Lean Six Sigma Black Belt

Transcription

1 Lean Six Sigma Black Belt The core role of the Black Belt is to lead significant LSS projects and their remit often includes coaching Green Belts and supporting Project Champions. This training programme has been configured to provide the expertise to achieve these objectives but additionally will equip delegates to become process experts and internal consultants. The modules are: 1. Green Belt (4 days open/6 days in-house) 2. Process Measurement and Control (3 days) 3. Data Analysis Techniques (3 days) 4. Managing Change (3 days) 5. Advanced Black Belt Tools (3 days) Optional, but highly recommended: Lean Six Sigma for Innovation and Design (3 days) The modules can be taken in rapid succession in one training wave or spread out over a longer time period. If you have already completed some of the modules, you can upgrade to Black Belt from your current position. Lean Six Sigma Green Belt Introduction: Key principles and foundations of Lean and Six Sigma Waste and Flow Variation and Sigma values Roles and Responsibilities The systematic approach (DMAIC) Project Selection Intro to Change Management The programme then follows the DMAIC (Define, Measure, Analyse, Improve, Control) phases, we cover 8 steps within this framework, covering relevant tools and techniques under each of these headings as follows: Define Phase: Step 1 - Select the Problem

2 Improvement Charter SIPOC Focus on the Customer Voice of the customer and business Critical To Quality requirements In frame / out of frame Stakeholder analysis Elevator speech Measure Phase: Step 2 Understand the Current Situation Process Stapling Value added flow analysis Process mapping: Deployment Flow Charts and Value Stream Mapping Moments of Truth Data collection VOC to CTQ Matrix CTQ to Output Measures matrix Operational Definitions Measurement Plans Continuous and Attribute data Introduction to Gauge R and R Data collection methods Y to X (output to inputs) matrix Output, Input and In-process measures Introduction to Sampling methods Variation Statistical Process Control Charts Special & Common Cause Variation Process Capability Process Sigma Analyse Phase: Step 3 - Identify & Check the Possible Causes Fishbone/Ishikawa diagrams Negative Brainstorming Interrelationship Diagrams Developing measurements of possible causes Process Flow and Waste Introduction to Data Analysis Techniques Pareto Charts Basic Graphs Scatter diagrams Hypothesis Tests Regression Analysis

3 Introduction to Design of Experiments Logical Cause testing Financial & Tollgate reviews Introduction to Multi-Generational Plans Improve Phase: Step 4 Generate Possible Solutions Lean Solutions SMED Batch size reduction Pull processing Product Families, 3Rs and Cells Introduction to Theory of Constraints Process Levelling and Sequencing 5S Creative thinking Assumption Busting Catalyst Ideas Box Step 5 Select the Solution N/3 and Paired Comparisons Prioritisation Matrices Force Field Analysis XY grids Step 6 Plan & Test the Solution Process Pilot FMEA Poke Yoke Financial Review - Cost benefit analysis Control Phase: Step 7 Implement & Standardise the Solution Process management Visual Management Control Plans Standardisation Documentation Response Plan Control Charts Step 8 Assess Achievements & Lessons Storyboards Final Review / Post Project Review Recognition

4 Process Measurement and Control Day 1: Introduction to Basic Statistics and Graphical Analysis using Minitab This session introduces Minitab and builds up your knowledge of the software (no previous experience required) using a case study. Tools include - o Frequency Plots o Scatter Plots o Pareto Charts o Time Series Plots Day 2: Process Sampling with continuous and discrete (attribute and count) data Sample Size calculations for continuous and discrete data Measurement Systems Analysis o Gauge R&R Study o Attribute Agreement Analysis Recap of Understanding Variation and SPC, Tests for Special Causes Individuals (X-moving Range) SPC Charts Control Charts in Practice Day 3: Statistical Thinking o The Normal Distribution o Normality Testing o Data Transformation and when to use it o Introduction to p-values o Central Limit Theorem Six Sigma Measures and Process Capability o DPU/DPMO and determining number of opportunities o Sigma Calculations o Capability Indices (Cp/CpK, Z) o Six Sigma Shift

5 Data Analysis Techniques Day 1: Hypothesis Testing o T tests for comparing 2 groups o Type I and Type II Errors o Power and Sample Size o ANOVA and Test for Equal Variances for comparing multiple groups o Proportions and Chi Square tests for 2 or more groups discrete data Day 2: Regression Analysis o Linear Regression o Multiple Linear Regression and Model Building o Curvilinear regression o Introduction to Logistic Regression Day 3: Control Charts o Charts for Attribute Data (p, np, c, u, Laney) o Charts for sub-grouped data (Xbar-R, Xbar-S, X-MR-R/S) Introduction to Design of Experiments Factorial Experiments Case Study

