Six sigma case study example cost reduction

Case Study 4 Six Sigma

Lecture 14

Design for Six Sigma

(DFSS)

What is DFSS?

It is application of Six Sigma techniques to the development process.
It consists of advanced tools used in the Six Sigma projects.
These techniques are generally used in complex problems.
These tools involve a good knowledge of statistics, engineering and experimentation
Example of DFSS projects

Evaluate the interface of new computer design
Develop a new solder paste
Improve the reliability of pre-ship product design
Reduce the settle-out time of stepper motor
Improve electromagnetic interference testing
Improve reliability of complex power supply
Common areas of DFSS projects

Testing for thermal design
Improving reliability issues
Improving software/hardware interface
Tracking change orders
Improving test strategy
Improving product capability
Determining manufacturing process settings
Evolution of DFSS

Quality Function Deployment (QFD)

The Taguchi analysis technique

Design for manufacturability (DFM)

Design for Six Sigma (DFSS)

Quality Function Deployment (QFD)

It focused on upstream quality assurance function to meet tough customer specifications.
It was a planning and control function
It was a medium for establishing and facilitating cause-and-effect relationships, from VOC (voice of the customer) to the detailed process operating conditions.
From VOC to process design

The Taguchi analysis technique

The philosophy of Taguchi analysis technique were as follows:

To design, produce, and deliver products and services that are robust to uncontrollable environmental conditions and insensitive to the variations of the component parts.

To ensure that products and services are produced and delivered around satisfactory targets, with a minimum level of variations.

To engineer considerations of reliability into products and services.

Taguchi steps for optimization of a product, service, or process

Step 1. System design: The first step is to select the most appropriate technique to achieve the objective of the proposed product or process development.

Step 2. Parameter design optimization: The second step is to determine the product values and operating levels of process variables, which are less sensitive to changes in environmental conditions and other noise factors.

Step 3. Tolerance design: The third step is to determine the solutions to tighten the tolerances and reduce variations

Design for Manufacturability (DFM)

It focuses on the following:

Part cost reduction
The use of modular design
The elimination of unnecessary complexity
Standardization and usage of common parts and material
Design for process capability
Examples of DFM

When IBM designed a printer (proprinter), it achieved 79% reduction of parts and 83% reduction in assembly time compared to the previous model.
Dell reduced the assembly time of its optiframe computers by 32% and service time by 44%.
Texas Instrument’s electronic box on the H1 tank reduced assembly cost by 50% with 58% fewer fabricated parts.
The goal of DFM

The goal of DFM is to come up with the design that meets the customer requirements as well as the requirements of the manufacturing operations. This ensures that the design can be produced with respect to available technology & cost.

What is the goal of DFSS?

The goal of DFSS (Design for Six Sigma) is to ensure that the development processes deliver products and services that perform at the highest sigma level possible

What is DFSS philosophy?

Significant economic value can be added by optimizing the following ratio:

The above ratio indicates that the value of the product or service can be increased by either increasing “functional achievement” and “process capability” or by reducing “cost” and “lead time”.

Elements of DFSS philosophy

Design influences up to 70% of production/service cost.
Functional impediments (bureaucracy) to progress should be removed.
Traditional business or engineering skills should be supplemented with intense customer collaboration and a rigorous problem-solving methodology.
Processes and structures will need to be changed, new skills learned, and significant investment and trade-offs made.
There will be a learning curve, so patience may be required in the early stages of DFSS.
New products and services performing at higher sigma levels will outperform those performing at lower level.
High powered tools commonly used in DFSS

Regression analysis

Hypothesis testing

Gage R&R

FMEA (Failure Mode & Effects Analysis)

DOE (Design of Experiments)

What is Regression analysis?

Regression analysis is applied when data collected from an experiment are used to empirically quantify through a mathematical model the relationship that exists between the response variable (y) and influencing variable (x).
It is a powerful tool to quantify the relationship between two or more continuous variables.
It can reveal the strength of the relationship between input and output variables.
It was covered in the prerequisite statistics course for Six Sigma.
What is Hypothesis testing?

