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American Society for Quality

Six Sigma Black Belt Certification (SSBB)

Six Sigma Black Belt certification examination

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Basics of Hypothesis Testing and Tests for Means in Six Sigma

Course Number:
oper_41_a02_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Basics of Hypothesis Testing and Tests for Means in Six Sigma

use key hypothesis testing concepts to interpret a testing scenario
recognize the implications of a hypothesis test result for statistical and practical significance
use the margin of error formula to determine sample size for a given alpha risk level
match definitions to key attributes of point estimates
distinguish between statements expressing confidence, tolerance, and prediction intervals
recognize how confidence intervals are used in statistical analysis
calculate the confidence interval for the mean and interpret the results in a given scenario
calculate the tolerance interval in a given scenario
perform key steps in a one-sample hypothesis test for means, and interpret the result
test a hypothesis using a two-sample test for means

Overview/Description
In the Analyze phase of the DMAIC methodology, Six Sigma teams analyze the underlying causes of issues that need to be addressed for the successful completion of their improvement projects. To that end, teams conduct a number of statistical analyses to determine the nature of variables and their interrelationships in the process under study. It is rarely possible to study and analyze the full scope of population data pertaining to all processes, products, or services, so Six Sigma teams typically collect samples of the population data to be analyzed, and based on that sample data, they make hypotheses about the entire population. Because there is a lot at stake in forming the correct conclusions about the larger population, Six Sigma teams validate their inferences using hypothesis tests. This course builds on basic hypothesis testing concepts, terminologies, and some of the most commonly used hypothesis tests – one- and two-sample tests for means. The course also discusses the importance of sample size and power in hypothesis testing, as well as exploring issues relating to point estimators and confidence intervals in hypothesis testing. 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

Common DFSS Methodologies, Design for X, and Robust Designs

Course Number:
oper_44_a01_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Common DFSS Methodologies, Design for X, and Robust Designs

match new-product terms to examples
determine whether or not DFSS is appropriate for a given situation, and why
identify tools and approaches that are included in DFSS methodology
match the steps of the DMADOV methodology with the questions asked and activities performed in them
identify key requirements of a DFX initiative
identify the definition of Design for X (DFX)
match design for manufacturability and producibility strategies to examples of their practical implementation
recognize how to set and use target cost when designing for cost
recognize valid circumstances for readjusting a target cost
match DFX characteristics to associated strategies for design
identify the goals of robust design
use tolerance design calculations to determine tolerance specifications in a given scenario
distinguish between worst-case tolerancing and statistical tolerancing approaches

Overview/Description
Design for Six Sigma (DFSS) is the methodology associated with the design of a process, product, or service, which results in Six Sigma output that satisfies both the external customer and internal business requirements. DFSS is an innovative strategy for the design or redesign of a process, product, or service from the ground up. This course examines several of the common methodologies utilized in Design for Six Sigma (DFSS), beginning with the two common counterparts to the DMAIC methodology: DMADV and DMADOV. Design for X is emerging as an important knowledge-based multifunctional approach to design that is aimed at particular prioritized process constraints, such as cost, manufacturability, testability, or maintainability. This course explores several constraints in more detail, offering strategies for achieving designs concentrated on the chosen criteria. Another recently developed approach, robust design, uses parameter and tolerance control to produce designs which will be reliable during manufacturing and while in use. This course will address the basic aims of parameter control, tolerance design, and statistical tolerancing. 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.

Data Types, Sampling, Collection, and Measurement in Six Sigma

Course Number:
oper_40_a02_bs_enus

Expected Duration (hours)
2.3

Lesson Objectives

Data Types, Sampling, Collection, and Measurement in Six Sigma

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

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 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.

Designing, Conducting, and Analyzing Experiments in Six Sigma

Course Number:
oper_42_a02_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Designing, Conducting, and Analyzing Experiments in Six Sigma

determine whether a chosen design is a full factorial design that can meet resolution requirements, in a given scenario
recognize the characteristics of an experiment, represented by a given run table
calculate the number of runs in a given experiment
calculate an estimate of a main effect in a full factorial experiment
based on results from a full factorial experiment, recognize which terms should be included in the model
recognize circumstances suitable for a fractional factorial design
recognize the design implications of a proposed fractional factorial experiment
interpret an interaction plot
identify conditions that recommend a randomized block design
identify the trial pattern that will fully randomize a given block design
identify the characteristics of Latin square designs
recognize which experimental factors are significant in the results of a Latin square design

Overview/Description
Six Sigma teams design and conduct experiments to investigate the relationships between input variables and response variables. By controlling and changing the input variables and observing the effects on the response variables, a Six Sigma team gains a deep understanding of these relationships. After determining what and how much needs to be changed to meet the desired improvement, teams generate solution ideas based on the best combination of input variables' settings 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 explores 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 Black Belt certification exam and is designed to assist learners as part of their exam preparation.

