Design FMEA (DFMEA) Tutorial

By admin | Published: February 11, 2008

In this tutorial, you will learn:

Completing the Design FMEA Form:

  • Identifying the Product Function
  • Identifying Potential Failure Modes
  • Identifying Potential Failure Effects
  • Determining the Severity of the Effect
  • Identifying Potential Cause(s) of the Failure Mode
  • Determining the Probability of Occurrence of the Failure Mode
  • Identifying Design Verifications for the Causes
  • Determining the Probability of Non-Detection of the Failure Mode

Using the Completed DFMEA Form:

  • calculating the Risk Priority Number (RPN)
  • determining Corrective and Preventive Actions
  • prioritizing Actions Based on the RPN

What Is A Design FMEA?

* FMEA is a method for identifying potential or known failure modes and providing corrective and preventive actions.
* The Design FMEA is a disciplined analysis of the part design with the intent to correct or prevent the design-based failure modes prior to the first production run.

The FMEA Process: Read More »

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Understanding defect based six sigma metrics: DPO, DPMO, PPM, DPU, Yield

By admin | Published: February 27, 2007

DPO, DPMO, PPM, DPU Definitions – Six Sigma Defect Metrics

What Is DPO? What Is DPMO?

A unit of product can be defective if it contains one or more defects. A unit of product can have more than one opportunity to have a defect.

  • Determine all the possible opportunities for problems
  • Pare the list down by excluding rare events, grouping similar defect types, and avoiding the trivial
  • Define opportunities consistently between different locations

Proportion Defective (p):

p = Number Of Defective Units / Total Number of Product Units

Yield ( Y1st-pass or Yfinal or RTY)

Y = 1 – p The Yield proportion can converted to a sigma value using the Z tables

Defects Per Unit – DPU, or u in SPC

DPU = Number Of Defects / Total Number Of Product Units The probability of getting ‘r’ defects in a sample having a given dpu rate can be predicted with the Poisson Distribution.

Defects Per Opportunity – DPO

DPO = no. of defects / (no. of units X no. of defect opportunities per unit)

Defects Per Million Opportunities (DPMO, or PPM)

DPMO = dpo x 1,000,000 Defects Per Million Opportunities or DPMO can be then converted to sigma & equivalent Cp values in the next page. The DPMO, DPM, Sample Size, CI Calculator will help you calculate the metrics.

If there are 9 defects among 150 invoices, and there are 8 opportunities for errors for every invoice, what is the dpmo? dpu = no. of defects / total no. of product units = 9/150 = .06 dpu dpo = no. of defects / (no. of units X no. of defect oppurtunities per unit) = 9/(150 X 8) = .0075 dpo dmpo = dpo x 1,000,000 = .0075 X 1,000,000 = 7,500 dpmo What are the equivalent Sigma and CP values? See Sigma Table.

Converting Yield to sigma & Cp Metrics – Example

Given: a proportion defective of 1%

  • Yield = 1 – p = .990
  • Z Table value for .990 = 2.32σ
  • Estimate process capability by adding 1.5 σ to reflect the ‘real-world’ shift in the process mean 2.32σ + 1.5σ = 3.82σ
  • This σ value can be converted to an equivalent CP by dividing it by 3σ : CP = 3.82σ/3σ = 1.27 Note: Cpk cannot be estimated by this method

Six Sigma Capability Improvement

SixSigma-Capability-Improvement

Sigma Table

Yield dpmo Sigma (σ) Cp Equiv. COPQ (Cost of Poor Quality)
.840 160,000 2.50 0.83 40%
.870 130,000 2.63 0.88
.900 100,000 2.78 0.93
.930 70,000 2.97 0.99
.935 65,000 3.01 1.00
.940 60,000 3.05 1.02
.945 55,000 3.10 1.03 30%
.950 50,000 3.14 1.05
.955 45,000 3.20 1.06
.960 40,000 3.25 1.08
.965 35,000 3.31 1.10
.970 30,000 3.38 1.13
.975 25,000 3.46 1.15
.980 20,000 3.55 1.18 20%
.985 15,000 3.67 1.22
.990 10,000 3.82 1.27
.995 5,000 4.07 1.36
.998 2,000 4.37 1.46
.999 1,000 4.60 1.53 10%
.9995 500 4.79 1.60
.99975 250 4.98 1.66 5%
.9999 100 5.22 1.74
.99998 20 5.61 1.87
.9999966 3.4 6.00 2.00

Our free  DPMO, DPM, Sample Size, CI Calculator will help you calculate the metrics.

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Six Sigma Confidence Intervals Tutorial

By admin | Published: May 1, 2006

When we calculate a statistic for example, a mean, a variance, a proportion, or a correlation coefficient, there is no reason to expect that such point estimate would be exactly equal to the true population value, even with increasing sample sizes. There are always sampling inaccuracies, or error. In most Six Sigma projects, there are at least some descriptive statistics calculated from sample data. In truth, it cannot be said that such data are the same as the population’s true mean, variance, or proportion value. There are many situations in which it is preferable instead to express an interval in which we would expect to find the true population value. This interval is called an interval estimate. A confidence interval is an interval, calculated from the sample data, that is very likely to cover the unknown mean, variance, or proportion. For example, after a process improvement a sampling has shown that its yield has improved from 78% to 83%. But, what is the interval in which the population’s yield lies? If the lower end of the interval is 78% or less, you cannot say with any statistical certainty that there has been a significant improvement to the process. There is an error of estimation, or margin of error, or standard error, between the sample statistic and the population value of that statistic. The confidence interval defines that margin of error. The next page shows a decision tree for selecting which formula to use for each situation. For example, if you are dealing with a sample mean and you do not know the population’s true variance (standard deviation squared) or the sample size is less than 30, than you use the t Distribution confidence interval. Each of these applications will be shown in turn.

Confidence Intervals in Six Sigma Methodology

Confidence intervals are very important to Six Sigma methodology. To understand Confidence Intervals better, consider this example scenario: Acme Nelson, a leading market research firm conducts a survey among voters in USA asking them whom would they vote if elections were to be held today. The answer was a big surprise! In addition to Democrats and Republicans, there is this surprise independent candidate, John Doe who is expected to secure 22% of the vote. We asked Acme, how sure are you? In other words how accurate is this prediction? Their answer: “Well, we are 95% confident that John Doe will get 22% plus or minus 2% vote” In the statistical world, they are saying that John Doe will get a vote between 20% and 24% (also known is Confidence Range) with a probability of 95% (Confidence Level).

Definition of Confidence Intervals

According to University of Glasgow Department of Statistics, Confidence Interval is defined as: A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data. If independent samples are taken repeatedly from the same population, and a confidence interval calculated for each sample, then a certain percentage (confidence level) of the intervals will include the unknown population parameter. Confidence intervals are usually calculated so that this percentage is 95%, but we can produce 90%, 99%, 99.9% (or whatever) confidence intervals for the unknown parameter. In our Acme research example

  •   The confidence interval is the range 20 to 24
  • The confidence level is 95%
  • The confidence limits are 20 (lower limit) and 24 (upper limit)
  • The unknown population parameter is “What percentage of the total vote will John Doe Get”

Confidence Limits

Confidence limits are the lower and upper boundaries of a confidence interval. In our Acme example, the limits were 20 and 24.

Confidence Level

The confidence level is the probability value attached to a given confidence interval. It can be expressed as a percentage (in our example it is 95%) or a number (0.95).

Confidence Interval for a Mean

A confidence interval for a mean is a range of values within which the mean (unknown population parameter) may lie. Examples of Confidence Interval for a Mean* A Web master who wishes to estimate her mean daily hits on a certain webpage. * An environmental health and safety officer who wants to estimate the mean monthly spills.

Confidence Interval for the Difference between Two Means

A confidence interval for the difference between two means specifies a range of values within which the difference between the means of the two populations may lie. Examples of Confidence Interval for the Difference between Two Means* A Web master who wishes to estimate her difference in mean daily visitors between two websites. * An environmental health and safety officer who wants to estimate the difference in mean monthly spills between two production sites.

When we calculate a statistic for example, a mean, a variance, a proportion, or a correlation coefficient, there is no reason to expect that such point estimate would be exactly equal to the true population value, even with increasing sample sizes. There are always sampling inaccuracies, or error. In most Six Sigma projects, there are at least some descriptive statistics calculated from sample data. In truth, it cannot be said that such data are the same as the population’s true mean, variance, or proportion value.

There are many situations in which it is preferable instead to express an interval in which we would expect to find the true population value. This interval is called an interval estimate. A confidence interval is an interval, calculated from the sample data that is very likely to cover the unknown mean, variance, or proportion. For example, after a process improvement a sampling has shown that its yield has improved from 78% to 83%. But, what is the interval in which the population’s yield lies? If the lower end of the interval is 78% or less, you cannot say with any statistical certainty that there has been a significant improvement to the process.

There is an error of estimation, or margin of error, or standard error, between the sample statistic and the population value of that statistic. The confidence interval defines that margin of error. The next page shows a decision tree for selecting which formula to use for each situation. For example, if you are dealing with a sample mean and you do not know the population’s true variance (standard deviation squared) or the sample size is less than 30, than you use the t Distribution confidence interval. Each of these applications will be shown in turn.

Decision Tree for selecting What Formula to use:

Six-Sigma-Confidence-Interval-Formula

Six Sigma Z Confidence Intervals for Means

Z Confidence Interval for Means applies to a mean from a normal distribution of variable data. Use the normal distribution for the confidence interval for a mean if the sample size n is relatively large (= 30), and s is known. The confidence interval (C.I.) includes the shaded area under the curve in between the critical values, excluding the tail areas (the a risk). The entire curve represents the most likely distribution of population means, given the sample’s size, mean, and the population’s standard deviation.

Six-Sigma-Z-Confidence-Interval-Means-Chart Six-Sigma-Z-Confidence-Interval-Means-Formula1

Here we are making an assumption that the underlying data we are working with is distributed like the bell curve shown. The most common confidence interval used in industry is probably the 95% confidence interval. If we were to use its formula on many sets of data from the population, then 95% of the intervals would contain the unknown population mean that we are trying to estimate. And 5% of the intervals would not contain the population mean. 2.5% of the time, the interval would be low, and 2.5% of the time, the interval would be too high. The probability is 95% that the interval contains the population parameter. The 95% value is the confidence coefficient, or the degree of confidence. The end points of the interval are called the confidence limits. In the graphic on the top, the endpoints are defined by

Six-Sigma-Z-Confidence-Interval-Means-Formula2

Example – Z Confidence Interval for Means

Calculate a 95% C.I. on the mean for a sample (n = 35) with an x-bar of 15.6″and a known s of 2.3 ”

Six-Sigma-Z-Confidence-Interval-Means-Example

This interval represents the most likely distribution of population means, given the sample’s size, mean, and the population’s standard deviation. 95% of the time, the population’s mean will fall in this interval.

Use the t distribution for the confidence interval for a mean if the sample size n is relatively small (< 30), and/or s is not known. The confidence interval (C.I.) includes the shaded area under the curve in between the critical values, excluding the tail areas (the a risk). The entire curve represents the most likely distribution of population means, given the sample’s size, mean, and standard deviation.

Six-Sigma-t-Confidence-Interval-Mean-ChartSix-Sigma-t-Confidence-Interval-Mean-Formula

Use the χ2 (chi-squared) distribution for the confidence interval for the variance The confidence interval (C.I.) includes the area under the curve in between the critical values, excluding the tail areas (the a risk). The entire curve represents the most likely distribution of population variances (sigma squared), given the sample’s size and variation.

Six-Sigma-t-Confidence-Interval-Variance-Chart

Six Sigma t Confidence Interval for a Variance Example

Calculate a 95% C.I. on variance for a sample (n = 35) with an S of 2.3″
Six-Sigma-t-Confidence-Interval-Variance-Example

This interval represents the most likely distribution of population variances, given the sample’s size and variance. 95% of the time, the population’s variance will fall in this interval.

This Z Confidence Interval for Proportions applies to an average proportion (which is from a binomial distribution).

Six-Sigma-Z-Confidence-Interval-Proportions-Chart

 

Example – Z Confidence Interval for Proportions

Six-Sigma-Z-Confidence-Interval-Proportions-Example

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DMAIC Process

By admin | Published: April 11, 2006

Six Sigma Engineering

  • A Six Sigma Engineer develops efficient and cost effective processes to improve the quality and reduce the number of defects per million parts in a Manufacturing/Production environment.
  • Six Sigma Engineers determine and fine tune manufacturing process. Once a process is improved, they go back and re-tune the process and reduce the defects. This cycle is continued till they reach 3.4 or less defects per million parts.
  • Six Sigma is all about knowledge sharing. If a company has more than one manufacturing unit/plant, its more than likely that one of the plants produces better quality than others. The Six Sigma team should visit this higher quality plant and learn why its performing better than others and implement the techniques learned across all other units.
  • Research/Design department within a company can use the above techniques to learn from another R&D departments in the same company or affiliate companies and implement those techniques.
  • Motorola developed a five phase approach to the Six Sigma process called DMAIC.

