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Lean Six Sigma Black Belt Certification Program: In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

Lean Six Sigma Black Belt Certification Program This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

Course Objectives This course aims to provide delegates with a foundation for Excel knowledge and skills. ' Customer Feedback Great course and excellent trainer. Thanks Tracy Preston - Western Power Distribution Very helpful course. Would definitely take another one. Pedro was very patient and made it fun and engaged with us all. Laura Smith - James Grant Very professional and well mannered, fun and pleasant at the same time. I learnt a lot Carolina Foster - CNN 1 year email support service Take a look at the consistent excellent feedback from trainees visiting our site ms-officetraining co uk With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke courses. Tailored in company training courses: You can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Please visit our site (ms-officetraining co uk) to get a feel of the excellent feedback our courses have had and look at other courses you might be interested in. What is Excel? The Excel Interface Ribbon, Tabs and Groups Microsoft Office Backstage view Working with Workbooks Create a blank Workbook Create a Workbook using a Template Opening and saving Files Working with worksheets Worksheet navigation Select one or multiple Worksheets Insert, Move, Copy or Delete a Worksheet Working with Cells, Rows & Columns Cell References How to select cells or ranges Different types of data Move, Copy and Delete cells Pasting Options Find and Replace Working with Rows and Columns Overview of formulas in Excel Autocalculation The parts of an Excel formula Using calculation operators in Excel formulas The difference between Absolute, Relative and Mixed references Using Excel functions; Sum, Average, Max and Min Cell Formatting Font Format options Number Format options Change the Fill and Borders Cell Alignment An Introduction to Charts Create a simple Chart Format your chart Page Layout and Print Page Layout and Page Break View Change the Page Orientation Set Page Margins Headers and Footers in a worksheet Print a worksheet or workbook Who is this course for? Who is this course for? For those who want to explore in more detail formulas and functions, data analysis and data presentation. Requirements Requirements General knowledge of the Windows OS Career path Career path Excel know-how can instantly increase your job prospects as well as your salary. 80 percent of job openings require spreadsheet and word-processing software skills

Lean Six Sigma Green Belt Certification Program This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. Green Belt is not just a role, it is also a competency required for leadership positions at many top companies. This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. With a real-world project focus, the series will teach the fundamental methodology, tools, and techniques of the Define, Measure, Analyze, Improve and Control Process Improvement Methodology. This course is delivered through sixteen 3-hour online sessions. What you Will Learn At the end of this program, you will be able to: Identify strategies for effectively leading high performing process improvement teams Analyze whether projects align with business strategy Apply process improvement methodologies to DMAIC steps, based on real world scenarios Explain ways to appropriately respond to process variation Distinguish among best practice problem solving methodologies Evaluate and effectively communicate data-driven decisions, based on real world scenarios Introduction Lean Six Sigma & quality The vision The methodologies The metric Project Selection Why Projects Random idea generation Targeted idea generation CTQs (Critical to Quality) & projects Project screening criteria Quick improvements Introduction to Define Project Planning Developing the core charter Developing a project charter Facilitation Process Management Business process management Top-down process mapping Voice of the Customer Voice of Customer Stakeholder analysis Communication planning Kicking off the project Define Summary Introduction to Measure Data Collection Fact-based decision making Data sampling Operations definitions Data collection plan Measurement system analysis Graphical Statistics for Continuous Data Meet Six SigmaXL Graphical & statistical tools Data stratification Graphical Statistics for Discrete Data Pareto analysis Dot plots Plotting data over time: Looking for patterns Variation Concepts Variation is reality Special Cause and Common Cause variation Example of standard business reporting Individuals Control Chart Process Capability Genesis of process capability Calculating the metrics of Six Sigma Yield metrics: Measuring process efficiency Cost of Poor Quality The Cost of Poor Quality (COPQ) Cost of Quality categories Calculating the Cost of Poor Quality Measure Summary Introduction to Analyze Process Analysis Introduction to process analysis Value-added analysis Cycle time analysis WIP & pull systems Analyzing bottlenecks and constraints Cause & Effect Analysis Fishbone/Ishikawa diagram 5-Whys analysis Graphical & statistical tools Advanced Analysis Why use hypothesis rests? Hypothesis tests Correlation and regression analysis Analyze Summary Introduction to Improve Solutions Creativity techniques Generating alternative solutions Solution selection techniques Introduction to Design of Experiments Introduction to DOE DOE activity Error Proofing Failure mode & effect analysis Poka-Yoke Project Management Fundamentals Successful teams Project roles Conflict management Standardization Standardization The Visual Workplace 5S Piloting & Verifying Results What is a pilot? Evaluating results Improve Summary Introduction to Control Statistical Process Control Review of Special & Common Cause variation Review of Individual Control Chart P-Chart for discrete proportion data Transition Planning Control plan Project closure Control Summary Summary and Next Steps

Lean Six Sigma Green Belt Certification Program: In-House Training This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. Green Belt is not just a role, it is also a competency required for leadership positions at many top companies. This learning series is designed to enable participants to fulfill the important role of a Lean Six Sigma Green Belt and to incorporate the Lean Six Sigma mindset into their leadership skills. With a real-world project focus, the series will teach the fundamental methodology, tools, and techniques of the Define, Measure, Analyze, Improve and Control Process Improvement Methodology. This course is delivered through sixteen 3-hour online sessions. What you Will Learn At the end of this program, you will be able to: Identify strategies for effectively leading high performing process improvement teams Analyze whether projects align with business strategy Apply process improvement methodologies to DMAIC steps, based on real world scenarios Explain ways to appropriately respond to process variation Distinguish among best practice problem solving methodologies Evaluate and effectively communicate data-driven decisions, based on real world scenarios Introduction Lean Six Sigma & quality The vision The methodologies The metric Project Selection Why Projects Random idea generation Targeted idea generation CTQs (Critical to Quality) & projects Project screening criteria Quick improvements Introduction to Define Project Planning Developing the core charter Developing a project charter Facilitation Process Management Business process management Top-down process mapping Voice of the Customer Voice of Customer Stakeholder analysis Communication planning Kicking off the project Define Summary Introduction to Measure Data Collection Fact-based decision making Data sampling Operations definitions Data collection plan Measurement system analysis Graphical Statistics for Continuous Data Meet Six SigmaXL Graphical & statistical tools Data stratification Graphical Statistics for Discrete Data Pareto analysis Dot plots Plotting data over time: Looking for patterns Variation Concepts Variation is reality Special Cause and Common Cause variation Example of standard business reporting Individuals Control Chart Process Capability Genesis of process capability Calculating the metrics of Six Sigma Yield metrics: Measuring process efficiency Cost of Poor Quality The Cost of Poor Quality (COPQ) Cost of Quality categories Calculating the Cost of Poor Quality Measure Summary Introduction to Analyze Process Analysis Introduction to process analysis Value-added analysis Cycle time analysis WIP & pull systems Analyzing bottlenecks and constraints Cause & Effect Analysis Fishbone/Ishikawa diagram 5-Whys analysis Graphical & statistical tools Advanced Analysis Why use hypothesis rests? Hypothesis tests Correlation and regression analysis Analyze Summary Introduction to Improve Solutions Creativity techniques Generating alternative solutions Solution selection techniques Introduction to Design of Experiments Introduction to DOE DOE activity Error Proofing Failure mode & effect analysis Poka-Yoke Project Management Fundamentals Successful teams Project roles Conflict management Standardization Standardization The Visual Workplace 5S Piloting & Verifying Results What is a pilot? Evaluating results Improve Summary Introduction to Control Statistical Process Control Review of Special & Common Cause variation Review of Individual Control Chart P-Chart for discrete proportion data Transition Planning Control plan Project closure Control Summary Summary and Next Steps

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Complete Visual BASIC training course description A hands-on introduction to programming in Visual BASIC V6. What will you learn Build Visual BASIC applications. Build simple components. Debug Visual BASIC programs. Examine existing code and determine its function. Complete Visual BASIC training course details Who will benefit: Anyone wishing to program in Visual BASIC. Prerequisites: None although experience in another high level language would be useful. Duration 5 days Complete Visual BASIC training course contents Application development using VB VB features. VB editions, VB terminology. Working in the Development Environment. Event-Driven Programming. Creating a program in VB. Project and executable files. VB reference materials. Visual Basic fundamentals Objects. Controlling objects. Introduction to forms. Introduction to controls. Basic controls. Working with code and forms Modules. Working with code. Interacting with the user. Working with code statements. Managing forms. Variables and procedures Introduction to variables and constants. Working with Procedures and Functions. Controlling program execution Common VB functions. Comparison and relational operators. Conditional structures. Looping structures. Debugging Error types. Debugging logic errors in VB. Working with controls Control types. Standard controls. Advanced standard controls. ActiveX controls. Insertable controls. Data access using the ADO data control Overview of ActiveX Data Objects. VB data access features. Relational database concepts. Using the ADO data Control to access data. SQL. Manipulating data. Using the data form wizard. Input validation Field-Level validation. The masked edit control. Form-Level validation. Form events used when validating data. Error trapping Trapping run time errors. Error handling overview. Enabling an error trap. Errors and the calling chain. In line error handling. Enhancing the User Interface Menus. Status bars. Toolbars. Drag and Drop Overview of Drag and Drop. Mouse events Drag and Drop basics. More about controls Using control arrays. Using the controls collection. Working with object variables. Collections. Finishing touches User Interface design principles. Distributing an application. Creating a default project.

ISO 30401 sets requirements and provides guidelines for establishing, implementing, maintaining, reviewing and improving an effective management system for knowledge management in organizations. All the requirements of this standard are applicable to any organization, regardless of its type or size, or the products and services it provides. Knowledge management is the intentional process of defining, structuring, retaining, and sharing an organization’s employees’ knowledge and experience.