Determining Process Performance and Capability in Six Sigma

Course Number:
oper_40_a06_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Determining Process Performance and Capability in Six Sigma

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

Overview/Description
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.

Determining Requirements by Listening to the Voice of the Customer in Six Sigma

Course Number:
oper_39_a01_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Determining Requirements by Listening to the Voice of the Customer in Six Sigma

match the voice of the customer (VOC) strategy tasks to their descriptions
identify how to perform different aspects of a customer-segmentation analysis in a given scenario
identify examples of the three main customer-segmentation criteria
recognize considerations associated with gathering customer data
determine the most appropriate customer data collection method to use in a given scenario
recognize key concepts related to the measures for ensuring validity and reliability of data collection outcomes
identify the definitions of key terminology associated with validity, reliability, and margin of error in data collection
recognize how various tools are used to identify and analyze customer requirements
identify the characteristics of CTx requirements
classify CTx requirements in a given scenario
categorize elements of a process improvement project within a SIPOC diagram

Overview/Description
Customers are at the heart of all Six Sigma initiatives, and this focus on customers is what makes Six Sigma an outstanding organizational performance improvement program. The voice of the customer (VOC) is a Six Sigma strategy used to capture requirements and feedback from customers in order to meet their requirements. Voice of the customer is a critical input at every stage in the Six Sigma DMAIC process, particularly at the Define stage. At this stage, critical customer requirements concerning quality, cost, process, and delivery are collected and translated into measurable, actionable project goals using a number of tools. Using VOC begins with defining Six Sigma goals for collecting and analyzing customer requirements. It is imperative that Six Sigma leaders determine the critical to x (CTx) requirements concerning quality, cost, process, and delivery requirements of customers and the organization. Then the team needs to identify and select the most effective methods for collecting customer feedback and requirements. From there, customer requirements are translated into measurable, actionable project goals. This course examines how an organization uses the voice of the customer to define the problem at hand and to set the direction of its Six Sigma efforts. It discusses some common customer data collection methods – such as surveys, interviews, and focus groups – and looks at how to ensure validity and reliability in data collection. In addition, the course illustrates how tools such as CTx, SIPOC, Kano analysis, critical-to-quality (CTQ) analysis, and quality function deployment (QFD) are used to translate customer data into critical customer requirements and actionable goals for the Six Sigma team. 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.

FMEA and Other Nonstatistical Analysis Methods in Six Sigma

Course Number:
oper_41_a05_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

FMEA and Other Nonstatistical Analysis Methods in Six Sigma

interpret a failure modes and effects analysis (FMEA) worksheet to prioritize failures for improvement
recognize the distinctions and relationships between Process FMEAs and Design FMEAs
calculate the risk priority number (RPN) for a given cause of failure
identify the purpose of gap analysis in Six Sigma
sequence examples of the performance of each step in a gap analysis
recognize activities performed in the scenario planning process
identify the characteristics of scenario planning
match suggested steps in a root cause analysis to associated activities
identify errors made by a team conducting a 5 Whys analysis, in a given scenario
interpret a fault tree analysis (FTA)
classify situations as more suitable for fault tree analysis (FTA) or for failure modes and effects analysis (FMEA)
recognize the type of waste expressed in a conventional statement and associate it with Lean Six Sigma thinking for eliminating that waste

Overview/Description
Getting to the source of why something has gone wrong in a system or process is critical to identifying the changes necessary for resolving the problem. During the Analyze phase of a Six Sigma project, a Black Belt practitioner utilizes a variety of statistical and nonstatistical tools and methods for analyzing systems and processes to identify variation and defects, reduce costs, eliminate waste, and reduce cycle time. While many of the tools used in the Analyze phase are statistical and quantitative in nature, there are many useful nonstatistical methods. Nonstatistical methods help in the analysis by including qualitative considerations in identifying potential problems, their root causes, and their impacts. They help prioritize these causes and generate initial ideas for resolving problems when a project enters the Improve phase. This course covers the use of various nonstatistical analysis methods including failure modes and effects analysis (FMEA), gap analysis, scenario planning, root cause analysis, the 5 Whys, fault tree analysis (FTA), and waste analysis. 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.

