2011 Courses in Leeds

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)

OOAD training course description A workshop course providing thorough practical knowledge of object oriented analysis and design methods. What will you learn Perform Systems Analysis with Object Oriented methods. Identify key classes and objects. Expand and refine OO problem domain models. Design Class hierarchies using inheritance and polymorphism. Design programs with Object Oriented methods. OOAD training course details Who will benefit: System analysts, designers, programmers and project managers. Prerequisites: It is desirable that delegates have experience of programming in C++/Java or some other OOP language. Duration 5 days OOAD training course contents What is OO? Classes, objects, messages, encapsulation, associations, inheritance, polymorphism, reusability. What is Systems Analysis and design? Data flow diagrams, structure diagrams. The OO approach. OOA The problem domain and object modelling. Identifying classes and objects. Generalisation and inheritance. Defining attributes and methods. OOD Refining the OOA results. Designing the User Interface. Designing the algorithms and data structures using objects. Designing the methods. OOP Prototyping. Implementing OOD with OOPs and OOPLs.

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In the modern work environment, managers and leaders must treat all colleagues with respect, including those who are challenging. Understanding the causes of difficult behaviour and being self-aware are crucial for creating a fair and inclusive workplace. This training session equips participants with the knowledge and communication tools to manage and overcome difficult behaviours effectively. This course is available to book for delivery in-house for your organisation and people exclusively, for either a half or full days training workshop. Prices are dependent on size of group ranging from a minimum of 4 to a maximum of 12 delegates. If you have a smaller number of delegates the Puritas 1:1 Leadership Coaching Programme is recommended.

Mastering effective feedback is crucial for empowering others but can be damaging if not delivered thoughtfully. This course equips mangers and leaders with tools to deliver sensitive, factual, and specific feedback for positive outcomes. It also guides them in soliciting feedback, processing it constructively, and fostering a growth mindset for continuous improvement. This course is available to book for delivery in-house for your organisation and people exclusively, for either a half or full days training workshop. Prices are dependent on size of group ranging from a minimum of 4 to a maximum of 12 delegates. If you have a smaller number of delegates the Puritas 1:1 Leadership Coaching Programme is recommended.

Overview Business modelling requires analysts to produce the quantitative models that top management uses to support their decision-making, but top management must also understand the strengths and weaknesses of the models if they are to use them effectively to support their decisions and robustly navigate the strategic negotiation landscape. This is particularly important for PPP projects where long-term commitments are made now based on bankability, value for money and risk allocation. During the course, participants will gain knowledge and skills on the frameworks, tools, and methodologies necessary to build quantitative models for financial decision-making in order to improve the financial viability and bankability of PPP projects. Participants will master modelling frameworks on capital budgeting, risk measurement, regression analysis and Monte Carlo Simulation among others.

Overview Budgeting is the core activity of financial management used to make organisations effective and efficient in service delivery. Organisations deliberately strengthen their acts of budgeting, as part of their financial management, in order to enhance their effectiveness. This course has been designed to educate participants on how to prepare and use budgets in their organisations.

Overview To achieve maximum effectiveness, managers and professionals must continually expand their business knowledge and sharpen their skills. This program is designed to achieve this goal in a time-efficient manner. Integration of Downstream Refining and Petrochemicals to achieve greater efficiencies is yet another critical factor in this business which will be covered in this course

Overview This is particularly important for PPP projects where long-term commitments are made now based on bankability, value for money and risk allocation. During the course, participants will gain knowledge and skills on the frameworks, tools, and methodologies necessary to build quantitative models for financial decision-making in order to improve the financial viability and bankability of PPP projects. Participants will master modelling frameworks on capital budgeting, risk measurement, regression analysis and Monte Carlo Simulation among others.