DUBLIN--(BUSINESS WIRE)--Research and Markets (http://www.researchandmarkets.com/research/34a710/six_sigma_black_be) has announced the addition of the "Six Sigma Black Belt (2007 BOK): Measure" training to their offering.
The Six Sigma Black Belt (2007 BOK): Measure training bundle consists of the following six courses:
(1) Process Characteristics for Six Sigma (2) Data Collection and Measurement in Six Sigma (3) Six Sigma Measurement Systems (4) Basic Statistics and Graphical Methods for Six Sigma (5) Probability for Six Sigma (6) Process Capability for Six Sigma
Each course is broken down further into a series of lessons as follows:
(1) Process Characteristics for Six Sigma
Overview/Description
To improve the processes behind an organization's products and services, a Six Sigma Black Belt must measure them. But first, they must identify those processes. Among the many Six Sigma tools, several are designed specifically to isolate relevant process variables, determine their relationships to each other, prioritize their importance relative to customer or business requirements, and assess their efficiency. Using SIPOC and cause-and-effect matrices, Black Belts can determine which process inputs to target first those with the most significant impact on important outputs. 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 involving takt time, one-piece flow, and pull to balance the flow of processes, eliminating the inefficiencies of work in process, cutting overhead budgets, and reducing lead time. Looking closer at the steps of a given process, Black Belts are then able to wield a number of analysis tools such as spaghetti and Venn diagrams, process maps, and value stream maps 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 improve 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. It connects these tools to the overarching goals of eliminating wasted time, highlighting free time, and increasing the proportion of value-added time. 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.
Target Audience
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Prerequisites
Proficiency at the Green Belt level with process variables and analysis tools as scoped in the ASQ - Six Sigma Green Belt Body of Knowledge (BOK)
Expected Duration (hours) 2.0
Lesson Objectives
Process Characteristics for Six Sigma
- identify key concepts relating to the use of SIPOC diagrams and cause-and-effect matrices for identifying and prioritizing process variables
- 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
- identify steps for creating a spaghetti diagram
- use a Venn diagram to find potential free time in an employee's schedule
- 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
(2) Data Collection and Measurement in Six Sigma
Overview/Description
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 measure those processes with the appropriate data. The crucial steps of data collection and measurement precede process improvement in any Six Sigma initiative. Successful data collection starts with careful planning; a knowledge of various data types, sampling strategies, and measurement methods; and an ongoing awareness of best practices for ensuring data accuracy and integrity. Only reliable and suitable data will yield dependable analyses that translate into desired process improvements. As Six Sigma team leaders, Black Belts will help to oversee careful data collection efforts during the Measure phase of the Six Sigma DMAIC process. They will determine what should be measured, how data should be collected, and what tools can be employed to gather data as the basis for further improvements. This course prepares Black Belts for successful data collection by surveying the types of data, measurement scales, sampling methods, and collection techniques available. It offers guidance for ensuring data integrity, pointing to different collecting methods for different informational needs, and recommending best practices for front-line data collectors. It compares the relative advantages of both manual and automated data collection, and surveys the wide variety of tools available for measuring the properties of an organization's products or services. 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.
Target Audience
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Prerequisites
Proficiency at the Green Belt level with Six Sigma data collection concepts as scoped in the ASQ Six Sigma Green Belt Body of Knowledge (BOK)
Expected Duration (hours) 2.0
Lesson Objectives
Data Collection and Measurement in Six Sigma
- determine what type of data to collect in a given scenario
- recognize how to convert data into a different data type
- match measurement scales to associated statistical analysis tools
- recognize the use of best practices for ensuring data accuracy and integrity in data collection
- match sampling methods with applications suitable to their use |w
- identify the advantages of automated data collection
- sequence the steps in the data mining process
- match measurement tool categories to descriptions
- recognize an example of the correct application of the rule of ten
- classify examples of tests as destructive or nondestructive
(3) Six Sigma Measurement Systems
Overview/Description
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.
