Design FMEA (DFMEA) Tutorial

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 »

Posted in Six Sigma | Tagged , , , , , , , , , , , , | 2 Responses

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

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
Read More »

Posted in Tutorials | Tagged , , , , , , | Leave a comment

Six Sigma Confidence Intervals Tutorial

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”
Posted in Tutorials | Tagged , , , , , , , , , , , | 2 Responses

DMAIC Process

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 »

Posted in Tutorials | Tagged , | Leave a comment

FMEA Tutorial

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.
Read More »

Posted in Tutorials | Tagged , , , , | Leave a comment

Six Sigma Acceptance Sampling Tutorial

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 »

Posted in Tutorials | Tagged , , , , , , , | Leave a comment

Six Sigma Tutorial

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 »

Posted in Tutorials | Tagged , , | 8 Responses
%d bloggers like this: