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INTEGRATION OF “SIX SIGMA” INTO MULTIDISCIPLINARY ENGINEERING DESIGN PROJECTSPowerPoint Presentation

INTEGRATION OF “SIX SIGMA” INTO MULTIDISCIPLINARY ENGINEERING DESIGN PROJECTS

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INTEGRATION OF “SIX SIGMA” INTOMULTIDISCIPLINARY ENGINEERING DESIGN PROJECTS

- Mahbub Uddin
- Department of Engineering Science
- Trinity University
- Raj Chowdhury
- School of Technology
- Kent State University

1

- Introduction
- II. Six Sigma Tools and Methodology
- III. Integration of Six Sigma into Multidisciplinary Engineering Design Projects
- IV. Conclusions

2

- “Six Sigma” is a data-driven, fact based, decision making management tool used to improve the profitability of a business enterprise by reducing the waste and defects while improving product, processes and services and increasing the customer satisfaction.
- Six Sigma is widely used in industry to improve the efficiency of product design and development, manufacturing and marketing.

3

Who is using Six-Sigma ?

ABB Burlington Hitachi Lincoln PerkinElmer

Allied Cannon Honda Electric Poloroid

Signal Citigroup Hughes IBM Lockheed Martin

Alcoa Conseco Jaguar Maytag Raytheon

Amazon Dow-Chemical Kodak NCR Johnson Control

Bendix American Express Lear Sony Merrill Lynch

Ford Honeywell Nokia NCR American Standard

Motorola Siemens T.I Bank of America

SUN-Micro Boeing General Electric

Over

200

Companies

are using

Six-Sigma

today.

Few

examples

are:

Improving Productivity …..

Six-Sigma methods have applied to a variety of industries, such as:

Manufacturing, Service, Government, Legal, Financial

Software, Healthcare and Education

4

History of “Six-Sigma”

- Six-Sigma was developed in the 1980’s by Motorola to address the problem of how to be competitive with the Japanese companies.
- Motorola engineers devised a method to track quality and compare performance with customer requirements. An ambitious target of near perfect quality (Six-Sigma) was the goal. Motorola’s goal required ten (10) times improvement in five (5) years.
- Motorola’s ambitious goal paid off…Two (2) years after launching Six-Sigma the company was awarded the “Malcolm Baldridge National Quality Award”.
- Over the next ten (10) years Motorola achieved the following:
* Cumulative savings attributed to six-sigma efforts were $14

billion.

* Five(5)-fold growth rate in sales with profits up to 20% per year. * Stock prices gains compounded to an annual rate of 21.3%

5

Six-Sigma-An Overview

- The Greek letter commonly represents standard deviation. The phrase six sigma, on the other hand, denotes a specific performance level—namely, 3.45 defects per million opportunities. Six Sigma concepts are inspired by three fundamental ideas:
- Cause and Effect Relationship.
- A large variety of continuous physical observations follow the normal probability distribution.
- Common-cause variability is inherent in all systems. Additional variability occurs due to assignable causes that must be identified and eliminated.

6

- The basis of six sigma can be illustrated with the standard normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:
- -
- The standard normal distribution has the probability density function:
- z e - - <
- The standard normal distribution has zero and a unit variance.
- The normalization allows performance comparison of a wide variety of processes and operations with widely varying units and dimensions. The above equation leads to the familiar bell-shaped curve.

7

8 normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

~ Why?? … Is it Worth Pursuing??? normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

Cost of defects

as a percent(%)

of sales

Sigma

Level

Quality

Level

“DPMO”

- Improve Quality
- Improve Reliability
- Reduce Cost
- Reduce errors
- Reduce waste
- Improves Process
- Improves Design
- Improves Customer
- Satisfaction
- Effective
- Management
- Promotes Excellence
- - Creativity
- - Collaboration
- - Communication
- - Dedication

1

2

3

4

5

6

691,000 31% > 40%

309,000 69% 20- 40%

67,000 93.3% 15- 30%

6,200 99.4% 10- 20%

230 99.98% 5- 10%

3.4 99.997% 0- 5%

Improving Productivity …..