Fundamentals of Lean and Six Sigma and their Applications

Course Number:
oper_36_a01_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Fundamentals of Lean and Six Sigma and their Applications

sequence key developments in the evolution of continuous improvement methodologies
recognize the impact of other continuous improvement methodologies on Six Sigma and Lean
distinguish between the Lean and Six Sigma improvement methodologies
recognize the best approach for integrating Lean and Six Sigma initiatives, given basic organizational conditions
match Lean tools with the Six Sigma stages they align to
classify a business process as a core process or support process and identify what makes it so
categorize examples of stakeholders
recognize how Lean Six Sigma was applied to a manufacturing process in a given scenario
recognize characteristics and quality considerations that are unique to service organizations
categorize examples of the three key aspects of service quality
recognize examples of service industry activities that would be good candidates for a Lean Six Sigma initiative

Overview/Description
Six Sigma is a data-driven improvement strategy that views all activities within an organization as processes. Process inputs can be controlled and adjusted to effect significant improvements in process outputs. Six Sigma uses a rigorous and systematic methodology known as DMAIC (define, measure, analyze, improve, and control) and a number of qualitative and quantitative tools. Its goal is to drive process, product, and service improvements for reducing variation and defects. Lean is also an improvement methodology, but with a different focus. It aims to enhance process flow, reduce cycle time, and eliminate waste. Though Lean and Six Sigma originated in different places and under different circumstances, they are now largely seen as complementary methodologies. Organizations across various industries are striving to become faster and more responsive to customers, achieve near-perfect quality, and operate using world-class cost structures. You need both Lean and Six Sigma to achieve these goals. This course introduces Six Sigma and Lean methodologies and looks at the relationship between them. It also explores relationships among business systems and processes using some practical examples of Lean Six Sigma applications in both manufacturing and 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.

Impact on Stakeholders and Benchmarking for Six Sigma

Course Number:
oper_37_a01_bs_enus

Expected Duration (hours)
1.5

Lesson Objectives

Impact on Stakeholders and Benchmarking for Six Sigma

recognize the steps in creating an action plan for analyzing Six Sigma stakeholders
recognize the impact of Six Sigma projects on different categories of stakeholders
distinguish realities about benchmarking from misconceptions
recognize the goals and use of key benchmarking approaches
characterize the source and scope of a benchmarking target
recognize how benchmarking can benefit a Six Sigma project
match the phases of the benchmarking process with the steps performed in them
identify recommendations for ethical conduct in benchmarking

Overview/Description
The success of Six Sigma deployment in an organization largely depends on the success of individual Six Sigma projects. Organizational stakeholders, including customers, suppliers, and employees, have a strong influence on the implementation of Six Sigma projects. In turn, these projects impact the organizational stakeholders by throwing many opportunities and challenges before them and requiring them to adapt to changes caused by improvements. Benchmarking is used in Six Sigma projects to set improvement goals against world-class and competitive reference points. Benchmarking may also be used at later stages in Six Sigma DMAIC while evaluating existing operations and incorporating best practices to maximize the success of improvement efforts. This course helps analyze key stakeholders and explore the impact Six Sigma projects can have on them. The course also discusses the concept of benchmarking, various benchmarking types, and how benchmarking is used in Six Sigma. 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.

Lean Improvement Methods and Implementation Planning in Six Sigma

Course Number:
oper_42_a03_bs_enus

Expected Duration (hours)
1.9

Lesson Objectives

Lean Improvement Methods and Implementation Planning in Six Sigma

identify best practices and methods associated with Lean tools
identify characteristics of cycle-time reduction tools and the steps in SMED
recognize the characteristics of heijunka
recognize examples of activities typically performed during each day of a kaizen blitz
recognize how to apply Theory of Constraints concepts to help analyze process throughput and alleviate bottlenecks
calculate overall equipment effectiveness (OEE)
determine whether best practices are followed for a pilot test, given a scenario
sequence the steps in conducting a simulation
select an optimum solution

Overview/Description
As a Lean Six Sigma improvement team moves into the Improve phase of a DMAIC project, it begins to generate a list of solutions to address process problems. Lean offers several techniques to reduce waste and cycle time, as well as improvement tools such as kaizen, theory of constraints, and overall equipment effectiveness (OEE). After appropriate methods and tools are used and solutions developed, the implementation of proposed solutions needs to be tested and verified to ensure that optimal choices are made. This course looks at some of the popular Lean methods and implementation planning in Six Sigma. It examines Lean tools used for reducing waste and cycle time, and the Japanese principles of continuous improvement – kaizen and kaizen blitz. It also looks at some other process improvement methodologies such as theory of constraints and OEE. Finally, the course examines planning for proposed solutions, including conducting pilot tests and simulations. 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.

Measuring and Modeling Relationships between Variables in Six Sigma

Course Number:
oper_41_a01_bs_enus

Expected Duration (hours)
1.5

Lesson Objectives

Measuring and Modeling Relationships between Variables in Six Sigma

calculate and interpret the correlation coefficient r
recognize the characteristics exhibited by a given scatter diagram
recognize key considerations related to correlation analysis
calculate and interpret the equation for the line of least squares in a given scenario
use the p-value method to validate a hypothesis test for a given regression equation
interpret graphs used to perform a residual analysis

Overview/Description
As a Six Sigma team moves into the Analyze stage of the DMAIC process, it looks more closely at the variables and variable interrelationships identified during the Measure stage. As part of the analysis, a scatter diagram of dependent and independent variables is drawn to visualize the form, strength, and direction of their relationships. By determining their correlation coefficient, a linear relationship can be quantified and identified as positive, negative, or neutral. Then, using regression analysis, a model is developed to describe the relationship as a linear equation and then used for predictions and estimations. However, it is essential to analyze the uncertainty in the estimate, to test that the relationship between variables is statistically significant, and that the model is valid. This course discusses two important tools – correlation and regression analysis for measuring and modeling relationships between variables. In terms of correlation, it takes learners through examples of scatter diagrams for two variables, the calculation and interpretation of the correlation coefficient, and the interpretation of its confidence interval. The course also draws learners' attention to some key considerations in correlation analysis, such as correlation and causation. In terms of regression analysis, the course discusses the simple linear regression model, how to create it using sample data, interpret and use it, and conduct a hypothesis test to check that the relationship between the variables is statistically significant. Finally, the course looks into how residual analysis is used to test the validity of the regression model. 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.

Multivariate Tools and Nonparametric Tests in Six Sigma

Course Number:
oper_41_a04_bs_enus

Expected Duration (hours)
1.8

Lesson Objectives

Multivariate Tools and Nonparametric Tests in Six Sigma

interpret factor scores as part of factor analysis (FA)
interpret the results of a discriminant analysis
interpret the results of a multiple analysis of variance (MANOVA)
identify statements that define nonparametric tests
recognize situational factors that call for a nonparametric method and choose the appropriate test, in a given scenario
identify the limitations of nonparametric tests
select the situation that is best suited for a Kruskal-Wallis test
validate a hypothesis by performing a Kruskal-Wallis test
recognize examples of business problems that are suitable for a Mann-Whitney test and identify the assumptions that must hold true
validate a hypothesis by calculating the Mann-Whitney test statistic and interpreting the result
recognize how the test statistic is calculated for a Mann-Whitney test

Overview/Description
In the Analyze phase of the DMAIC methodology, a Six Sigma team begins to analyze the root causes of the problems that it identified in the earlier stages. This analysis may require churning out huge volumes of data of different types. Sometimes this data is of a multivariate nature, meaning that many dependent and independent variables need to be considered simultaneously. As such, Six Sigma teams often use advanced multivariate tools to manage this type of data. Another set of advanced statistical analysis tools used in this phase is nonparametric tests. In conventional hypothesis tests – called parametric tests – a sample statistic is obtained to estimate a population parameter and hence requires a number of assumptions to be made about the underlying population, such as the normality of data. However, a nonparametric test is used when some of these assumptions, such as normality of data, cannot be safely made. This course deals with multivariate and categorical data analysis tools such as factor analysis, discriminant analysis, and multiple analysis of variance (MANOVA). The course also aims to familiarize learners with approaches for analyzing nonparametric data, particularly the use of Kruskal-Wallis and Mann-Whitney tests for validating hypotheses. 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.

Probability and Probability Distributions in Six Sigma

Course Number:
oper_40_a05_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Probability and Probability Distributions in 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

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 SkillSoft's ASQ-aligned Green Belt curriculum.

Process Flow Metrics and Analysis Tools for Six Sigma

Course Number:
oper_40_a01_bs_enus

Expected Duration (hours)
1.8

Lesson Objectives

Process Flow Metrics and Analysis Tools for Six Sigma

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

Overview/Description
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.

Six Sigma Business Case, Project Charter, and Tools

Course Number:
oper_39_a02_bs_enus

Expected Duration (hours)
2.2

Lesson Objectives

Six Sigma Business Case, Project Charter, and Tools

recognize steps in developing a business case for Six Sigma project charter
determine whether a problem statement adequately describes the problem and recommend changes for improvement if needed
identify the best practices for determining project scope
assess project goal statements using the SMART criteria
identify examples of considerations related to the key performance measurement areas in a Six Sigma project
match the steps in a Six Sigma project performance review to their related activities
identify key concepts related to Six Sigma project tracking tools
recognize how to organize a work breakdown structure
identify the purposes of the work breakdown structure
assign roles and responsibilities using the RACI model
select analytical tools for team use in a given scenario

Overview/Description
A project charter is the most important document used to initiate and manage a Six Sigma project. It is treated as an informal contract between an organization and the Six Sigma team. The project charter articulates the business case for Six Sigma projects, specific problem that the Six Sigma team is going to work on, and the project's scope, goals, and objectives in very clear, specific, and measurable terms. As part of the process of developing a project charter, some performance measures such as cost, revenue, and schedule are identified and developed. Once the project is kicked off, the project charter is reviewed periodically by stakeholders in relation to a project's actual progress. A number of project management and analytical tools, such as Gantt charts, tollgate reviews, work breakdown structures, RACI model, affinity diagrams, tree diagrams, and prioritization matrices are used to measure and track the project's progress on a continuous basis. This course deals with the key issues in developing project charters and tracking a Six Sigma project. It takes you through some of the key elements of a Six Sigma project charter, including the business case and problem statement, as well as the project's scope, goals, and objectives. It also explains project performance measures and how to review the performance of a Six Sigma project using these measures. In addition, the course introduces common tools for tracking a project's progress and deliverables. 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.

