A Design Failure Mode Effects Analysis is an investigative summary of the potential risks during production that are identified in the design stage. From a history in aerospace engineering, FMEA applications now stretch from industries like software and manufacturing.
KEY TAKEAWAYS
Allows engineers to assess risk factors before resources spent
Data is filed on a DFMEA matrix for analysis
Type of mistake-proofing to evaluate options for production
Involves 10 steps based on inductive reasoning
History of DFMEA
Failure Modes and Effects Analyses were first used by aerospace engineers at NASA in the 1960s to track rocket prototypes. During the 1970s, FMEA became more popular in other industries, like US environmental and geological projects.
Nowadays, FMEA is routinely used by quality, reliability, and safety engineering teams. The point of this process is to analyze an effects analysis in order to determine a failure mechanism – once a failure mechanism, or point of failure, has been identified, design modifications can be made to fix the errors before they are seen on the production room floor.
With a DFMEA in particular, the intended perspective is on design, and so the analysis must take place concurrently with the design and testing phases pre-production.
There are 3 other types of FMEA:
FMECA stands for Failure Mode Effects and Criticality Analysis, which divides the assessment into two measurements: failure and criticality of failure modes
PFMEA adds “process” to the beginning of the acronym, representing a focus on assembly workflow, and is meant for the next step after design
Functional FMEA is often used for software before the design stage has begun, and identifies the core requirements for design
Inductive vs Deductive Reasoning
An FMEA works using inductive – or forward – reasoning. This top-down approach works by making observations about a service or product and gradually drawing around a hypothesis to reach a final assessment.
The opposite to inductive reasoning is deductive reasoning, which is backward – or bottom-up thinking. This type of reasoning draws valid inferences and assembles these into a conclusion where the logic follows the premises.
The 10 Steps in a DFMEA
There are ten steps of a DFMEA, and some of them seem repetitive, but are essential for uncovering minute potential failures.
Review design – the assessment begins with an introductory critical overview
Identify potential failure modes – participants will mark areas where they suspect failures could occur, whether or not there is evidence of failure
Identify potential failure effects – the effects of certain mechanism failures can have far greater impact for use and safety than failures themselves, so these effects are anticipated in the report
Identify potential causes of failure – different than effects, pinpointing potential causes of that failure is a way of identifying a trigger or catalyst that may be the culprit of later problems
Assign severity ranking – A number from 1 (negligible effect) to 5 (catastrophic failure) is assigned, depending on the results of the type of failure and whether only parts of the product are damaged or rendered irreparable
Assign occurrence (probability) ranking – A number from 1 (extremely unlikely) to 5 (inevitable, frequent failure) is assigned to represent a range of the frequency of failures
Assign detection ranking – A number from 1 (certainty of fault detection) to 5 (undetected by operators) is assigned to indicate the probability that failures will be caught and corrected by real-time operators, and is helpful for assessing the maintainability of a production system
Calculate Risk Priority Number (RPN) – The RPN is calculated using the S, P, and D rankings above using the following formula:
RPN = Severity (of failure) × probability (of failure happening) × detection (the chance that the failure would go undetected before correction)
Outline action plan – at this stage, engineers will draw up a plan to correct the design risks in order to start testing their concluded hypothesis
Recalculate RPN – finally, the RPN is redone to see the anticipated impact of the changed design plan
Common DFMEA Applications
In almost any industry where quality is a key factor, a DFMEA can help with error prevention and greater control during assembly and production. Some of the applications of a DFMEA:
Designing a new, independent product
Popularly used in total quality management (TQM) business philosophy
For making product improvements to an underperforming product
For re-designing a system of assembly or production
For developing new product distribution and marketing strategies
For saving costs on failure detection by pro
DFMEA Industries
Any team wanting to assess their probable risk of failure in the production and delivery of a product or service can perform a DFMEA. They are most popularly done in the following industries:
Manufacturing
Software (SaaS)
Healthcare
Service Industries Of course, the depth of a DFMEA will vary depending on the levels of error detection necessary, and some industries may more reliably use root-cause analyses or deductive reasoning strategies.
Downsides of DFMEA
A DFMEA report is extremely unique depending on the product and the team assessing it. One of the biggest difficulties of any FMEA is that the failure may be identified, but it cannot be fixed until the failure mechanism is also identified. There is great risk for this minor difference to lead to errors of correlation and causation.
Moreover, assigning numerical indicators to risk factors may help calculate an overall probability of failure, but may not be accurate enough in assessing the actual risk when there are concurrent failures. If there is more than one problem, then many versions of a DFMEA may be required as well as the more general root-cause analysis (RCA).
