Business Strategy and Operations
Six Sigma Black Belt - The Measurement Phase
This course consists of multiple titles to provide you with the techniques and skills required to deliver your Six Sigma project. You will learn about Six Sigma improvement opportunities from a financial perspective:
what to consider before launching a project, the financial metrics that will be involved, and how to calculate the cost of poor quality. You'll explore ways to listen to the voice of the customer to determine
what they really need.
Six Sigma is a registered Trademark of Motorola Corporation, and all right, title, and interest in Six Sigma belongs to Motorola.
Target Audience:
Candidates for black belt certification; managers/executives overseeing personnel involved in the implementation of Six Sigma in their organization; consultants involved in implementing a
Six Sigma proposal; and organizations implementing a Six Sigma project.
Curriculum Includes:
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Six Sigma Black Belt - The Measurement Phase Training Curriculum Online
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sk6sigmeas
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$209.00
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Process Analysis and Documentation
The Six Sigma DMAIC system is a roadmap that points the way to process and performance improvement. The second phase in this methodology is Measure. You cannot hope to improve
processes and performance without first knowing where you are, assessing where you want to be, and then planning how to get there, while measuring progress toward the goal all along the way. In the
words of an old adage, what gets measured, gets done. In this course, you will learn about the steps in the Measure phase. Then you will learn key principles of measurement; learn how to identify key
process input variables and key process output variables; document their relationships through a cause and effect diagram; assign constant, noise, and experimental variables to causes in a cause and effect
diagram; and create an action plan to change noise variables to constants.
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Probability and Statistics
The Measure step of the DMAIC system used in Six Sigma relies on the use of probability and statistics to produce information on variations in process. Black Belt candidates must be proficient in
descriptive, inferential, and process-oriented statistical thinking; must understand how to stratify data; and must appreciate the uses of the Central Limit Theorem and inferential statistics to interpret
available data. This course serves as a summary for each of these areas, and provides a refresher on basic statistical methods and the uses of probability
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Collecting and Summarizing Data
The Six Sigma DMAIC system is a roadmap that shows the way to process and performance improvement. The second phase in the roadmap, the Measure phase, provides a methodology and
tools for establishing a baseline of current processes. This baseline is your starting point for process improvement. A critical part of establishing baselines is collecting data that will enable you to learn the
current status of performance and processes, monitor improvement efforts, and evaluate success at the conclusion of those efforts. Data puts teeth into process improvement and provides solid evidence that
your efforts are bearing fruit. In this course, you will learn about collecting and summarizing data. You will learn how to plan for data collection and design a useful and professional-looking data collection
vehicle, how to choose an appropriate check sheet for data collection purposes, how to create an effective sampling strategy, and how to conduct a Failure Mode and Effects Analysis (FMEA).
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Properties and Applications of Probability Distributions
Whether calculating future process capability or the length of time before product failure, probability distributions are essential to the Six Sigma� Black Belt. Distributions and their random variables are
the key to determining supportable conclusions for hypothesis tests and confidence intervals. This course discusses the general use of probability distributions, and reviews the applications of normal
and nonnormal distributions. The course also details the benefits and teaches the steps of the Student's T-test Distribution and the F-Distribution.
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Measurement Systems
The Six Sigma DMAIC system is a roadmap that shows the way to process and performance improvement. The second phase in the roadmap, the Measure phase, provides a methodology and
tools for collecting data and establishing baseline measures of current processes. However, measurement itself can be problematic. A measurement system is only as good as the measuring
instruments and the operators, and both are responsible for a certain amount of variation. An important part of process improvement--conducted before collecting measurement data--is analyzing the
measuring system to ensure that measurements are made without bias, are reproducible by all measuring instruments, and are repeatable by all operators. In this course, you will learn about
Measurement Systems Analysis (MSA). You will see how to assess properties of a measurement system, including how to plan and conduct a Gauge R&R. You will learn some important principles of
metrology and learn salient features of some common measuring instruments, including gauge blocks, calipers, and micrometers, among others.
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Analyzing Process Capability
Process capability is determined by a range of variables including people, machines, materials and measurements. The cumulative result must be quantified numerically to determine current performance
and project future potential. To do so requires a knowledge of control limits, specification limits, capability indices, and the difference between short- and long-term variability. This course teaches Six
Sigma Black Belts the process capability techniques to analyze current output and project expected value.
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Calculating Process Capability
When process data fails to produce a normal distribution, special indices are required to calculate process capability. This course teaches Black Belt candidates the indices for non-normal process
capability, and discusses how to transform non-normal data for use in standard statistical tests. It also examines the difference between continuous and discrete data, and the distributions used for capability
analysis of discrete data.
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