It is a technique used to determine when there is a significant difference between two sample populations.
It is also used to determine whether there is a significant difference between a sample population and a target value.
It can be used on both discrete & continuous data to verify hypothesis.
It was covered in the prerequisite statistics course for Six Sigma.
Hypothesis testing using “null hypothesis”

Hypothesis testing is used when decisions need to made about a population. It involves null hypothesis (H0) and an alternative hypothesis (H1). Following are the examples of the hypothesis:

Null (H0): Mean of population equals the criteria.
Alternative (H1): Mean of population differs from the criteria.

Null (H0): Mean response of machine A equals mean response of machine B.
Alternate (H1): Mean response of machine A differs from machine B.

Null (H0): Mean response from the proposed process change equals the mean response from the existing process.
Alternate (H1): Mean response from the proposed process change does not equal the mean response from the existing process.

Null (H0): There is no difference in fuel consumption between regular and premium fuel.
Alternative (H1): Fuel consumption with premium fuel is less.
Error types in Hypothesis testing

We can reject a null hypothesis that is in fact true (type I error).
We can fail to reject a null hypothesis that is false (type II error).
What is Gage R&R study?

Gage R&R (Repeatability & Reproducibility) study is the evaluation of the measuring instruments to determine its capability to give a precise response.
Gage repeatability is the variation in measurements considering one part and one operator.
Gage reproducibility is the variation between various operators measuring one part.
It evaluates the effectiveness of a measurement system.
It can describe the measurement process and thereby clearly identify “good” or “defective” output.
With the measurement process validated, the team can begin to collect data.
Characteristics of gage R&R study

The measurement must be in statistical control, which is referred to as statistical stability. This means that variation in the measurement system is from common causes and not special causes.
Variability of the measurement system must be small compared with both the manufacturing process and specification limit.
Increments of measurement must be small relative to both process variability and specification limits. A common rule of thumb is that the increments should be no greater than one-tenth of the smaller of either process variability or specification limits.
Causes of variations in the gage

Variation due to location of measurement

Variation due to the gage R&R

Steps to conduct a test for location variation

Measure one part in a QC room (reference value).

Instruct one appraiser to measure the same part 10 times, using the gage being evaluated.

Determine the difference between the reference value and observed value.

Express percent of process variation as a ratio of bias to process variation multiplied by 100.

Express percent of tolerance for bias as a ratio of bias to tolerance multiplied by 100.

Adjustments of the measurement system to make it acceptable

Percent of tolerance

Percent of process variation

Number of distinct data categories

Steps to conduct a test for R&R variation

Develop a measurement plan:

a) 4-6 operators

b) 4-6 parts

c) 3-4 measurements per day

d) 3-5 days.

2. Do not adjust the gage during the measurements.

3. Randomize the measurement schedule.

4. Have the operators’ record any factors which may affect the measurements, such as temperature, gage setting, condition of the part etc.

5. Analyze the data.

Gage R&R (ANOVA) for Data Run

What is FMEA (Failure Mode & Effects Analysis)?

FMEA is a method that which can identify and eliminate concerns early in the development of a process or a design and provides a form of risk analysis.
FMEA is an analytical approach directed toward a problem prevention through the prioritization of potential problems and their resolution.
Once the solution is approved, the team proceeds with an evaluation of potential implementation risks, using FMEA.
It is a structured approach to identifying, estimating, prioritizing, and evaluating the risk involved in the proposed solution.
Many a times, the solution is modified after FMEA analysis to prevent potential problems.
Benefits of properly executed FMEA

Improved product functionality and robustness.
Reduced warranty costs
Reduced day-to-day manufacturing problems
Improved safety of products and implementation processes.
Reduced business process problems
Roadmap to create FMEA

Note an input to a process or design

List 2 or 3 ways input/function can go wrong

List at least one effect of failure

For each failure mode, list one or more causes of input going wrong

For each cause list at least one method of preventing or detecting the cause

What is DOE (Design of Experiments)?