Six Sigma Measurement Systems and Metrology

Course Number:
oper_40_a03_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Six Sigma Measurement Systems and Metrology

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

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.

Six Sigma Project Selection, Roles, and Responsibilities

Course Number:
oper_36_a02_bs_enus

Expected Duration (hours)
1.8

Lesson Objectives

Six Sigma Project Selection, Roles, and Responsibilities

distinguish between "evolutionary" and "revolutionary" improvement methodologies
identify common reasons for deciding not to implement Six Sigma when analyzing an organization from a high level
match each stage in a Six Sigma readiness assessment with the types of questions that would be asked
recognize the sources and characteristics of potential Six Sigma projects
determine whether an organization has correctly carried out the Six Sigma project selection process
recognize conditions under which Lean kaizen events would be advantageous for an organization
choose a Lean kaizen event project based on information gathered in the project selection process
sequence examples of the steps for selecting a Lean kaizen event
recognize how alternative improvement methodologies are used
recognize how the balanced scorecard approach can be used in aligning projects with organizational goals
recognize project metrics that align with organizational goals as represented by the balanced scorecard
match characteristics of successful project metrics to examples
recognize Six Sigma stakeholders from their roles and relationships
recognize functional characteristics of the Black Belt role
match Black Belt roles with examples of Black Belt performance
recognize key qualities and qualifications in a Black Belt candidate

Overview/Description
Deployment of Six Sigma, Lean, or another continuous improvement methodology demands major investments of time, effort, and money on behalf of an organization. Organizations need to exercise due diligence to determine if Six Sigma or Lean is the appropriate methodology to employ, or perhaps a less demanding quality and process improvement approach is better suited to meet their needs. Having decided on the methodology, improvement teams need to determine screening criteria for the selection of most appropriate improvement projects. Success of these projects largely depends upon the contribution of a variety of Six Sigma stakeholders. As a key Six Sigma stakeholder, Black Belts often lead improvement teams and their skills and qualifications are critical to teams' ability to deliver the expected results. This course deals with the key considerations around the selection of Six Sigma, Lean, and continuous improvement projects. It also explores roles and responsibilities of key stakeholders and qualifications needed for Black Belts for leading Six Sigma teams. 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.

Six Sigma Strategic Planning and Deployment

Course Number:
oper_36_a03_bs_enus

Expected Duration (hours)
2.5

Lesson Objectives

Six Sigma Strategic Planning and Deployment

identify the goals of strategic planning in Lean Six Sigma
identify how Hoshin Kanri is applied in Six Sigma strategic planning
sequence the steps using portfolio analysis to prioritize a potential Six Sigma project
recognize the strategic goal of portfolio architecting
identify examples of the kinds of questions asked during a SWOT analysis
recognize the techniques associated with each stage of conducting a feasibility study
recognize examples of each area of a PEST analysis
recognize the importance of business continuity and contingency planning in strategic planning
match Six Sigma leadership levels with examples of their roles
recognize the performance of key enterprise leadership responsibilities in a given scenario
distinguish between enterprise leadership roles and Six Sigma team leadership roles
recognize accurately classified organizational roadblocks in a Six Sigma initiative
identify examples of tactical error organizational roadblocks
classify examples of organizational changes brought about by Six Sigma
identify effective approaches for overcoming resistance in a given scenario
match types of resistance to examples of people exhibiting them
identify the strategies that should be used to continue managing a change initiative in a given scenario

Overview/Description
Strategic planning of Six Sigma projects and Lean initiatives plays a critical role in their success in an organization deploying them. A number of strategic analysis and planning tools, such as Hoshin Kanri, feasibility studies, SWOT and PEST analysis, can be used in support of strategic deployment of improvement projects and to enhance their value and effectiveness. An organization's culture and its inherent structure, lack of resources, and top leadership support sometimes create organizational roadblocks that may result in deployment failures. Six Sigma Black Belts should be able to identify these roadblocks and deal with them effectively. Six Sigma deployment is a revolutionary strategy and may result in significant organizational changes. Black Belts need to proactively anticipate human responses, overcome them, and lead the organizational change. The course discusses the importance of strategic planning and deployment of Six Sigma projects and Lean initiatives and some of the key tools used for this purpose. This course also explores organizational roadblocks and ways to manage them effectively. In addition, the course deals with changes caused by Six Sigma deployment, resistance to it, and strategies Black Belts can apply to manage change. 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.

