Six Sigma Green Belt (6 Days Program)

Introduction

Green Belts provide value within the organization’s Six Sigma initiative in a variety of ways. They serve on Black Belt project teams to help collect and analyze data, develop process maps, assist the Black Belt in certain levels of statistical analysis, and develop experimental designs for a particular project.

These activities serve to both support and accelerate progress in every project—which helps to maximize the organization’s return on its investment, and adds capacity to deliver even greater numbers of breakthrough improvement projects throughout the company

Green Belts can also be assigned specific improvement projects to conduct on their own projects that would not require the statistical rigor demonstrated by the Black Belt. Green Belts are able to conduct these projects within the scope of their normal daily roles.

Objectives

1. Determine critical customer requirements
2. Align internal measurements to critical customer requirements
3. Use the proven tools and techniques of project management to    successfully
4. Resource and complete projects on time
5. Use the proven tools and techniques of Six Sigma to eliminate non    value add process
6. Steps and defect drivers
7. Identifying good returns from projects

Methodology 

Lecture, Discussion & Case Studies

Course Content

1. Defining Problem/ Opportunities and Measuring existing process

2. Six Sigma D-M-A-I-C Breakthrough Strategy

• SIPOC & VOC
• Project Definition and Charter
• Process Mapping
• Normal Distribution – Working with Variation
• Variable & Attribute Data

3. Basic Statistics

• Normality Test

4. Graphical Tools and Analysis:

• Cause and Effect Analysis
• Histograms
• Pareto
• Boxplot
• Scatter Plot
• Dotplot
• Run chart
• Timeseries

5. RTY & Cost of Poor Quality

6. Analyse & Improve Phase

7. Statistical Thinking and Control Charts

• Variable Charts – X-Bar R Chart, IMR Chart, X-Bar S Chart
• Attribute Charts – P Chart, NP Chart, C-Chart, U-Chart.

8. Process Capability – Cpk , Ppk, Z value

9. Planning Data Collection

10. Pareto Analysis & 80/20 Principles

11. Measurement System Analysis (MSA)–Variable & Attribute data

• Gage Repeatability & Reproducibility
• Linearity, Stability , Accuracy & Biasness

12. Statistical Tolerancing

13. Improving existing process.

14. Controlling/monitoring process for stability and root cause identification

 

15. Project Reviews

16. Cause and Effect Analysis

17. FMEA for Six Sigma

18. Estimation and Confidence Intervals

19. Hypothesis Testing and Statistical Significance

• One Sample T-Test
• Two Sample T-test
• Paired T-Test

20. Non Parametric test:

• 1-Sample Wilcoxon
• Mann Whitney Test
• Kruskal Wallis Test
• Friedman Test

21. Analysis of Variance (Anova)

• One Way Anova with many levels
• Two Way Anova with one or many levels

22. Correlation and Simple Linear Regression

23. Determine How Good the Correlation

24. Introduction to Basic DOE

25. Control Phase

26. Error Proofing - POKA YOKE

27. Statistical Process Control

28. Standardized Work and Visual Controls

29. Create the Project Management Plan

Target Audience

Managers, Executives, and Supervisors who wants to:

• Learn this highly valued skill set
• Initiate the Six Sigma methodology to eliminate a current business    problem
• Successfully fulfill the future role of a Six Sigma Black Belt

Introduction to Minitab and Its Statistical Application

Introduction

Manufacturing Organizations use 6-sigma tools to drive continual improvement in Manufacturing and Production Operations. In the present work environment everyone involved in continual improvement should be equipped or armed with the know how in utilizing 6-sigma tools. However, we will focus on key 6-sigma tools. This course will improve the analytic skill effectiveness in problem solving and implementation of the appropriate tools in the continual improvement process using Minitab 15.

Objectives

On completion of the program, participants will be able:

• Understanding Minitab Application
• Understand and apply the Quality Planning tools
• Identify and apply the Quality Control tools
• Identify and utilize Quality Improvement tools
• How to carry out their Process Capability studies
• Understand Basic Statistics and Anova

This will enhance participants capability of problem solving and conducting many quality improvement activities using Minitab.

