Training
These courses combine lectures, software demonstrations, question-and-answer sessions and hands-on computer workshops for an interactive learning experience.
* Note: Training fees only cover the training and working luncheon. Other services shall be at the attendee's own expense.
Have questions? Please contact DiscoveryChina@jmp.com
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Date/Time: Monday, May 15, 9:00 - 16:30 – Wednesday, May 17, 9:00 - 16:30
Location: Shanghai
Instructors: Juliana Zhou, Systems Engineer, JMP China
Early Registration Price: ￥5800 (register before April 30)
Standard Price: ￥6500Course Description: Design of experiments (DOE) is a scientific way to research and study relationships among many factors and response variables. By selecting reasonable experimental conditions, DOE can identify the optimal improvement scheme and reduce the number of experimental runs. Therefore, DOE can help enterprises shorten R&D time, cut down on experiment costs, predict product or process performance by the precise statistical model and optimize product quality and process settings.
Purpose: Master basic principles of experimental design and analysis; improve understanding of design and analysis of experiments, avoid solving problems by speculation; learn operational steps of design and analysis of experiments through practical case studies; apply the tools of experimental design and analysis to daily work to improve product quality, design and production efficiency.
Who Should Attend:
- Analysts who are responsible for implementation of DOE.
- Engineers and managers from departments of R&D, quality, production, engineering, process.
- Consultants, or scientific research personnel focusing on quality management, continuous improvement and Six Sigma management.
- Teachers and students from industry engineering and management schools.
Distinguishing Features: This course will link theory, emphasizing case studies. It will introduce some new concepts and techniques, such as custom design and tolerance design. Students will benefit from hands-on work with JMP to improve training efficiency.
Content:
- DOE Fundamentals
- Introduction to DOE
- Descriptive statistics
- Data Type and Distribution
- Descriptive statistics Terminologies
- Normal Distribution
- Normal distribution test
- Data Type and Distribution
- Data Visualization
- Introduction of Graph Builder
- Exploratory Data Analysis
- Contour profile
- Predict profile
- Surface plot
- Hypothesis & Test
- Basics of hypothesis & test
- One-sample hypothesis & test
- Two-sample hypothesis & test
- Multi-sample hypothesis & test
- Regression
- Basics of regression
- Simple linear regression
- Polynomial regression
- Multiple regression
- Classical DOE
- Basics of DOE
- Full factorial design
- Screening design
- Response surface design
- Multiple responses design
- Advanced DOE
- Custom design
- Augment design
- Design diagnostics and evaluation
- Definitive Screen Design
- Questions and Discussion
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Date/Time: Monday, May 15, 9:00 - 16:30 – Wednesday, May 17, 9:00 - 16:30
Location: Shanghai
Instructors: Weihua Li, Systems Engineer, JMP China
Early Registration Price: ￥5800 (register before April 30)
Standard Price: ￥6500Course Description: 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.
Purpose: 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.
Distinguishing Features: This course will link theory with practice, emphasizing case studies. It includes a wide range of content, including life test design, accelerated test design, degradation, corresponding data analysis, systems reliability analysis and other methods. Students will enjoy a free trial of JMP to improve training efficiency.
Day 1
o Basis of reliability analysis
- Types of reliability data and metrics of interest
- Nonparametric estimates and confidence intervals
- Weibull and lognormal distributions
- Definition and applications of probability plot
- Maximum likelihood inference, parametric estimates, and confidence intervals
- Parametric model selection
- Bayesian statistical methods for reliability
- 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
- Overview of ALT methods
- Physics-based acceleration models
- Temperature-accelerated life tests
- ALT with two accelerating variables
- Design of ATL
- Varying-stress and step-stress models
o Degradation modeling
- Accelerated repeated measures degradation
- Bayesian analysis of accelerated repeated measures degradation
- Accelerated destructive degradation
Day 3
o Reliability analysis of non-repairable systems
- Introduction to reliability block diagram (RBD)
- Metrics of system reliability
- Components of RBD and configuration settings
- Analysis of system reliability
- RBD based reliability allocation
o Reliability analysis of repairable systems
- Introduction to repairable systems simulation (RSS)
- Components, events, and actions in RSS
- RSS based outage time analysis
- RSS based budget planning
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Date/Time: Monday, May 15, 9:00 - 16:30 – Wednesday, May 17, 9:00 - 16:30
Location: Shanghai
Instructors: Mandy Xi, Systems Engineer, JMP China
Early Registration Price: ￥5800 (register before April 30)
Standard Price: ￥6500Course Description: Statistical quality control, namely SQM (Statistical Quality Management) is to use mathematical statistics methods to analyze and solve problems, reduce the variation of production and service process, it has become an integral part of modern Quality Management system, also is the most important core contents of Six Sigma Management. Implementation and promotion of statistical quality management can help enterprises to establish a "Data Driven Decision" quality management consciousness, the enterprise overall quality management level rise a step, and highly increase customer satisfaction.
Purpose:
- Guide trainees to set up decision based on the "Data and the Objective Facts" and the concept of confidence.
- Comprehensively learning from quality data management to design of experiments and other commonly used statistical quality management methods.
- Eliminate subjective, objectively quantify various uncertain factors in the real work, confirm the key factors influencing process variation and quality performance, and enhance the ability to solve the problem.
Who Should Attend:
- Professionals who are responsible for implementation of SQM, Six Sigma.
- 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.
Distinguishing Features:
- Link theory with practice, with an emphasis on case studies.
- Wide range of content, including data management, hypothesis tests, regression, SPC, MSA, DOE, data mining and other major statistical quality management techniques.
- A free trial of JMP software to improve training efficiency.
Content:
Day 1
- Brief introduction of JMP
- Data Management
- Fundamentals of data
- Access data
- Integrate data
- Clean data
- Define data
- Explore data
- Data Visualization
- Fishbone chart
- Pareto plot
- Bubble plot
- Graph Builder
- Descriptive Statistics
- Computing basic statistics
- General statistical graph
- Normality test
- Interactive association analysis
Day 2
- Hypothesis Test
- Basics of hypothesis test
- One sample mean hypothesis test
- Two sample means hypothesis test
- Sample size and statistical power
- Equivalence Test
- One-way ANOVA
- Introduction of Proportion Test
- Introduction of Nonparametric Test
- Regression
- Basics of regression
- Regression building
- Regression diagnosis
- Regression prediction
- Data Mining
- Partition (decision trees)
Day 3
- Statistical Process Control
- Basics of SPC
- Variable control chart
- Attribute control chart
- Process capability analysis
- Measurement System Analysis
- Basics of MSA
- Bias and linearity
- Variable Measurement System Analysis
- Attribute Measurement Systems Analysis
- Design of Experiments
- Basics of DOE
- Full factorial design
- Response surface design
- Brief Introduction of Custom Design
- Q & A
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