Six Sigma Team Dynamics and Training

Course Number:
oper_38_a03_bs_enus

Expected Duration (hours)
2.1

Lesson Objectives

Six Sigma Team Dynamics and Training

identify how teams leaders should handle groupthink or risky-shift forms of maladaptive behavior
identify key functions of team leads in managing team behavior
recognize issues and conditions that are likely to spark conflict
choose the best conflict-resolution approach and recognize the steps for resolving the conflict, in a given scenario
suggest techniques for improved management of meetings in a given scenario
recognize key elements of team meetings
match decision-making tools to team situations in which they should be used
identify the Lean Six Sigma Black Belt's training responsibilities
recognize steps involved in implementing an effective training curriculum
identify the basic components of a training plan
identify the essential requirements of Six Sigma training
distinguish between modes of training
describe key learning theories
recognize the characteristics of Six Sigma certification
recognize techniques to evaluate training
recognize why it's important to get feedback on training effectiveness

Overview/Description
Black Belts have the challenging task of managing the full spectrum of team dynamics on Six Sigma improvement projects. Besides resolving conflicts, Black Belts require the skills to manage and optimize group behavior. They must also be familiar with tools and techniques for making team meetings and decision-making process more effective. Black Belts are often called upon to assess training needs and plan its delivery to ensure team members have all necessary skills and knowledge required for the success of improvement projects. This course explores several team dynamics and management techniques for Black Belts. It also deals with training as a tool for improving the performance of Six Sigma team members and strengthening their ability to realize project goals. 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.

Six Sigma Team Dynamics, Roles, and Success Factors

Course Number:
oper_38_a01_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Six Sigma Team Dynamics, Roles, and Success Factors

determine the best team model for a given scenario
match team types with statements describing their best applications
recognize the team types that work best for different constraints
recognize Belbin roles played by team members in a given scenario
associate the nine Belbin team roles with their strengths and allowable weaknesses
recognize good recommendations for team selection
recognize which critical success factors need improvement in a team scenario
match critical success factors with descriptions of how they are fulfilled in a team
associate Six Sigma team members with the training they should receive in preparation for launching a team project
identify activities that should be an established part of all team meetings
identify appropriate suggestions for dealing with symptoms of team challenges in a given scenario
associate examples of team problems with the virtual team challenges they symptomize

Overview/Description
Forming an effective Six Sigma team for driving improvement projects throughout an organization is essential to Six Sigma success. Teams are vital to Six Sigma and Lean projects that have goals of improving an organization's existing quality, enhancing bottom-line performance, and reducing costs. The methods used to form and develop a Six Sigma team will have a dramatic effect on the team's overall performance. Black Belts need to proactively contribute to the effectiveness of Six Sigma teams to promote positive organizational change. This course explores the variety of team types, roles, and composition, revealing strategies for selecting strong Six Sigma teams whose members bring diverse talents, knowledge, and aptitudes to the team. The course also examines factors critical to team success including management support, clarity of goals, and following ground rules. Acknowledging that today's organizations increasingly rely on virtual teams that cross geographic and cultural barriers, the course equips you with strategies for meeting the unique challenges of virtual teams. 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.

Six Sigma Team Facilitation and Leadership

Course Number:
oper_38_a02_bs_enus

Expected Duration (hours)
2.1

Lesson Objectives

Six Sigma Team Facilitation and Leadership

recognize which motivation theory is guiding a team leader's assumptions in a given scenario
distinguish between modern motivation theories
recognize how to overcome factors that demotivate project team members
recognize examples of theory-based motivational techniques that are applied in the organization
recognize examples of how motivational techniques are applied to empower employees
match situational leadership styles to examples of when they should be used
distinguish between the basic leadership approaches
match team members' feelings at each stage with stage-appropriate facilitation approaches
recognize examples of good communication practices for team facilitation
sort communications information into sections of a communications plan
identify the scope of key types of information in a communication plan
choose communication tools that will meet team leaders' objectives in a given scenario
identify the characteristics of A3 reports

Overview/Description
Six Sigma Black Belts must possess specific qualities to succeed throughout the deployment cycle. Some of these qualities include effective leadership, motivation, team building, and communication. As team leaders, Six Sigma Black Belts need to know how to facilitate teams and apply motivational techniques to achieve assigned goals. Black Belt leaders adopt appropriate leadership approaches to help develop, strengthen, and integrate all elements necessary for the effective team facilitation. They need to recognize the stages of team development and choose targeted approaches for managing performance at every stage. Finally, they need to develop a communication plan that outlines the why, what, who, where, and how of team communication. This course offers strategies for effective team facilitation, which involves leadership, motivation, team building and communication. 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.