Methodology

Lecture, Workshop Activities, Discussion & Calculations

Course Content

1. Introduction – Statistical Principles

- Introduction to Minitab

- Basic Statistics

- Types of variables

- The Gaussian Distribution

- Standard Deviation and Standard Error of the Mean

- Exercise/Case Study

2. Descriptive Statistics and Normality Tests

- Frequency distributions (Histogram)

- Describing curves

- Anderson-Darling Test – Test for Normality

- Scatter Plot, correlation & regression

- Logistic Regression

- Pareto

- RTY

- Exercise/Case Study

3. 1-way ANOVA and 2-way Anova

- Key concepts

- Repeated measures 1-way ANOVA

- Ordinary 2-way ANOVA

- Post-hoc tests

- Exercise/Case Study

4. Statistical applications in quality improvements:

- Law of Variation, Common and Special Causes

- Basic principle of Statistical Process Control (SPC)

- What is a control chart?

- Types of control chart

- Process improvement and capability

- Exercise/Case Study/Assignment

5. Hypothesis testing and statistical significance

6. Nonparametric hypothesis Tests

7. Confidence intervals

8. P Values

9. Outliers test

10. Sample size study

11. Exercise/Case Study/Assignment

12. Comparing Two Groups (t-test)

- Key concepts: t tests and related nonparametric tests

- Unpaired t test

- Paired t test Kruskal-Wallis test

- Friedman’s test

- Exercise/Case Study/Assignment

Target Audience

1. QA/QC Managers / Executives / Supervisors / Engineers involved in process improvement preferably graduates in Science or Engineering fields
2. This course involves many calculations and mathematical modeling

Failure Mode and Effect Analysis ( FMEA )

Objectives

At the end of this training programme, participants will be able to, among others:

1. Understand the purpose and benefits of FMEA

2. Understand the FMEA Methodology

3. Create detail Process Mapping

4. Construct Cause & Effect Matrix

5. Create an FMEA by applying the methodology and process steps using a case study

6. Understand the application of FMEA supporting RCA, Prevention and Detection tools

The Model: 

1. Process Selection Process Mapping

2. Cause & Effect Matrix

3. Process FMEA

4. Control Plans

Course Content

1. FMEA Overview

-  History of FMEA

-  What is FMEA?

-  FMEA Model

-  FMEA link to problem prevention

-  Benefits of FMEA

-  Types of FMEA – design, process, equipment

-  FMEA in product life cycle

-  Roles and responsibilities

-  Steps in filling up FMEA template

-  FMEA Process Steps and Flow

2. Prepare FMEA

-  Define Scope

-  Define Objectives and Success Criteria

-  Define Team

-  Review FMEA Process

-  Gather Relevant Information

-  Prepare Preliminary FMEA

-  Example – marking process

3. Develop FMEA

-  Define Unit Process flow Steps

-  List Potential Failure Modes

-  List Potential Failure Effects

4. Assign Severity Rating

-  Severity overview/guideline

-  Relationship of failure/effect/cause

-  Occurrence guideline

-  Detection overview/guideline

-  RPN guideline

5. Act on FMEA Results

-  Prioritize Failure Mode

-  Determine recommended action

-  Assign owner(s) to action

-  Data collection

-  Revise FMEA

6. FMEA deployment & Follow-up

-  RPN guidelines

-  FMEA and criticality

-  When to revise FMEA?

Selecting and Developing an In-House Process FMEA:

1. Form a team, with three to four groups

2. Select appropriate critical process

3. Developing a Process Mapping

4. Developing a Cause and Effect Matrix

5. Developing a Preliminary FMEA

6. Focusing on High RPN Numbers

7. Preparing Final FMEA and Reporting

Understanding and application of supporting RCA, Prevention and Detection tools of FMEA:

1. Ishikawa "Fishbone" Diagram

2. Sentencing Technique

3. The Global 8D Problem Solving Approach

4. Flowcharts & Process Mapping

5. Basic Anova (Basic DOE)

6. Statistical Process Control

7. Poka-Yoke (Mistake-Proofing)

8. Control Plan

9. FMEA Software

10. 5 Whys Techniques

11. Normality Test

12. Histograms

13. Pareto Charts

14. Basic Statistics

15. Cause & Effect Matrix

Target Audience

Managers/QA Engineers/Technicians/supervisors/QA staffs who wish to apply FMEA to proactively manage the operation issues or abnormalities to stay competitive in the market by using this systematic, scientific approach.