6 Managing Change Day 1 Importance of Managing Change in Lean Six Sigma Projects Change Process Overview Developing the competencies of a successful Change Agent - effective facilitation Impact of culture, history and resistance on change Day 2 Day 3 Developing the competencies of a successful Change Agent - visioning, management of self and others, persuasion and influencing skills The role of Stakeholders in change Managing the States of Change current, transition and future Developing the competencies of a successful Change Agent building and managing effective project teams, managing conflict Planning for Change - communications, training and development, reward & recognition Change programme and progress monitoring

7 Advanced Black Belt Tools Day 1: Advanced ANOVA General Linear Model - ANOVA with 2 or more factors Main Effects and Interactions, co-variates Further t-test topics Transforming data for robustness 1 sided tests and 1 sample tests Non-Parametric Hypothesis Tests Advanced Regression Regression model with continuous and discrete Xs Transforming data to improve the model Using the model Introduction to stepwise and best subsets regression Day 2: Logistic Regression Single and Multiple Binary Logistic Regression Ordinal Logistic Regression Design of Experiments Full Factorial and Fractional Factorial Experiments Planning and Designing the experiment - blocking, randomising and replication, practical considerations Analysing the Experiment plots, residuals analysis, reducing the model Using the final model response optimiser, predictions Day 3: Introduction to Process Simulation software Time-Weighted Control Charts Time Series and Seasonality The broader role of the Black Belt Lean Six Sigma deployment, roles, project management and review, financials, Black Belt competencies Revision Case Study Covering the DMAIC phases from Define through to Control

8 Optional: Lean Six Sigma for Innovation and Design Create the future for your organisation As a Lean Six Sigma practitioner, are you finding that improving your existing products, processes and services is not sufficient to meet your goals? Are you frustrated with unsuccessful attempts to innovate brand new ways of doing things? Do you want to design new products or services quickly and right first time? Do you sometimes wish you could start with a blank sheet of paper and develop a new concept that excited the customer, was lean, flexible, responsive, and was defect free from day 1? This course builds on your existing knowledge of improvement and DMAIC methodology and provides an additional and complementary range of tools and techniques to support highly effective and rapid innovation. Objectives Empower yourself to be more effective in creating the future of your organisation When to apply DMADV versus the DMAIC process? Understand the DMADV process and the tools you can apply to your projects Design and tollgate reviews and how these reviews should be conducted and managed The best way to predict the future is to create it. Peter F. Drucker, American Management Guru So Why and How Does Lean Six Sigma Help? The Lean Six Sigma Innovation and Design Journey Provides a design methodology that integrates customer focus and structured tools with a systematic design process.

9 A total design approach that maintains focus on the customer throughout the process Consider use of Tollgates and Design Reviews to assure appropriate governance of innovation development work Describe and practice an extended range of techniques to capture and analyse voice of customer Build "House of Quality" matrices, using Quality Function Deployment, through an extensive and detailed case study Day 1 Day 3 Introductions and Objectives Importance of Innovation and DMAIC vs DMADV? Change Management DEFINE: Segmenting Customers and Multi-Generation Planning MEASURE: VOC techniques and issues Day 2 QFD, creating the House of Quality CTQs and rating needs, Competitive and Benchmark assessment Value Proposition and Design Scorecards ANALYSE: Functional Analysis, Transfer Functions and HOQ2 Concept Development and Testing Design Elements and High Level Design Capability Evaluation, Modelling and Risk DESIGN: Detailed Design and Design of Experiments Design Integration and Pilot planning VERIFY: Piloting and Implementation Control, Process Management, Transition and Closing Supported by lots of exercises and discussion breaks Course Outline The Curriculum is built around a case study and a variety of practical exercises to give participants real hands on experience in application of Lean-Six Sigma thinking and techniques to the challenges faced when innovation and design is required in your organisation.

10 Introduction & Overview The importance of effective and rapid innovation to modern business and barriers to success, An overview of DMADV (the LSSID Framework) and why it makes the critical difference, Quality Function Deployment and the House of Quality, DMADV building on DMAIC, Linking DMADV with new product stage gate processes. Define Project start up, Market and customer segmentation, the change management challenges of DMADV. Measure Voice of the Customer and Introduction to advanced VOC techniques, Quality Function Deployment (House of Quality 1), Benchmarking and setting design goals, Design scorecards, Defining the value proposition. Analyse Function and Functional Analysis, House of Quality 2, Creative Thinking, Concept development, testing and selection, Building the Design Elements, Simulation. Design Detailed Design planning, Lean design tools, Modelling and Introduction to Design of Experiments, assessing Design Capability, Robust Designs, Design Integration, Design Review, Risk Management. Verify Piloting and testing, Control Planning, Implementation and Handover to operations/bau