Statistical Process Control (SPC) and Control Charts in Six Sigma

Course Number:
oper_43_a01_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Statistical Process Control (SPC) and Control Charts in Six Sigma

recognize the objectives of statistical process control (SPC)
recognize key concepts related to the use of SPC
recognize examples of variables that are good candidates for statistical process control
select the best option for rational subgrouping, in a given scenario
recognize the description of the rational subgrouping principle
identify considerations for determining appropriate subgroup size
use the appropriate control chart to determine upper and lower limits for a given process
recognize suitable applications for moving average charts
calculate moving averages
identify key concepts related to the use of short-run SPC charts
determine appropriate corrective actions for the trend exhibited in a given control chart

Overview/Description
Ensuring a process is in control is critical to any Six Sigma project, but how do you determine with certainty if a process is on track or requires improvement? Where do you find the 'proof' or solid facts that a process is out of control and requires intervention? By applying statistical process control (SPC) methods, a Six Sigma team can identify and control variation in a process. This course covers the basic concepts in statistical process control methodology, including the selection of variables and rational subgrouping. One of the most important tools used in SPC methodology is the control chart, and this course explores how to select the right control chart for the variables being measured, and how to interpret specific patterns they reveal. 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.

Sustaining Six Sigma Improvements

Course Number:
oper_43_a03_bs_enus

Expected Duration (hours)
2.3

Lesson Objectives

Sustaining Six Sigma Improvements

identify the overarching benefit of conducting a postmortem analysis in a Six Sigma project
determine what a Black Belt should have done differently in scheduling and selecting participants for a postmortem analysis, in a given scenario
recognize the key objectives of conducting and presenting the results of a postmortem
match examples of planning considerations to the aspect of training they help you to plan
identify elements that enhance communication in a training session
recognize examples of recommended presentation practices in a given training scenario
identify good practices associated with evaluating and following up on training
identify the characteristics of effective documentation
rank four types of documentation according to the documentation hierarchy
distinguish between types of documentation by recognizing examples of information suitable for each
recognize the best strategy for ongoing evaluation
recognize how control charts, controls plans, and lagging and leading indicators can be used in monitoring and evaluation

Overview/Description
As a Six Sigma project winds down, a number of activities are undertaken to hold the improvements and gains achieved from the project. For instance, lessons learned from all phases of a project are documented. Efforts are made to replicate and apply improvements to other parts of the organization. Training for process owners and staff is developed and implemented to ensure consistent execution of revised methods and to maintain them. Improved processes are regularly evaluated to identify additional improvement opportunities. And finally, leading and lagging indicators are monitored to ensure processes consistently deliver expected value to the organization. This course will explore the importance of utilizing lessons learned and the role of training and documentation in sustaining support for Six Sigma improvements. Specifically, it will explore the use of a postmortem analysis, guidelines for developing training plans, and recommendations for delivering the training. Project documentation along with different measurement tools used for ongoing evaluation of the improved process will be discussed too. 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.

Tests for Variances and Proportions, ANOVA, and Goodness-of-fit in Six Sigma

Course Number:
oper_41_a03_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Tests for Variances and Proportions, ANOVA, and Goodness-of-fit in Six Sigma

perform key steps in a hypothesis test for proportions, and interpret the results
perform key steps in a one-sample hypothesis test for variance, and interpret the results
distinguish between characteristics of one-sample tests for variance and two-sample tests for variance
perform key steps in a one-way ANOVA and interpret the results
interpret results in a two-way ANOVA
recognize examples of business problems that warrant a two-way ANOVA
determine whether a goodness-of-fit test was calculated and interpreted correctly
identify business problems or organizational questions that are suitable for a goodness-of-fit test
use a contingency table to test the relationship between two variables
identify statements that describe the purpose of contingency tables

Overview/Description
As a Six Sigma team moves into the Analyze phase of a project, team members begin analyzing the information and data collected in the earlier phases. During the Analyze phase, Six Sigma teams identify possible sources of variation, underlying root causes, and areas for improvement. It is here where assumptions or hypotheses about a process, product, or service are made and validated using tests based on sample data. This course aims to familiarize you with some of the advanced hypothesis tests used in Six Sigma. You are taken through the key steps in testing hypotheses for proportions, variances, and analysis of variance (ANOVA), and their underlying assumptions, with the help of examples and case studies. You will also learn how to use goodness-of-fit test statistics and contingency tables for validating hypotheses about various aspects of the variables being analyzed. 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.

Understanding DOE and Planning Experiments in Six Sigma

Course Number:
oper_42_a01_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Understanding DOE and Planning Experiments in Six Sigma

identify the purposes of design of experiments (DOE)
match key design of experiments (DOE) concepts with examples
recognize a balanced experiment from its design table
recognize factors that should be blocked and randomized in a given scenario
distinguish between reasons for using repetition and replication
calculate the interaction effect between factors in a given scenario and determine its significance
recognize the role of power in an experiment
match common experimental resolution levels to descriptions
classify the goal of an experiment, in a given scenario
identify recommendations for choosing responses, factors, and levels in an experiment
identify considerations related to measurement methods in DOE
choose an experimental design in a given scenario
recognize the differences between full and fractional factorial designs