DOE is used in studies in which the influence of several factors are studied.
This approach is used if insufficient historical data is available.
DOE can be used to study the effect on output variables by changing the input process variables.
This is an extremely powerful statistical technique to determine the interactions between two variables.
DOE ensures the robust design of products and services.
Examples of DOE application

Reducing the rejection rate of a touch-screen computer from 25% to less than 1%.
Maintaining paper quality at a mill while switching to a different grade of wood.
Reducing the defect rate of the carbon-impregnated foam from 85% to zero.
Reducing error rate on service orders while at the same time improving the response time on service calls.
Improving bearing durability by a factor of five.
Checklist for DOE

List the objective of the experiment

List the assumptions

List factors which might be considered in the experiment

Choose factors and their level to consider in the experiment

Reduce many-level factors to two-level factors

Choose the number of trials

Determine if what factors will be blocked in the experiment

Choose the fractional factorial design

Determine the sample size for the number of trials

Determine the random order of trial sequence

Plan a follow-up experiment strategy

Plan the analysis approach

Following tools are also sometimes used in DFSS

Conjoint analysis
Multi-generation product planning
Design scorecard
Monte Carlo simulation
Non-linear QFD (Quality Function Deployment)
Reliability engineering
Statistical tolerance
Scope of DFSS

Since the DFSS approach not only applies to product and service design but also to process design, projects can be focused on the following areas:

Developing new product or service
Standardizing multiple version of the same process
Redesigning the existing process which is chronically out of control
Optimizing the process capability of an existing process
Critical questions to evaluate for DFSS projects

What will be designed or redesigned?

Who will be its customers?

Why is the project critical to the organization?

How does the project link to the strategic direction of the organization?

What is the market opportunity?

What is the potential risk?

Key points for leaders to succeed in DFSS

Conduct a formal evaluation of how effectively you organization develops and launches new products and services.

Treat the implementation of DFSS in exactly the same way you would a capital investment, focusing on costs versus benefits and return on investment.

Be prepared to invest in process management and measurement as a precursor to DFSS.

Measure your investment in DFSS over a 3-5 year period.

Implementing DFSS is hard work; as such there is a strong incentive not to do it. Reward behavior change, not just results.

Remove unnecessary bureaucracy.

Resource for success: DFSS projects are more complicated than DMAIC projects, so plan accordingly.

The training and couching support required for DFSS is greater than for DMAIC, so plan accordingly.

Key points for leaders to succeed in DFSS

9. Be prepared to radically review your resource requirements for the development projects in order to reflect the change in emphasis from reaction to prevention.

10. Educate all your key stakeholders in the fundamentals of Six Sigma. You cannot introduce DFSS in a vacuum.

11. Do not build your DFSS initiative around specialists; use the cross-functional teams from the start.

12. Include customers and suppliers in the new product development projects.

13. Train your customers and suppliers in the philosophy of DFSS and the use of tools and techniques.

14. Consider using DFSS initially for new process design; the projects are easier and can be completed much faster.

15. Select your DFSS Black Belts even more carefully than your DMAIC Black Belts. Ensure that they are well trained and deploy them over a period of 2-3 years.

Key points for leaders to succeed in DFSS

16. Create opportunities for your DFSS Black Belts to work on specific elements of the DMAIC process. Assign them small starter projects that can be completed in a relatively short time frame to learn the tools instead of hitting them with a huge first-time project that will take years to complete.

17. Resist the temptation to cherry-pick from the DFSS toolbox in order to show-off your traditional development process. DFSS is as much about leadership, management, and culture as it is about tools and techniques.

18. Don’t neglect creativity, one creative idea can transform your future.

19. There is multiple interpretations of what listening to the customer means. Beware of superficial efforts and insist on actual data.

20. Insist on your development teams produce the data to demonstrate at all phases of the DFSS process that they are “on target with minimum variations”.