Environment Spills Training - Hazardous Toxic Substances

Objective

This course is designed for personnel responsible for managing and dealing with hazardous materials. The aim of the course is to give course members an appreciation of the need for strict control procedures needed to enable them to deal with different types of spill accidents.

Course Content

Module 1

• Understand the steps in dealing with a spillage

Module 2

• Be able to select the correct spill control media and equipment

 

Module 3

• Know the procedure for summoning assistance

Module 4

Environment Spills Response: Overview

• What is a spill under the regulations
• Reportable quantities
• Fines and costs associated with spills
• Spill video "Hazardous Materials - Leaks, Drips and Spill Cleanup"

Module 5

Health Effects Of Exposure To A Chemical Spill:

• Toxicology - how chemicals affect our body
• Routes of Entry
• Chemistry for non-chemists

Module 6

Personal Protective Equipment:

• Protective clothing
• Respirator protection etc.

Module 7

Spill Equipment:

• Various adsorbents available on the market
• Agents for neutralization

Module 8

Spill Procedures & Clean Up

• General spill control procedures
• Dealing with oils and solvents
• Dealing with reactive materials

Module 9

Potential For Spills

• Workshop-group identifies specific materials at their location that could spill and could cause an environmental hazard
• Review & discuss your current spills response procedures

Module 10

How Long Does It Take?

• Theory training takes four hours. Chemical Spill Response Drill And     Review will take three hours

Target Audience

All workers, supervisors, managers, engineers and joint health and safety committee members who are involved with spills procedures

Effective Inventory and Store Management

Introduction

Organization’s materials cost constitutes the major cost for the production of their products. Materials cost consist of Ordering Cost, Receiving Cost, Holding Cost, Reject Cost and  Purchase Cost. We need to balance or optimize this cost. This course is solely focus on Materials Management.

Objectives

On completion of the Program, participants will be able:

• To develop Ordering Parameters / Inventory control techniques
• JIT and Kanban Implementation
• To understand 80/20 principles in life, manufacturing and Store
• To conduct ABC Analysis
• To calculate Average Inventory Estimation
• To calculate Statistical Safety Stock
• To Compute Economic Ordering Quantity (EOQ)
• To Calculate Total Planned Average Inventory
• To improve Inventory Turns
• To conduct Cycle Counting and improve inventory accuracy
• To analyze Demand Variation and Lead Time Impact on Inventories
• To initiate Supplier Stocking Programs
• To drive for Consignment Systems programs
• To generate Materials Aging Report and Dispositions
• To drive for Short Span and Lead Time

Methodology

Lecture, Workshop Activities, Discussion & Calculations

Course Content

1. Improve inventory management using Economic Order and Purchase Quantity techniques
2. A practical, goal-oriented technique for obtaining control of warehouse operations the ABC analysis. How to motivate your employees to be more productive. Assignment of task on  how to decide what to assign to whom and when. Specific ways to evaluate your own productivity
3. Methodologies for layout planning, reduce travel time and distance with the proper use of space to smooth the flow, layout philosophies and capacity factor guidelines. Determine space-building requirements. Pinpoint approaches for layout and space resources.
4. Various techniques for storage location, use of locator system controls, ABC analysis, how to improve inventory control, establish material location and inventory control guidelines.  Evaluate physical inventory and cycle counting programs.

Target Audience

1. Factory Manager / Production Manager / Executive / Supervisor
2. Materials Manager/Executive/Officer
3. Warehouse/Store Manager / Executive/ Supervisor
4. Production & Materials Planner / Manager
5. Purchasing Manager, Executive, Buyer

Effective QCC & 7QC Tools

Introduction

Effective QCC & 7QC tools is a program designed to support manufacturers on how to use various Quality tools on improving your Quality & Operations efficiency

Objective

1. Enable to chart out an appropriate QCC structure and clarify its functions within the organization
2. Provide skills and techniques on the concepts, operation and philosophy of Quality Control Circle / Innovative and Creative Circles
3. Understand the importance of Quality Tools in process control & improvements
4. Interpret graphs and QC Tools in a correct manner
5. To implement the 7 QC Tools effectively to analyze and interpret data for problem solving

Methodology

Lecture, Discussion & Case Studies.

