Six Sigma Black Belt (2007 BOK): Measure

Duration: 120.0

Six Sigma measurement systems are vital to improving an organization's processes. Measurement systems encompass the conditions, devices, and the human element of measurement, which together must produce correct measurements and comply with appropriate standards. Measurement error, or measurement variability, is a problem whose components must be thoroughly understood and kept in check to maintain the effectiveness of any measurement system. Measurement variability contributes to the overall variability in the process and it is important to understand its sources and minimize it. Black Belts can calculate correlation, bias, linearity, stability, reproducibility, and repeatability to analyze and further improve measurement systems. This course examines how to analyze a measurement system to help it produce correct measurements and minimize its proportion of variability in the overall variability. It introduces key elements of metrology and international systems of measurement, explores the many sources of measurement error, and surveys a broad range of items that can be measured in various functional areas of the enterprise. The course also presents some of the considerations influencing the use of measurement systems in service industries. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft's ASQ-aligned Green Belt curriculum.

• classify the source of error in a measurement scenario
• recognize the components and meaning of measurement error
• recognize how an instrument's attributes should be considered when setting calibration intervals
• recognize the appropriate consideration of required elements for developing a traceability document
• use agreement values to interpret measurement data, in a given scenario
• calculate and interpret bias as a percentage of tolerance, in a given scenario
• interpret a linearity plot
• assess the stability status of a measurement system based on an x bar and R chart
• use the formulas for repeatability and reproducibility to evaluate a measurement system, in a given scenario
• match examples of performance measures to functional areas
• identify considerations related to measurement in a service context

Duration: 105.0

To improve the processes behind an organization's products and services, a Six Sigma Black Belt must measure them. Among the many Six Sigma tools, several are designed specifically to identify and prioritize process input and output variables and their importance relative to customer or business requirements. Using cause-and-effect matrices, Black Belts can determine which process inputs to target first. Using process efficiency formulas, they can determine the ratio of value-added time to total lead time, then enhance this ratio by reducing that troublesome drag on lead time – work in process. With metrics established, Black Belts can recommend approaches to balance the flow of processes and determine the impact that 'hidden factories' could have on process flow metrics. Looking closer at the steps of a given process, Black Belts are then able to wield a number of analysis tools such as flowcharts, spaghetti diagrams, process maps, value stream maps, and gemba walk to reveal lurking time traps, constraints, and wasted steps – all with a view of improving process characteristics for optimum efficiency. This course provides strategies to measure the current state of an organization's processes by analyzing the variables of its processes, using metrics to calculate process flow performance, and employing tools to analyze processes. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft's ASQ-aligned Green Belt curriculum.

• calculate rankings and match input variables to their relative significance
• use the formula for calculating process cycle efficiency (PCE)
• calculate the desired amount of work in process (WIP) and predict the consequent improvement in PCE
• identify the benefits of reducing WIP
• match value flow concepts to definitions
• calculate takt time and determine the best option for streamlining a process to meet customer demand, in a given scenario
• recognize examples of how "hidden factories" negatively impact organizational processes
• identify steps for creating a spaghetti diagram
• recognize best practices for using a gemba walk
• match process analysis tools to descriptions of their use
• sequence activities involved in conducting a value stream analysis
• interpret elements of a value stream map

Duration: 120.0

Organizations need to make inferences about a population from sample data, and understanding how to calculate the probability that an event will occur is crucial to making those inferences. In a Six Sigma context, it is often important to calculate the likelihood that a combination of events or that an ordered combination of events will occur. Understanding probabilities can provide Black Belts with the tools to make predictions about events or event combinations. To make accurate inferences about a population from the sample data collected in the Measure stage, Black Belts must also be familiar with the characteristics of various probability distributions, and their suitability for different types of data. Understanding the behavior of probability distributions allows the Black Belts to find the probability that values will be found within a given range, and thus to provide information on the variation in the organization's processes and products. This course provides Black Belts with basic information on probabilities and probability distributions, from the frequently used normal, Poisson, and binomial distributions, to the more specialized hypergeometric, Weibull, bivariate, exponential, and lognormal, as well as the distributions that test hypothesis and set confidence intervals: Chi-square, Student's t, and F distributions. When chosen appropriately to represent the data, these distributions will provide information on process and product variation, and support subsequent inferences based on sample data. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft's ASQ-aligned Green Belt curriculum.

