Lean Sigma manufacturing black belt

INTRODUCTION

The Lean Sigma Manufacturing Black Belt training develops the knowledge and experience to enable Belts to lead and deliver complex, cross-functional change improvement projects.

The programme integrates Lean and Six Sigma to provide an improvement model that concentrates on both reducing cycle time and variability. Lean tools are linked to the robust DMAIC problem-solving approach, and focus on analysing processes, customer demand and product flow – producing fast and visual process improvements. Six Sigma techniques are applied throughout the improvement process to provide robust data analysis, measurement and creative solutions required to reduce variability in product and processes.

This training is designed with a modular format which combines training inputs and practical simulation activities with an ongoing focus on project progress and reviews.

ACCREDITATION

The Smallpeice accreditation process for Lean Sigma Black Belts incorporates completing a project for formal assessment and passing a multi-choice examination.

BQF Accreditation Option: Smallpeice courses are also licensed for BQF (British Quality Foundation) accreditation, enabling candidates to achieve this external accreditation benchmark. The accreditation steps are as described above, with an additional £175 fee to cover the licensed accreditation route (Note: more details on accreditation options will be provided in Block 1).

BLOCK 1

Defining the
Problem

Understanding the
Current State &
Introduction to Statistics

Data Collection Planning &
Measurement
Systems Analysis

Leading
Effective Change

October 11th

October 12th

October 13th

October 14th

Programme Introduction

• Programme Objectives & Certification Routes

• Improvement Project Roles and Responsibilities

Improvement Roadmap

• Introduction to the DMAIC roadmap

• Pre-requisites for successful projects

• Project selection criteria

• Application of the DMAIC toolkit in daily work

The Define Phase

• Writing problem statements

• Setting the project objectives

• Scoping the project – the concept of y =f (x)

• Mapping the high level process – SIPOC mapping tool

• Linking the problem to the voice of the customer

• Defining critical to quality characteristics

• Drawing up the project charter

Understanding the Current State

• Introduction to the Measure Phase

• Detail Process flow charting techniques

• Taking a proactive approach to identifying weaknesses in the current process

• Use of failure mode effects analysis (FMEA) to identify potential opportunities for defects

• Evaluating, reducing & managing risk

Introduction to Statistical Concepts & Minitab

• Introduction to basic statistics and their context in Lean Sigma projects

• Learning to describe data -measures of location, variation and shape

• Communicating statistics to a non-technical audience

• Introduction to Minitab software

Data Collection Planning

• Understanding different types of process variation

• Selecting what to measure

• Key considerations for data collection planning

• Deciding how to collect the data: an introduction to sampling techniques

• Completing a data collection plan

• Writing effective operational definitions for data collection

Measurement Systems Analysis

• Checking the measurement system

• Understanding measurement system variation

• An introduction to Gauge Repeatability and Reproducibility

• Gauge R & R in Minitab

Preparing and Planning for Change

• Understanding the nature of change

• Analysing enablers and barriers to change

• Analysing and managing stakeholders

• Understanding the role of the project sponsors

• Writing an effective communication plan

Leading Process Change

• Barriers & enablers to team effectiveness

• Influencing for change

Facilitating Improvement Activities with Teams

• Identifying activities

• Organising your team & stakeholders

• Facilitation Skills

Contracting for Success

• Review next steps

• Action Planning

BLOCK 2

Analysing the
Value Stream Current State

Optimising the Effectiveness of Critical Equipment Designing a
Lean Future State Barriers to
Creating Flow & Implementing the Future State