Course Content

1. Brief introduction to QCC, Quality Control and PDCA
2. Introduction to Continuous Improvement and 7 basic QC tools
3. Brainstorming - A creative process : Cause & Effect (Fishbone diagram)
4. Basic understandings of Statistics
5. Conducting a Normality Test (Anderson Darling’s test)
6. The basic understanding of variations and how to control these variations
7. Introduction, application and description on Check Sheet or Check List
8. Introduction, application and description on Boxplot
9. Introduction, application and description on Pareto Diagram
10. Introduction, application and description on Graphs and Control Charts
11. Introduction, application and description on Scatter Diagram and Coorelation
12. Flowchart constructions and applications
13. Construction of Histograms and its application
14. Basic Anova

Excel Tools and Templates for the above applications will be given free during training.

Target Audience

1. Production Manager / Executive /Engineers/ Supervisor
2. Factory Manager / Supervisor / Coordinator
3. Line Manager / Executive/ Supervisor
4. QC or QA Manager / Executive / Officer
5. Factory Workers & Personnel involved in Quality Control

Design of Experiment (DOE)

Introduction

The DOE training seminar begins with the fundamentals of Design of Experiments (DOE) methods and continues with advanced concepts, principles and requirements. Topics include Anova, Full Factorial Designs, Fractional Factorial Designs, robust designs, the Response Surface Methodology(RSM), reliability DOE and Taguchi Design. We will begin with screening design, process characterization and optimization.

Objective

1. Participants learn to solve problems, improve yields, achieve robust processes and build models for prediction with Design of Experiments (DOE)
2. Response Surface Methodology (RSM) and Multiple Regression Analysis
3. The training course presents concepts and Minitab or excel based templates tools that you could use to help your organization:

• The Design experiments that are effective for studying the factors that may affect a product or process
• Analyze experimental results in order to identify the significant factors and evaluate ways to improve and optimize the design
• To determine if interactions between factors are significantly affecting the output of the process

Methodology

Lecture, Discussion & Case Studies

Course Outline

1. Baselining Data collection 

It is considered passive observation. The process is monitored and recorded without intentional changes or tweaking. In Designed Experiments, the independent variable (Response) is observed. Designed experiments are used to:

• Determine which factors (X’s) have the greatest impact on the response (Y)
• Quantify the effects of the factors (X’s) on the response (Y)
• Prove the factors (X’s) you think are important really do effect the process

2. This training includes the full understanding, applications and terms used in lectures and case studies

3. Orthogonality

Since our goal in experimentation is to determine the effect each factor has on the response independent of the effects of other factors, experiments must be designed so as to be horizontally and vertically balanced. An experimental array is vertically balanced if there are an equal number of high and low values in each column. The array is horizontally balanced if for each level within each factor we are testing an equal number of high and low values from each of the other factors. If we have a balanced design in this manner, it is Orthogonal. Standard generated designs are orthogonal. When modifying or fractionating standard designs be alert to assure maintenance of orthogonality.

4. Repetition

Completing a run more than once without resetting the independent variables is called repetition. It is commonly used to minimize the effect of measurement and to analyze factors affecting short term variation in the response

5. Replication

Duplicate experimental runs more than once after resetting the independent variables is called replication. It is commonly used to assure generalization of results over longer term conditions

6. Randomization

Running experimental trials in a random sequence is a common, recommended practice that assures that variables that change over time have an equal opportunity to affect all the runs. When possible, randomizing should be used for designed experimental plans

7. Blocking

A block is a group of “homogeneous units”. It may be a group of units made at “the same time”, such as a block by shift or lot or it may be a group of units made from “the same material” such as raw material lot or manufacturer. When blocking an experiment, you are adding a factor to the design.