Different Sigma Levels, Defects per Million

Opportunities, Quality Level and

Cost of defects/failures

Six-Sigma Tools normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Process Maps
- Cause and Effect Diagrams (Fishbone Diagram)
- Quality Function Deployment (QFD)
- Failure Modes and Effects Analysis (FMEA)
- Statistical Process Control (SPC)
- Analysis of Variance (ANOVA)
- Design of Experiments (DOE)
- Process Capability Analysis (PCA)
- Measurement Systems Analysis
- Multi-Variant Studies
- Control Plans
- Pugh Matrix (Criteria Matrix), etc.

10

Design for Six Sigma Methodology normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- The tools of Six Sigma are most often applied through various performance improvement methodologies such as:
- DMAIC: Design, Measure, Analyze, Improve & Control
- DMADC: Define, Measure, Analyze, Design & Verify
- DMEDI: Define, Measure, Explore, Design & Implement
- Usually, DMAIC is used for improvement of product, process and services, while DMADV and DMEDI are used for design and development of new product, process and services.

11

Define normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

·What is the business case for the project?

·Identify the customer?

·Current state map

·Future state map

·What is the scope of this project?

·Deliverables

·Due Date

Control

·During the project how will I control risk, quality, cost,

schedule, scope, and changes to the plan?

·What types of progress reports should I create?

·How will I assure that the business goals of the projects

were accomplished?

·How will I keep the gains made?

Measure

·What are the key metrics for this business process?

·Are metrics valid and reliable?

·Do we have adequate data on this process?

·How will I measure progress?

·How will I measure project success?

Improve

·What is the work breakdown structure?

·What specific activities are necessary to meet the

project’s goals?

·How will I re-integrate the various sub projects?

Analyze

·Current state analysis

·Is the current state as good as the process can do?

·Who will help make the changes?

·Resource requirements

·What could cause this change effort to fail?

·What major obstacles do I face in completing this

project?

12

Flow Chart of Application of DMAIC Methodology

Flow Chart of Application of DMADV Methodology normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Define
- What is being designed?
- Why is it being designed?
- Uses of QFD or the Analytic Hierarchical Process, to assure that the goals are consistent with customer demands and enterprise strategy

- Verify
- Verify the design’s effectiveness in the real world

- Measure
- Determine critical to stakeholder metrics
- Translate customer requirements into project goals

- Design
- Design the new product, service or process
- Use predictive models, so,I;atopm, prototypes, pilot runs, etc. to validate the design concept’s effectiveness in meeting goals

- Analyze
- Analyze the options available for meeting the goals
- Determine the performance of similar best – in – class designs

13

Implementation of Six Sigma normal distribution. The standard normal variable z is related to the normal random variable x by the relationship: Design Methodology

The Implementation of DMADV methodology will be illustrated through a multidisciplinary engineering design project: Design, built and test the performance of a Shell and Tube Heat Exchanger. Shell and Tube Heat Exchangers are widely used in process industry. The design of a Shell and Tube Heat Exchanger requires the knowledge of thermodynamics, fluid mechanics, heat transfer, process control and instrumentations.

The schematic of a shell-and-tube heat exchanger.

14

Application of a Shell and Tube Heat Exchanger normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

Implementation of Six Sigma normal distribution. The standard normal variable z is related to the normal random variable x by the relationship: Design Methodology

The DFSS (Design for Six Sigma) methodology is based on

four fundamental design objectives:

1. Design should incorporate a balanced prospective of customer needs, current technology, efficient manufacturability, sustainability and commercialization.

2. Design should full-fill the product functional requirement without failure under normal conditions.

3. Design should be optimized based on the effect of uncertainties on its performance.

4. The performance level (Six Sigma or otherwise) of the designed product should be verifiable against all product requirements.

16

Step 1: Define normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Define the goal of the design activity: Design, build, test and improve the performance of a shell and tube heat exchanger.
- Define the customer need: The customer wants to reduce the uncertainties in the operation and performance of the heat exchanger.
- The cost of the heat exchanger should be reduced by 20% of the current market price. (This can be accomplished by efficiently estimating and optimizing the safety factor of the heat exchanger area).
- Make sure that design goals are consistent with customer needs.
- Establish a benchmark for a shell and tube heat exchanger.
- The following Six Sigma tools should be used in this step to assist the project management: QFD (Quality Function Deployment), SIPOC (Suppliers Input Process Outputs and Customers) and Process map.