Read More »

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FMEA Tutorial

By admin | Published: October 10, 2005

Definition of Failure Mode and Effects Analysis

Failure Mode

The manner in which the product/part or service does not meet the customer’s expectations

Effects Analysis

A study of the effects of failure on the function or purpose of the product/part or service The customer could be external to the company, or internal (within the company). It is considered a reliability planning tool, but it has also become a method for prioritizing alternative actions (that do not deal with failure modes), e.g., in the Six Sigma process.

FMEA is a systematized group of activities intended to:

  • Recognize and evaluate the potential failure modes and causes associated with the designing and manufacturing of a product
  • Identify actions which could eliminate or reduce the chance of the potential failure occurring
  • Document the above process.

It increases the likelihood that potential failures, and their effects and causes, will be considered prior to the final design and/or release to production. The key to the actions in this Reliability Analysis method is to plan preventive actions. A completed FMEA, which should be applied in an iterative process, contains a great deal of information about the product or process. It can be used as the starting point for later control plans, trouble-shooting guides, preventive maintenance plans, etc.

Key Things To Keep In Mind

“One of  the most important factors for the successful implementation of an FMEA program is timeliness…  Up front time spent properly completing an FMEA well, when product/process changes can be most easily and inexpensively implemented, will minimize late change crises.” AIAG FMEA Instruction Manual (3rd Edition)When going through the FMEA process, it is also important to remember to base your decisions on data, not on hunches! It should occur very early in the planning cycle.  FMEA teams will find themselves spending more time than usual early on, which will lead to leveraged savings later on. The use of data to verify the relationships between root causes and effects, to establish accurate rating criteria, and to determine effective preventive actions is one of the critical-to-success factors in the FMEA process.

Costs vs. Benefits

Lots of Tedious Work →  Increased success of implementation, & knowledge well captured by the cross-functional team.
Do not expect the up-front investigation and analysis to be quick or easy, but the extra initial work will typically provide an excellent payback.

Types  Of  FMEA: Design FMEA (DFMEA), Process FMEA (PFMEA)

At the DFMEA level, it is usually recommended to study each subsystem separately, and each component separately.  Their inter-relations can be evaluated in the System FMEA.
The System FMEA examines system deficiencies caused by potential failure modes between the functions of the system.  This includes the interactions between the systems and the elements of the systems.
The PFMEA is conducted on a process, whether it be in a manufacturing or a service environment.  It is generally recommended to study each machine or sub-process separately.  Their inter-relationships can also be studied in a System FMEA.  Service FMEAs are usually not preceded by a DFMEA.

Types Of FMEA

Design FMEA DFMEA – Objectives

  • Maximize system quality and reliability
  • Minimize design-based failure effects on the system
  • Should also take into account DFM/A/(X) principles

Process FMEA PFMEA – Objectives

  • Maximize system quality, reliability, & productivity
  • Minimize production process – based failure effects on the system
  • Minimize variation around the design specs due to the process

The key difference in the objectives between the two is the focus of the FMEA.  When conducting a DFMEA, the team must remember to think in terms of the causes and effects of failure modes due to the design itself.   The causes usually involve product design variables that can be specified by the design team.

Design for Manufacturability/Assembly All other (DFM/A/(X) considerations should be included in the DFMEA as well, e.g., tooling access, robustness to sources of process variation, ability of product to be produced at planned production rates, maintainability, etc. In a PFMEA, the team will be focused on those failure modes and causes that could result from the production or service process itself rather than from the design of the product.

Typical Team Structure:

Design Team:

  • Responsible Design Engineer*  –  Leader
  • Test Engineer/Technician
  • Reliability/Quality Engineer
  • Marketing/Product Manager
  • Material Management/Purchasing
  • Field Service Engineer/Technician Process

Typically,
the responsible designer will lead the DFMEA team. It is important to have cross-functional participation on the team, including at least some of the members from the after PFMEA team.

Process Team:

  • Responsible Manufacturing/Process Engineer**  –  Leader
  • Design Engineer*
  • Quality/Reliability Engineer
  • Tooling Engineer/Technician
  • Material Management/Purchasing
  • Responsible Operators
  • Maintenance Technician
  • Manufacturing/Process Engineer**

Usually, but not always, the leadership of the team changes to the responsible process engineer. This person ideally would have also been on the DFMEA team; and the DFMEA team leader would stay on as a member of the PFMEA team.  Several other team members, but not all, could change to reflect a heavier emphasis on the process under study.

The  FMEA Quality  Lever – Where  To  Put  The  Effort

If you apply the energy sooner, or further away from the introduction of the product or service, you will get much more leverage for your effort. Waiting to make the improvements or fixes after production starts will require a lot more effort.