8. Factorial Designs

Factorial Designs are primarily used to analyze the effects of two or more factors and their interactions. Base on the level of risk acceptable, experiments may be either full factorial, looking at each factor combination, or fractional factorial looking at a fraction of the factor combinations. Fractional Factorial experiments are an economical way to screen for vital X’s. They only look at a fraction of the factor combinations. Their results may be misleading because of Confounding, the mixing of the effect of one factor with the effect of a second factor or interactions. In planning a fractional factorial experiment, it is important to know the confounding patterns, and confirm that they will not prevent achievement of the goals of the DOE.

9. DOE Analysis:

Analysis of DOE’s includes both graphical and tabular information. It includes Pareto Analysis, Anova, Main Effects, Interactions analysis. It also include Cube Plots, Contour Plots and Optimization Plot,etc.

10. Response Surface Methodology (RSM)

Response Surface analysis is a type of Designed Experiment that allows investigations of non-linear relationships. It is a tool for fine tuning process optimization once the region of optimal process conditions is known. Using the CCD type RS Design, you will be designing an experiment that test each factor at five levels, and an experiment which can be used to augment a factorial experiment that has been completed. The CCD design will include Factorial points, STAR points, and CENTER Points.

11. Taguchi Designs

A Taguchi design, or an orthogonal array, is a method of designing experiments that usually requires only a fraction of the full factorial combinations. An orthogonal array means the design is balanced so that factor levels are weighted equally. Because of this, each factor can be evaluated independently of all the other factors, so the effect of one factor does not influence the estimation of another factor. In robust parameter design, you first choose control factors and their levels and choose an orthogonal array appropriate for these control factors. The control factors comprise the inner array (Signal). At the same time, you determine a set of noise factors, along with an experimental design for this set of factors. The noise factors comprise the outer array (NOISE). The L8 (2**7) Taguchi design (orthogonal array). L8 means 8 runs. 2**7 means 7 factors with 2 levels each. If the full factorial design were used, it would have 2**7 = 128 runs. The L8 (2**7) array requires only 8 runs − a fraction of the full factorial design. This array is orthogonal; factor levels are weighted equally across the entire design.

12. Anova

13. Essential Basic Statistics

14. Test for Normality

15. Understanding of 1-way Anova with many levels

16. Test for equal variance

17. Understanding of 2-Way Anova with one or more levels

Target Audience

• Engineering Manager / Executive / Supervisor/Engineers
• Process Improvement Managers / Process Engineers
• QC/QA Manager / Executive / Engineers
• Personnel involved in Quality Control & Improvement projects

Design of Experiment (RSM)

Introduction

The DOE training seminar begins with the fundamentals of Design of Experiments (DOE) methods and continues with advanced concepts, principles and requirements. Topics include Anova, Full Factorial Designs, Fractional Factorial Designs, robust designs, the Response Surface Methodology(RSM), reliability DOE and Taguchi Design. We will begin with screening design, process characterization and optimization.

Objectives

1. Participants learn to solve problems, improve yields, achieve robust processes and build models for prediction with Design of Experiments (DOE)
2. Response Surface Methodology (RSM) and Multiple Regression Analysis
3. The training course presents concepts of DOE and RSM and Minitab could be use to help your organization:

• RSM is the extension of DOE
• Analyze experimental results in order to identify the significant factors and evaluate ways to improve and optimize the design.

Methodology

Lecture, Discussion & Case Studies

Course Content

Baselining Data Collection

It is considered passive observation. The process is monitored and recorded without intentional changes or tweaking. In Designed Experiments, the independent variable (Response) is observed. Designed experiments are used to:

• Determine which factors (X’s) have the greatest impact on the response (Y)
• Quantify the effects of the factors (X’s) on the response (Y)
• Prove the factors (X’s) you think are important really do effect the process

DOE Analysis

Analysis of DOE’s includes both graphical and tabular information. It includes Pareto Analysis, Anova, Main Effects, Interactions analysis. It also include Cube Plots, Contour Plots and Optimization Plot, etc.