17

Step 2: Measure normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Measure the CTQ (Critical to Quality) variables and check their compliance with the Upper and Lower Control Limits (UCLs and LCLs).
For Heat Exchanger design project measure the following variables:

- Heat Transfer Area
- Shell-side heat transfer coefficient
- Tube inside heat transfer coefficient
- Shell-side fouling resistance
- Tube inside fouling resistance
- Tube wall thermal conductivity
- Effective tube inside area
- Effective tube outside area
- Tube inside diameter
- Tube outside diameter
- Number of tube passes
- Number of shell passes

18

Step 2: Measure (con’t) normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

For Heat Exchanger design project measure the following variables (con’t):

- Translate customer requirement into project goals: Heat exchanger outlet temperatures should not vary more than +5°C. Performance analysis indicates that by reducing the variation of heat exchanger outlet temperatures to +5°C will improve the heat exchanger operation from Three Sigma to Five Sigma.

19

Step 3: Analyze normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Analyze innovative concepts for design to satisfy customer need.
- Analyze the variability in CTQs.
- Analyze the effect of variability of input parameters to outputs.
For Heat Exchanger design example:

- Perform a stochastic (probabilistic) simulation for heat exchanger design.
- Analyze the variation of CTQs: 1) variation of shell-side heat transfer coefficient, 2) variation of tube in-side heat transfer coefficient, 3) variation of shell-side fouling resistance, 4) variation of tube inside fouling resistance, 5) variation of flow rates of input streams and 6) variation of temperatures of input streams.
- Perform a sensitivity analysis of heat exchanger output temperatures as a function of variability in CTQs. Take measures to reduce the variance in the CTQ variables. The following Six Sigma tools should be used in this step to analyze and reduce the variance in CTQs: ANOVA, DOE, SPC, and PCA.

20

Step 4: Design normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Design new equipment, process, product or services to satisfy customer needs. The objective is to develop a detail design, predict CTQs and revise design until CTQ predictions meet requirements. Also, develop a simulation model and management plan for construction/manufacturing and quality control. Develop a prototype model to validate design effectiveness.
For Heat Exchanger design example:

- Complete the construction of a prototype shell and tube heat exchanger based on design calculation, analysis and simulation.

21

Step 5: Verify normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Verify whether the design satisfies the specifications, CTQ’s and customer needs under real world application. Develop a control plan to implement FEMA and SPC. Develop plans to assure continued performance at a desired sigma level.
For Heat Exchanger design example:

- Verify whether the expected performance of the heat exchanger is achieved under real world application. Continue to increase the sigma level by identifying and eliminating the most significant causes of the variability in heat exchanger design, manufacturing and operation processes.

22

Calculation of Sigma Level normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

On average, the outlet temperatures of the heat exchanger fails to meet its specification 8 hours per month on service. After design improvement using the DMADV methodology, the outlet temperature of the heat exchanger fails to meet its specification 1 hour per month on service. Calculate the Sigma level of the performance of the heat exchanger before and after the design improvement.

DPMO before design improvement =

Using Figure 3, the signal level = 3.7.

DPMO after design improvement =

Using Figure 3, the signal level = 4.5.

DMADV methodology Steps 1 – 5 are repeated to continuously improve the performance of the heat exchanger till it achieves the desired sigma level.

23

Calculation of Sigma Level normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

24

Senior normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

Design

Courses

Integration of Six Sigma into Engineering Design Courses.

25

Conclusions: normal distribution. The standard normal variable z is related to the normal random variable x by the relationship:

- Integrating Six Sigma concepts into multi-disciplinary design projects would provide students a better understanding on how to incorporate realistic design constraints, reduce variability and continuously improve product and process design.
- Integration of Six Sigma into multi-disciplinary design projects would significantly enhance students learning experience in project management and prepare them to provide leadership in implementing Six Sigma in Industry.

26

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