FMEA-Quality-Lever

In the above diagram, the further upstream you make changes, the more impact you will make.

Where  To  Spend  The  Effort? — Total  Time  Spent

In the sequential approach to product development, more time is typically spent on debugging the product after it is released to the market.  In  the concurrent approach, more time is spent in the up-front planning.  However, there will usually be much less time spent later on the post-production problem solving.

FMEA-Where-To-Spend-Effort

Spending more planning man hours up front does not mean that the product release time is pushed back. The cross-over point of these two overlaid graphs does not mean anything.  Q Which approach has less area under the curve? Why? A The concurrent approach very often results in 20-40% fewer man hours of total development time, due to the effectiveness of the up-front planning.

The main purpose of FMEA: Besides making customers happier by minimizing  potential failures, identifying necessary changes early, & prioritizing improvement  efforts, FMEA also provides:

  • A formal record of reliability and safety analysis ← The completed FMEA should become part of the design package.
  • A starting point in the preparation of field service policies ← And for preparing trouble-shooting guides.
  • A starting point for a preventive maintenance data base ← From the resulting prioritized preventive actions.
  • An indicator for test point location and sequencing ← From the identified
    causes, & also from the resulting prioritized preventive actions.

FMEA Quiz

  1. An FMEA is primarily considered a problem-solving tool.
    Incorrect. FMEA is primarily a reliability planning tool which considers the effects and causes of potential failure modes on the function or purpose of the part or service.
  2. A DFMEA evaluates the potential failure modes and causes associated with the manufacturing of a product
    Answer:  Incorrect.  A DFMEA evaluates the potential failure modes and causes associated with the design of a product
  3. The use of data to verify the relationships between root causes and effects is one of the critical-to-success factors in the FMEA process.
    Answer:  Correct
  4. FMEA teams will find themselves spending more time than usual early on in the planning process, which will usually lead to delayed product introductions.
    Answer:  Incorrect.  By spending the extra time on an FMEA early on in the planning process, there should be less time spent later debugging the product or service at its introduction to the market.
  5. At the DFMEA level, it is usually recommended to study each subsystem separately, and each component separately.
    Answer: Correct
  6. The objectives of a PFMEA include minimizing production process – based failure effects on the system but usually not minimizing  variation around the design specs due to the process. Answer: Incorrect.  The objectives of a PFMEA usually include both.
  7. When doing both a DFMEA and a PFMEA, it is important to keep the same team members throughout both FMEA processes.
    Answer: Incorrect.  It is typical for some (but not all) of the team members, and maybe even the team leader, to change for the PFMEA to reflect the emphasis on the process, rather than the design, of the part or service.
  8. Besides prioritizing improvement efforts, FMEA can also provide a formal record of reliability and safety analysis.
    Answer: Correct

Copyright © 2000-2010 Michael G. White. All rights reserved.

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Six Sigma Acceptance Sampling Tutorial

By admin | Published: May 2, 2005

Operating Characteristic Curve or The OC Curve

The OC curve quantifies the α and β risks of an attribute sampling plan. Below is an ideal OC curve (the bold line) for a situation in which we might want to accept all lots that are, say, ≤ 1% defective and reject all lots that are > 1% defective:

OPERATING-CHARACTERISTIC-OC-CURVE

With this ideal (no risks) curve, all batches with ≤ 1% defective incoming quality level would have a probability of acceptance (Pa) of 1.0. And, all lots with > 1% defective would have a Pa of 0. The Pa is the probability that the sampling plan will accept the lot. It is the long-run % of submitted lots that would be accepted when many lots of a stated quality level are submitted for inspection. It is the probability of accepting lots from a steady stream of product having a fraction defective P. Read More »

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Six Sigma Tutorial

By admin | Published: April 11, 2005

What Is Six Sigma? Definition Of Six Sigma, Lean Six Sigma Concepts

Six Sigma stands for Six Standard Deviations (Sigma is the Greek letter used to represent standard deviation in statistics) from mean. Six Sigma methodology provides the techniques and tools to improve the capability and reduce the defects in any process.

It was started in Motorola, in its manufacturing division, where millions of parts are made using the same process repeatedly. Eventually Six Sigma evolved and applied to other non manufacturing processes. Today you can apply Six Sigma to many fields such as Services, Medical and Insurance Procedures, Call Centers.

DMAIC

Six Sigma methodology improves any existing business process by constantly reviewing and re-tuning the process. To achieve this, Six Sigma uses a methodology known as DMAIC (Define opportunities, Measure performance, Analyze opportunity, Improve performance, Control performance). Read More »

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