Response Surface Methodology (RSM)

Response Surface analysis is a type of Designed Experiment that allows investigations of non-linear relationships. It is a tool for fine tuning process optimization once the region of optimal process conditions is known. Using the CCD type RS Design, you will be designing an experiment that test each factor at five levels, and an experiment which can be used to augment a factorial experiment that has been completed. The CCD design will include Factorial points, STAR points, and CENTER Points.

Target Audience

1. Engineering Manager / Executive / Supervisor/Engineers
2. Process Improvement Managers / Process Engineers
3. QC/QA Manager / Executive / Engineers
4. Personnel involved in Quality Control & Improvement projects

Effective Data Analysis for Better Decision Making

Objectives

At the end of this training program, participants will be able to, among others:

1. Able to construct and interpret common graphs & summary statistics for single variables, two or more variables
2. Able to apply appropriate numerical and graphical analysis, interpret results, make sound conclusion and prepare the report.

Course Content

1. Introduction to Statistics

• Deterministic Vs. Probabilistic
• How to solve Probabilistic Problem?
• What does statistics mean to you?
• Statistics is ….
• The Role of Statistics
• Statistical Methods
• Descriptive Statistics
• Inferential Statistics

2. Data Types and Sampling

• About Data
• Why We Need Data?
• Data Sources
• Data Types
• Population vs Sample
• The importance of Sampling
• Representative Samples

3. Distributions and Summary Statistics

• Distribution Shapes
• Normal Distribution
• Binomial Distribution
• Summary Statistics
• Continuous Variables
• Discrete Variable

4. Numerical and Graphical Analysis for Single Variable

• Numerical Analysis such as Mean, Median, Mode, Standard Deviation, Variance, Range, Quartiles, Inter Quartile Range (IQR), Coefficient of variation, Normality Test, P-Values and its interpretation
• Graphical analysis
• Continuous data: Histogram, Box-Plot, Normal Quantile Plot
• Discrete data: Bar Graph, Pareto Chart

5. Numerical and Graphical Analysis for Two or More Variable

 

6. Relationship between variables

• Numerical analysis such as Coefficient of Correlation (r)
• Graphical analysis
• Continuous vs continuous: Scatter Plot, Run (Trend) Chart
• Continuous vs discrete: Side-by-side Box-Plot, Variability Chart -  Dot-Plot, Bar Graph, Pareto Chart
• Discrete vs discrete: Mosaic Plot
• Non Parametric Test

7. Principles of Graphing Practices

8. Principles of Graphical Excellence

9. Good Graphing Practices

10. Errors in Presenting Data (Data Cleaning)

6 Sigma - Black Belt (15 Days Program)

Introduction

Six Sigma emphasizes quality improvement, but it is more than statistics and tools. The proven Six Sigma methodology is a systematic application that is focused on achieving significant financial results.

When properly deployed on carefully selected business projects, this methodology can lead to a significant reduction and in many cases, elimination of defects and out-of-control processes, which saves valuable corporate resources. That translates into immediate and dramatic financial profitability.

This Six Sigma Black Belt training is specifically designed to provide and prepare participants to implement the principles, practices, and techniques of Six Sigma in order to deliver breakthrough business improvement results time after time. This training is conducted in a series of sessions.

