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Description:

This is a bundled training package. It contains training for each of the bundled items below:

Course Price
Design of Experiments and Validation of Solutions in Six Sigma $74.95
Statistical Process Control and Control Plans in Six Sigma $74.95
Using Basic Control Charts in Six Sigma $74.95

Bundle Price: $139.00
Total Savings: $85.85


Design of Experiments and Validation of Solutions in Six Sigma

"We are, I think, in the right road of improvement, for we are making experiments," said Benjamin Franklin. In the Improve stage of the DMAIC process, Six Sigma teams design and conduct experiments to study the nature of relationships between input variables and the response variable(s). They do this by controlling and changing the input variables and observing the effects on the response variable(s). After determining what and how much needs to be changed to meet the desired improvement, teams generate solution ideas to optimize the response, and then the ideas are tested, implemented, and validated. Later in the control stage, efforts are made to keep the improved processes, products, or services under statistical control and to retain the gains. This course explains the basic design of experiments (DOE) concepts and outlines how to select, test, and validate improvement solutions in the final stages of a Six Sigma project. During the course, basic DOE concepts such as factors, levels, interactions, and main effects are introduced. The course also explores the full and fractional factorial designs and the DOE process. In addition, it teaches how to select, test, and validate solutions using a variety of analysis, screening, and testing tools commonly used in Six Sigma. This course is aligned with the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.
  • match the key elements of the DOE methodology with examples
  • match each type of DOE with an example
  • distinguish between full and fractional factorial DOEs based on the number of runs, factors, and levels for each
  • sequence examples of the steps in the DOE process
  • identify examples of interactions and main effects in DOE
  • match tools that are used to identify improvement solutions with descriptions
  • identify how to evaluate and select solutions using a solution selection matrix
  • recognize when to use various tools for testing and validating improvement solutions

Statistical Process Control and Control Plans in Six Sigma

In the final stages of the Six Sigma DMAIC methodology, once process improvement opportunities are identified and implemented, you need to make sure that the improved processes are controlled to sustain the process improvement gains. Statistical process control (SPC) provides tools which can be used to ensure that the processes are continuously monitored, that results are evaluated through the use of various control charts, and that each process is prevented from reverting to its previous state. The goal of this stage is also to develop a control plan to document and hold the gains, and to assist in monitoring and implementing controls. This course aims to introduce basic SPC and control chart concepts and how to develop a control plan to hold the gains prior to the closure of a Six Sigma project. The course identifies the key objectives and benefits of SPC and explains the concept of rational subgrouping. It also introduces the different types and the key elements of control charts, and identifies control chart patterns that indicate an out-of-control process. In addition, the types of control plans and the steps used to construct a control plan are discussed. This course is aligned with the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.
  • identify the key objectives of statistical process control
  • identify the benefits of statistical control
  • recognize examples demonstrating the different strategies for rational subgrouping
  • match the key elements with descriptions of their roles in control charts
  • determine the types of control charts suitable to use for given types of data
  • identify control chart patterns that indicate a process is out of control
  • match each control plan type with a description of the type of information it provides
  • sequence the steps in each phase of the construction of a control plan
  • match the key sections of a control plan with the information they contain

Using Basic Control Charts in Six Sigma

In a Six Sigma DMAIC project, once you've measured your current processes, analyzed the gaps and causes of problems, and improved processes to the desired level, you need to monitor and control them over an extended period of time. The process may show variation, and control charts are used to determine if the variation is natural to the process or if there is another reason for the discrepancy. Control charts can also be used in other stages of Six Sigma – to examine how a process is performing over time and also to identify and analyze any special cause variations. Depending on the type of data, different types of control charts can be used. They are broadly organized into two categories: charts for variable data, and charts for attribute data. As the journey continues, findings from the control charts may be used as the beginning point for a new improvement initiative.This course deals primarily with basic control chart concepts and how they are created and analyzed in Six Sigma. It teaches methods of creating and analyzing key variable and attribute control charts. The course also identifies the control charts to use in specific situations and the various steps in the standard control charting process. This course is aligned with the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.
  • recognize which variable or attribute control chart to use in a specific situation
  • identify the major activities that are performed at each step in the standard control charting process
  • identify the four commonly applied tests that determine evidence of special cause variation
  • determine any special cause variation in data by creating and interpreting an Xbar and R chart
  • recognize which formulas to use to help determine special cause variation in an Xbar and s control chart
  • determine any special cause variation in data by creating and interpreting an ImR chart
  • calculate the center line, UCL, and LCL for a p control chart to determine if special cause variation exists
  • calculate the center line, UCL, and LCL for an np control chart to determine if special cause variation exists
  • calculate the center line, UCL, and LCL for a u control chart to determine if special cause variation exists
  • calculate the center line, UCL, and LCL for a c control chart to determine if special cause variation exists
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Six Sigma Green Belt: Improve and Control e-learning bundle
  • Course ID:
    271518
  • Duration:
    n/a
  • Price:
    $139