• calculate the probability of compound events in a given scenario
• use the appropriate formula to calculate the number of combinations or permutations in a given scenario
• choose the appropriate discrete distribution for a given study
• identify equivalent approximations and conditions under which they hold true
• choose the most suitable continuous probability distribution to use for a given scenario
• recognize the characteristics and applications of lognormal, exponential, Weibull, and bivariate distributions
• choose the appropriate distribution formula and use it to find probability, for a given scenario
• use the Z-score formula and normalized Z-table to calculate cumulative probability of a value, in a given scenario
• calculate the mean and standard deviation for binomial data
• calculate probability using the hypergeometric distribution formula
• recognize whether or not the hypergeometric distribution should be used and why, in a given scenario
• match Chi-square, Student's t-distribution, and F distribution to descriptions of when they are typically applied

Duration: 144.0

Generally taken near the end of a program, Final Exam: Six Sigma Black Belt (2007 BOK): Measure enables the learner to test their knowledge in a testing environment.

• Topic T2 Objective O4
• Topic T6 Objective O8
• Topic T10 Objective O12
• Topic T14 Objective O16
• Topic T18 Objective O20
• Topic T22 Objective O24

Duration: 119.0

In any improvement initiative, organizations must determine whether their existing processes meet the targets and specifications demanded by the customer, or by the business. Measuring and analyzing the capability and performance of a process under review enables organizations to numerically represent and interpret its current state, and to report its sigma level. When done correctly, process capability analyses enable Black Belts to precisely assess current performance in light of future goals, and ultimately, to determine the need and targets of process improvement. Process capabilities can be determined for normal and non-normal data, variable (continuous) and attribute (discrete) data, and for long- and short-term alike. This course explores key considerations and calculations used in determining process capability and performance. This includes choosing parameters, verifying the stability and normality of a given process, and gathering and interpreting capability and performance data using common indices. The course also explores the special treatment of non-normal data and attributes data in the context of a capability study and long-and short-term capability. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft's ASQ-aligned Green Belt curriculum.

• recognize how specification limits, process limits, and process spread help determine process capability
• calculate process performance using metrics for yield, defect, and sigma levels
• use appropriate process capability and performance indices to assess a given process
• identify suitable approaches for identifying characteristics, tolerances, and specifications in a process capability study
• match methods of testing normality to their descriptions
• recognize the characteristics of short-term and long-term capability
• recognize how to process non-normal data in a capability study
• match attribute control charts with the circumstances in which they can be used to determine process capability

Duration: 140.0

An organization's success depends upon how it delivers on its processes. Before Black Belts can begin to improve an organization's processes, they must collect data to measure current processes using appropriate methods and tools. Successful data collection starts with careful planning and a knowledge of various data types, measurement methods, and sampling techniques. Black Belts also need to be aware of best practices for ensuring data accuracy and integrity. As Six Sigma team leaders, Black Belts help to oversee careful data collection efforts during the Measure phase of the Six Sigma DMAIC process. This course prepares Black Belts for successful data collection by surveying the types of data, measurement methods, and scales; sampling techniques; and collection methods available. It offers guidance for ensuring data integrity, pointing to different collection methods for different informational needs, and recommending best practices for front-line data collectors. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft's ASQ-aligned Green Belt curriculum.

• determine what type of data to collect in a given scenario
• match measurement tool categories to descriptions
• recognize an example of the correct application of the rule of ten
• match measurement scales to associated statistical analysis tools
• match sampling methods with applications suitable to their use
• recognize appropriate applications of subgroup and block sampling
• recognize the use of best practices for ensuring data accuracy and integrity in data collection
• label types of measurement system studies according to whether they test accuracy or precision
• recognize the use of best practices for ensuring data accuracy and integrity in data collection
• sequence the steps in a process for cleaning data
• identify the advantages of automated data collection
• sequence the steps in the data mining process