Objective

1. Compute and apply basic statistics

2. Establish and benchmark process capability

3. Apply key statistical tools for hypothesis testing

4. Learn how to construct and use variable and attribute data

5. Identify and leverage dominant variation sources

6. Establish realistic performance tolerances

7. Design and execute multivariable experiments

8. Learn how to plan and implement process control systems

Methodology 

Lecture, Discussion & Case Studies

Course Outline

1. Defining Problem/ Opportunities and measuring existing process

2. Six Sigma D-M-A-I-C Breakthrough Strategy

3. SIPOC & VOC

4. Project Definition and Charter

5. Process Mapping, QFD and FMEA

6. The Power of Data

7. Variable & Attribute Data

8. Process Time Study

9. Cost of Poor Quality

10. Statistical Thinking and Control Charts

11. Process Capability

12. Planning Data Collection

13. Pareto Analysis

14. Measurement System Analysis

15. Improving existing process.

16. Controlling / monitoring process for stability and root cause identifications:

-  Project Reviews

-  Cause and Effect Analysis

-  FMEA for Six Sigma

-  Estimation and Confidence Intervals

-  Hypothesis Testing

-  Analysis of Variance

-  Correlation and Simple Linear Regression

-  Logistic Regression

-  Determine Solutions

-  Introduction to DOE

-  Error Proofing -POKA YOKE

-  Standardized Work and Visual Controls

-  Create the Project Management Plan

17.Improving and Controlling Existing Process

-  Project Reviews

-  Review of Analyze Phase

-  Polynomial and Multiple Regression

-  Determine Solutions

-  Basic Design of Experiments

-  Full Factorial Designs

-  Fractional Factorial Designs

-  Response Surface Methodology

-  Taguchi design

-  Review of Improve Phase

-  Control Plans

-  Standardized Work and Visual Controls

-  Error Proofing - Poke Yoke

-  Control Charts for Variable & Attribute Data

Target Audience

Managers, Executives, Supervisors and Engineers who wants to:

 

-  Learn this highly valued skill set

-  Initiate the Six Sigma methodology to eliminate a current business    problem

-  Successfully fulfill the  role of a Six Sigma Black Belt

Amalan "6S Housekeeping" di Tempat Kerja

Methodologi

Syarahan, Perbincangan, Aktiviti Kumpulan & Pembentangan

Isi Kandungan

1. Pengenalan Kepada 6S                                                                      

-  SEIRI

-  SEITON                            

-  SEISOO

-  SEIKETSU

-  SHITSUKE

-  Shikari-yaro

-  Prinsip 6S

-  Kaedah 6S’s

2. Kepentingan 6S

-  Membaiki Aspek Keselamatan

-  Meningkatkan Moral Pekerja

-  Pemilikan Kawasan Kerja

-  Meningkatkan Produktiviti

-  Membaiki Penjagaan (Maintenance)

-  Membaiki Pulangan Syarikat

3. Keberkesanan Implementasi 6S

-  Kerkesanan Peringkat Permulaan Budaya Kerja Harian

4. Kaedah Menangani Pembaziran

-  Pengeluaran Berlebihan

-  Masa Menunggu

-  Kesilapan Process Kerja

-  Pengangkutan

-  Inventori

-  Kecacatan Produk

-  Sumber-Sumber Terbiar

-  Penyalahgunaan Sumber

5. Produktiviti & 6S

-  Kualiti

-  Kos

-  Kepantasan

-  Pergantungan

-  Fleksibiliti

-  Rekabentuk Kerja

6. Elemen Keberkesanan 6S

                     

-  Organisasi Sistematic

-  Rekabentuk Tempat

-  Pembersihan

-  Kawalan yang Distandardkan

-  Sorting- visual placement

-  Scrubbing clean

-  Standardisation control

Peserta

1. Semua staff yang bertugas di bahagian pengeluaran & pergudangan

2. Pengurusan, Supervisor / Penyelia, Team Leader & Operator

Advanced ICC Tools

Objective

The Advance ICC Tools is the extension of the 7 QC tools to enable the participants improve the problem solving capabilities. It is to enhance the project management skills of the participants. It helps to develop excellent ICC project by applying the new Advance ICC tools.

Methodology 

Lecture, Discussion & Case Studies 

Course Outline

1. Brief introduction to the PDCA cycle

2. Brief introduction to Continuous Improvement and 7 basic QC tools

3. Introduction to Value Stream Mapping

4. Drill down Tree Analysis

5. Introduction to QFD

6. FMEA

7. RTY

8. Advance SPC with Capability Studies

9. Normal Distribution

10. Basic Statistics and Normality Test

11. Case studies using ANOVA analysis, one-way and two-way

12. Extension of Scatter Plot using Regression Analysis

13. Introduction to Lean – 8 types of wastes

Target Audience

-  Production Manager / Executive /Engineers/ Supervisor

-  Line Manager / Executive/ Supervisor

-  QC or QA Manager / Executive / Officer

-  Factory Workers & Personnel involved in Quality Control and ICC