Overview/Description
Six Sigma teams concluding the Analyze phase of DMAIC with a well-understood problem, strive to generate a powerful solution in the Improve phase. Design of experiments (DOE) is a controlled approach to experimentation that enables teams to systematically change the level of one or more input factors and observe the effects on the targeted response. If teams exercise care in choosing the right design – including suitable factors, levels, and responses – their experiments can reveal the precise combination of factors that will optimize the response. Later, that combination will be tested, validated, and ultimately implemented to effect the desired process improvement. This course surveys the concepts that are fundamental to design of experiments methodology. This course also explores the basic elements of experiments, principles of good experimental design, and strategies for designing experiments with desired power and resolution, and resource and time constraints. The focus is on the planning stage of DOE, when teams set experimental objectives, choose the best experimental design, and prepare to run the experiments. In describing these activities, the course explores the questions teams should consider at each stage of planning. 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.

Using Basic Statistics and Graphical Methods in Six Sigma

Course Number:
oper_40_a04_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Using Basic Statistics and Graphical Methods in 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
recognize the significance of central limit theorem in the application of hypothesis tests
match tools for drawing valid statistical conclusions to descriptions of their use

Overview/Description
Organizations must ensure that their products and services are extremely consistent to desired specifications, as variations can lead to rejected orders, reworks, and eventually, customer dissatisfaction and financial losses. Statistics can provide Black Belts with the tools to summarize and assess collected data in a meaningful way for identifying sources of variation and controlling them. 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, test the statistical validity of their inferences, and optimize and control processes. This course provides Black Belts with basic statistical tools for describing, presenting, and analyzing 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.

Using Business and Financial Measures in Six Sigma

Course Number:
oper_37_a02_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Using Business and Financial Measures in Six Sigma

identify the important attributes of key performance indicators
recognize steps carried out during the three phases of a Six Sigma effort
recognize how leading and lagging indicators are connected to organizational goals and strategies
identify the importance of understanding the financial impact of customer loyalty
calculate revenue growth, market share, and margin from a given dataset
distinguish between types of project costs and benefits
sequence the steps in cost-benefit analysis
calculate ROI in a given Six Sigma scenario
use the net present value (NPV) calculation to decide whether to implement a potential Six Sigma project
match components of the present value formula to descriptions

Overview/Description
Six Sigma improvement begins with assessing the current performance of an organization's processes and products, and comparing it with the desired performance. An important part of this assessment is choosing a set of measures that will provide a comprehensive picture of how the company is achieving its goals of customer satisfaction, organizational learning and improvement, internal process performance, and bottom-line financial growth. This course examines two categories of key measures: business performance measures and purely financial measures, exploring how these measures reveal the current state of the business and point to gains achievable through Six Sigma. This course explores how business performance measures such as balanced scorecard, key performance indicators (KPIs), and lagging and leading indicators can be used to align Lean Six Sigma initiatives toward organizational goals. It also explores the critical role of customer loyalty in business success. Turning to purely financial measures of success, this course explores how these financial measures are crucial in determining whether the potential returns of Six Sigma projects will outweigh the required investment. It provides practice in using the formulas associated with these measures, including market share, cost-benefit analysis, return on investment (ROI), net present value (NPV), and hard and soft costs and benefits of Six Sigma projects. 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.

Using Lean Control Tools and Maintaining Controls in Six Sigma

Course Number:
oper_43_a02_bs_enus

Expected Duration (hours)
2.0

Lesson Objectives

Using Lean Control Tools and Maintaining Controls in Six Sigma

recognize statements that reflect the goals and features of total productive maintenance (TPM)
sequence the steps recommended for implementing total productive maintenance (TPM)
sequence descriptions of the stages of small group development
recognize the basic goal of a sample element from visual controls
recognize the advantages of using basic visual controls rather than sophisticated IT tools
recognize how various factors influence the decision to improve a measurement system in a given scenario
recognize why it is necessary to perform a measurement system re-analysis after a successful process improvement initiative
recognize the effect of reduced process variation on measurement system performance metrics
identify characteristics of a control plan
match control plan improvement goals with tasks carried out at each stage
recognize examples of information typically included in a control plan
identify actions involved in transferring responsibility from the Six Sigma team to the process owner

Overview/Description
In the final stages of the Six Sigma DMAIC methodology, teams need to control the improved processes in order to sustain improvement gains. Process control includes applying tools to continuously monitor and maintain each improved process, and to prevent it from reverting to its previous state. Apart from the statistical process control, there are a number of other Six Sigma and Lean tools to help to this end. This course introduces basic control tools commonly used in Lean Six Sigma projects. Specifically, it explores how total productive maintenance (TPM) promotes shared responsibility for maintaining process gains, and how the visual controls provide at-a-glance information about process performance. In addition, this course highlights the need to re-analyze the measurement system after improvement solutions are implemented, and provides guidelines for drawing conclusions from this. It tours the key elements of a vital tool for maintaining controls – the control plan – and explores the steps for developing an effective plan. 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.