by-step learn various experimental design types and analysis methods. • Improve understanding of design and analysis of experiments, avoid solving problems by speculation. • Learn operational steps of design and analysis of experiments through various practical case studies. • Apply the tools of experimental design and analysis to daily work, to improve product quality, design, and production efficiency.   Participants： • Analysts who are responsible for the implementation of DOE. • Engineers and managers from departments of R & D, quality, production, engineering, process, and so on. • Consultants, or scientific research personnel focusing on quality management, continuous improvement, and Six Sigma management. • Teachers and students from industry engineering and management school. • Anyone interested in the JMP design of the experiment is welcome to participate.   Distinguishing Features： • Linking theory with practice, building a comprehensive DOE system framework, gaining hands-on experience on DOE through case studies from typical scenarios in the manufacturing industry • In addition to classical DOE, advanced DOE techniques and new concepts such as custom design, definitive screening design (DSD), augment design, space filling design, and functional DOE would be introduced • Utilizing JMP visualization features (graph builder, prediction profiler, contour profiler, surface profiler, mixture profiler, Monte Carlo simulator, design space profiler, etc.) to improve analytical efficiency throughout the course • Enjoy the privilege of experiencing the latest version of JMP Pro 18 for free to optimize training efficiency and understanding of cutting-edge functionalities   Register