Operational process understanding is the basis for ensuring quality and maximising yields. Without operational process understanding, process output often is not predictable nor capable. This may be sufficient in some areas but for most processes, a more scientific and data-based approach is required.
This is an introductory course and the first of three within the topic of data analysis and design of experiments (DoE). In this module, you will learn the classical textbook approach to data analysis and DoE with focus on hypothesis testing, regression analysis and DoE. Furthermore, you will be introduced to practical statistical tools that you can use in your daily work.
This course is relevant for you if you are e.g. working with development, optimisation or process/product trouble shooting. You want to learn the basic statistical concepts behind hypothesis testing, regression analysis and DoE.
If you are participating in a Six Sigma Green Belt or Black Belt course, this module will be mandatory. The covered tools will become useful when working with data analysis on your DMAIC project.
After participating in this course, you will be able to:
- Do basic statistical analysis based on historical data
- Find correlations between causes (x) and effects (y) by performing linear and multiple regression analysis without interactions
- Set up and execute hypothesis tests with focus on distinguishing between statistical and practical significance
- Brainstorm and prioritise critical process parameters (CPPs) that are will be varied in a DoE
- Screen many potential critical factors with few experiments in a definitive screening design that can handle curvature and interactions
- Hypothesis testing – choosing the right test
- Linear regression – Correlation between cause (x) and effect (y)
- Multiple regression – Correlation between multiple causes (x’s) and effect (y)
- DoE - definitive screening – Examine what factors have a main effect on the response
- DoE catapult exercise – definitive screening
The preferred software for this course is JMP from SAS and the instructors will demonstrate all of the exercises in JMP. You are also welcome to use Minitab for the exercises where it is possible and will still be able to receive assistance from our instructors.
However, even if you are currently not a JMP user, we recommend you to install JMP on your computer before participating in the advanced courses, since some of the exercises will only be possible to perform with JMP.
This is an introductory course and the first of three within the topic of data analysis and DoE. The two other courses are the following:
- Data-driven root cause analysis and design of experiments (DoE) 2
- Data-driven root cause analysis and design of experiments (DoE) 3
This module only covers how to use the mentioned practical tools and not why you should use them. If you want to understand why these practical tools always work and how you can justify their application, then we strongly recommend you to participate in the advanced modules. These advanced courses will also provide you with more applied statistical tools needed when working with more data-heavy tasks and projects.
If you want to devote more focus on the development of performance and processes, you can get inspiration from methodical courses such as Design for Six Sigma or Quality Function Deployment offered by Storm Management. These courses will help you map and operationalise customer demands and develop future processes.
Furthermore, you have the opportunity to continue your education as a Six Sigma Green Belt or Black Belt, and become more competent and able to optimise, lead and develop processes.
Venue: NNE's headquarters, Bredevej 2, 2830, Virum, Denmark
Price: DKK 5,000 + VAT (includes course materials, refreshments and lunch)
Language of instruction: The course will be held in English unless all participants speak and understand Danish
Duration: 1 day
Course dates in 2018
- March 6
- May 22
- September 11
- November 20