October 26th

October 27th

October 28th

October 29th

Understanding the Current Value Stream

• Defining value streams

• Organising by value stream

• Developing the current state Map

• Mapping transactional processes

Analysing the Current State

• Balancing load and capacity

• Finding and analysing bottlenecks

• Cycle time and workload analysis

• Work in process and Lead time analysis

Introduction to Total Productive Maintenance

• Objectives of TPM

• Measuring Overall Equipment Effectiveness

• Autonomous maintenance

• Developing TPM standards

Rapid Changeover

• The concepts of SMED

• Internal vs external work

• Recording the changeover process

• Techniques for converting internal to external

• Changeover simulation activity

• The Concept of Jidoka

Steps for Creating a Future State Map

• Creating a Lean Vision

• Developing a 'must be' list

• Step by Step guide to developing the future state map

• Strategies for Creating Continuous Flow

• Push versus Pull Systems

WIP Control Strategies

• Material Flow Control

• Use of Kanban and CONWIP systems

Developing the Improvement Plan

• Identifying manning and skills requirements

• Developing the improvement plan

Barriers to Developing Flow

• Dealing with fluctuations in customer demand

• Dealing with physical constraints

• Dealing with non repetitive work & creating flow

• Scheduling non repetitive work flows

Creativity Techniques

• The role of creativity in Lean Thinking

• Barriers to creative thinking

• Creative Thinking Tools

• Links to Future State mapping

Facilitating Value Stream Mapping Activity

• Planning mapping activity

• On the day – dealing with facilitators nightmares

• After the event – implementing and sustaining the improvement

BLOCK 3

Analysing Performance & Verifying Root Causes

Optimising the Process

Designing the
Future State & Sustaining
the Improvements

November 1st

November 2nd

November 3rd

November 4th

Taking a Structured Approach to the Analyse Phase

• Introduction to the analyse phase

• 5 Why Approach to problem solving

• Forming and testing hypotheses

• Writing an analysis plan

Visualising the Data

• What shape is your data? – Histograms

• Visualising Descriptive Statistics – Graphical Summary Tool

• Understanding confidence limits

Assessing Process Control

• What is happening over time? – Time Series Plots

• Understanding process control

• Anatomy & use of Control charts

• Applications of SPC Charts

Assessing Process Capability

• Understanding process capability

• Calculating process capability for Continuous and Attribute Data

• Selecting appropriate capability metrics & indices

• Dealing with non-normal data

Verifying the Root Cause

• Link to cause and effect diagram

• Stratifying the data – use of box plots

• Are there any relationships? - Scatter Diagrams

• Where are the biggest opportunities? - Pareto

• Links to project storyboard – hints and tips for summarising and presenting the analysis

• Introduction to significance testing approach

Introduction to Regression Analysis

• Understanding correlation

• Introduction to simple linear regression

• Application in Lean Sigma Projects

Optimisation Processes:
Design Of Experiments

• Introduction to designed experiments (DOE)

• Applications of DOE techniques

• Basic system optimisation

• Screening techniques using DOE

• Optimising a worked example

Improve Phase: Solution Generation & Piloting

• Challenges of the Improve Phase

• Generating alternative solutions

• Evaluating and selecting the best solution

• Piloting & solution introduction

Solution Introduction & Control

• Developing a control plan

• Prevention & detection systems

• Choice of control method

• Out of control action planning

• Handover & transferring benefits

• Planning for continuous improvement

Contracting for Success

• Project reviews, lessons learnt & next steps

BLOCK 4

Define Phase Revisited

Advanced Data Collection Planning

Advanced Statistics

Hypothesis Testing

Regression Analysis

November 22nd

November 23rd

November 24th

November 25th

November 26th

Course Introduction

• Expectations & objectives

• The role of a Black Belt

• Black Belt project selection

Define Phase Revisited

• Scoping complex cross-functional projects

• Use of quality function deployment to understand the voice of the customer

• Benefits calculation & tracking

• Planning for change

• Managing your Black Belt Project

Advanced Data Collection Planning

• Planning data collection for Black Belt projects

• Sampling techniques re-visited

• Gauge R&R for variable and attribute data (bias & linearity)

• Statistical process control for attribute and variable data

• Process capability revisited

Advanced Statistics

• Basic statistics and probability revisited

• Probability distributions

• The central limit theorem

• Dealing with non-normal data

• Planning and communicating data analysis

Hypothesis Testing

• Use of inferential statistics

• Significance testing route map

• Analysis of variable and attribute data

• Power & sample size

• Non-parametric techniques

Multi-Variable Regression

• Introduction to multiple regression

• Analysis of multi-variable data

• Further diagnostic techniques

• Signpost to advanced regression techniques

BLOCK 5

Design of Experiments

Control Phase

December 13th

December 14th

December 15th

December 16th

December 17th

Design of Experiments

• Introduction to designed experiments (DOE)

• Applications of DOE techniques

• Basic system optimisation

• Screening techniques using DOE

• Optimising a worked example

Advanced Design of Experiments

• Review of Full Factorial Designs

• Benefits & limitations of basic DOE techniques

• Introduction to Fractional Factorial

• Screening Designs

• Overview of advanced techniques

Advanced Design of Experiments (Contd)

• Taguchi techniques

• Mixed & multi-level experiments

• Response surface designs
  (CCD, CCF & Box-Behnken designs)

• Introduction to evolutionary experimentation
  (EVOP techniques)

Note: All DOE modules Include SimWare Simulations & Minitab Analysis, as well as practical experimentation examples

Control

• Control phase techniques

• Embedding the change within the business

• Application of long term MSA

• Long-term control

Intro to DFSS

• Introduction to design for six sigma and the DMADV methodology

• Links to DMAIC

Review & Next Steps Planning