Target Audience
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Prerequisites
Proficiency at the Green Belt level with Six Sigma measurement systems as scoped in the ASQ - Six Sigma Green Belt Body of Knowledge (BOK)
Expected Duration (hours) 2.0
Lesson Objectives
Six Sigma Measurement Systems
- 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
(4) Basic Statistics and Graphical Methods for Six Sigma
Overview/Description
Organizations must ensure that their processes and products are extremely consistent, as variations can lead to rejected orders, lower revenues, and eventually, financial disaster. Basic statistics can provide Black Belts with the tools to summarize and assess collected data in a meaningful way. Black Belts can use descriptive (enumerative) statistics to tabulate and graphically represent sample data through a number of informative charts and diagrams. Using analytical (inferential) statistics, supported by the central limit theorem, Black Belts can confidently make inferences about the larger population based on their sample data, and can test the statistical validity of their inferences. Thus, basic statistics can provide an organization with a view of its performance in graphical format, and the tools for reaching valid conclusions regarding its processes and products. This course provides Black Belts with basic statistical tools for describing, presenting, and analyzing sample and population data. It explores the process of preparing and presenting sample data using graphical methods and then making valid inferences about the population represented by the sample. 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.
Target Audience
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Prerequisites
Proficiency at the Green Belt level with Six Sigma basic statistics as scoped in the ASQ - Six Sigma Green Belt Body of Knowledge (BOK)
Expected Duration (hours) 2.0
Lesson Objectives
Basic Statistics and Graphical Methods for Six Sigma
- match measures of central tendency to their characteristic advantages and limitations
- calculate measures of dispersion in a given scenario
- construct a cumulative frequency diagram in a given scenario
- recognize how to set class intervals for frequency distributions
- predict and interpret the histogram shape that would result from a given frequency distribution
- recognize how to use normal probability plots to determine whether data is normally distributed
- identify statements that reflect correct interpretations of a complex box plot
- identify the best interpretation of a given run chart
- recognize how to use a scatter plot to find the optimum target value and tolerance zones for a process parameter
- recognize the significance of the central limit theorem for inferential statistics
- match tools for drawing valid statistical conclusions to descriptions of their use
(5) Probability for Six Sigma
Overview/Description
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 SkillSofts ASQ-aligned Green Belt curriculum.
Target Audience
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Prerequisites
Proficiency at the Green Belt level with Six Sigma probability computations and distributions as scoped in the ASQ - Six Sigma Green Belt body of knowledge (BOK)
Expected Duration (hours) 2.0
Lesson Objectives
Probability for Six Sigma
- 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
(6) Process Capability for Six Sigma
Overview/Description
In any improvement initiative, organizations must determine whether their existing processes meet the targets and specifications demanded by the business, or by the customer. 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, and for variable (continuous) and attribute (discrete) data alike. This course explores the considerations, verifications, and associated calculations used to conduct process capability studies, from choosing parameters and verifying the stability and normality of a given process, to gathering and interpreting capability and performance data using common indices. It also explores the special treatment of non-normal data and attributes data in the context of a capability study. 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.
Target Audience
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Prerequisites
Proficiency at the Green Belt level with basic concepts in the measurement and analysis of process capability as scoped in the ASQ - Six Sigma Green Belt body of knowledge (BOK)
Expected Duration (hours) 2.0
Lesson Objectives
Process Capability for Six Sigma
- 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
- identify recommendations for sampling data
- choose the best recommendation for process improvement, given the results of a process capability study
- interpret the capability and performance index results in a process capability study
- 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
Reviews:
E-learning course feedback:
e-learning has dramatically increased the scope, range and availability of training & development to all Equant employees. No other solution could possibly have achieved this in such a short timescale, particularly given the geographical dispersement of our workforce and the commercial realities of our business. Peter Mansell, Head of Training Centre of Excellence, Equant
Where e-learning is concerned, I used to be a complete agnostic. But, twelve months on, Im totally converted. Now that we have experienced such measurable benefits, I would have no hesitation in giving e-learning a 100% recommendation to any organisation. John Guthrie, Head of International Management Development, Hilton International
'Based on our experience to date there is no doubt that e-learning will have an increasingly vital role to play as the company continues to evolve to meet the changing needs of its customers. Karl Gunner, Aspire Support Consultant, Intelligent Finance
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