failure-mode-and-effects-analysis

What Is A failure Mode And Effects Analysis? Failure Mode And Effects Analysis In A Nutshell

A failure mode and effects analysis (FMEA) is a structured approach to identifying design failures in a product or process. Developed in the 1950s, the failure mode and effects analysis is one of the earliest methodologies of its kind. It enables organizations to anticipate a range of potential failures during the design stage. 

Understanding a failure mode and effects analysis

History is littered with examples of product recalls because of poorly designed products or processes. 

One such example is the Takata airbag recall, the largest automotive recall in the world affecting an estimated 100 million vehicles.

The recall was caused by design and manufacturing problems which lead to the airbag becoming highly explosive if exposed to high humidity.

Developed in the 1950s, the failure mode and effects analysis is one the earliest methodologies of its kind.

It enables organizations to anticipate a range of potential failures during the design stage. 

When conducting a FMEA, the team is prompted to evaluate the:

  • Steps in the process.
  • Failure modes – what could go wrong?
  • Failure causes – why would the failure occur?
  • Failure effects – what would be the consequences of failure occurrence?

Conducting a failure mode and effects analysis

A FMEA should be performed using a simple spreadsheet. In general terms, here is how a typical business might run the analysis:

Step 1 – Assemble a team

Start by creating a cross-functional team with a diverse range of knowledge about the process or product to be analyzed.

This may include manufacturing, quality control, customer service, maintenance, or purchasing. 

Step 2 – Define the scope

In other words, is the FMEA being used for a concept, system, process, or design? Where are the boundaries and what is the level of detail required?

Process steps should be listed in rows at the far left of the spreadsheet.

Step 3 – List failure mechanisms

List the ways that each process step can fail through brainstorming or the reviewing of existing documentation.

This list should be exhaustive and many steps will have multiple avenues to failing.

Then, repeat the same process for the potential effects of each failure.

Step 4 – Assign severity rankings

Using a scale of 1 to 10, rank the severity of the potential effect on the customer.

A score of 9 would denote a high-impact event, while a score of 2 would denote a low-impact event.

Step 5 – List and score potential causes of failure

How could the failure effect occur?

For example, a bank customer could become dissatisfied (failure effect) because of an ATM running out of cash (failure cause). 

For each potential failure cause, rank it according to how frequently it is likely to occur.

Rare occurrences receive low scores, while frequent events receive higher scores.

Step 6 – List and score current process controls

What are the existing controls that prevent the failure mode from occurring? Some controls may only detect failure modes after they occur.

Returning to the previous example, the bank might receive an internal alert that cash in the ATM is running low.

Each control should then be scored according to its ability to detect the occurrence of a failure event.

A failure event that is easily detected by a control is given a low score while a higher score is assigned to an inconspicuous failure event. 

Step 7 – Determine the risk priority number 

The risk priority number (RPN) is the overall risk score of an event. It can be calculated by multiplying the severity, occurrence, and detection scores together.

A process step with a higher RPN demands immediate attention. Lower RPN steps are at less risk of failure.

Step 8 – Propose recommended courses of action

Lastly, the team should propose a course of action for:

  • All process steps with a high RPN.
  • All failure effects with a severity score of 9 or 10, or those effects associated with customer safety or regulation.
  • All process steps scoring highly for both severity and occurrence – otherwise known as high criticality combinations.

Actions that reduce risk ultimately involve eliminating the failure or addressing the cause of the failure.

Processes can also be improved by increasing design tolerance and reducing variation in process output quality.

Lastly, controls can be improved by making processes and tools mistake-proof (often achieved through automation).

Enhanced inspection and evaluation techniques can also increase control effectiveness.

Failure mode and effects analysis example

Consider the FMEA analysis for a company that designs bicycle brake cables. The assembled team defines three potential failure models and their associated effects:

  1. Cable breaks (potential failure mode) – bicycle rider is not able to close brake caliper to reduce speed, which may result in an accident and/or injury (failure effect). 
  2. Cable binds – bicycle rider is required to use more force to close brake calipers because of increased friction between brake cable and sheath.
  3. Cable slips at brake lever locking nut or caliper – brake caliper does not close when correct amount of force is applied to lever. This may result in less friction between the brake pads and wheels and a possible accident and/or injury.

Next, the team scores each failure mode for its potential severity (step four in the process outlined above):

  1. Cable breaks – 10.
  2. Cable binds – 6. 
  3. Cable slips at break lever locking nut or caliper – 9. 

Then, the team discusses how each failure could arise and then score it according to how frequently it may occur.

Remember, frequent events receive higher scores than events perceived to be rarer. 

  1. Cable breaks – nylon abrasion due to improper material use (2), nylon becomes brittle because of low relative humidity or repeated bending under load (4).
  2. Cable binds – cable that is bent or kinked because of misalignment (5), poor or non-existent lubrication between sheath and cable (2). 
  3. Cable slips at break level locking nut or caliper – the diameter of the brake cable is too small to be secure after the locking nut is engaged (7).

In step six, the team lists the current controls that either prevent a failure mode from occurring or detect it after it has occurred.

Each control is also scored according to how well it detects a failure event, with lower scores associated with events that are more easily detected.

  1. Nylon abrasion due to improper material use – choice of cable material based on applicable American National Standards Institute (ANSI) criteria, factory cable strength test (1).
  2. Nylon becomes brittle because of low relative humidity or repeated bending under load – laboratory examination for evidence of cracking (4).
  3. Nylon cable that is bent or kinked because of misalignmentdesign guide for nylon cable material, inspection of all new cable material (2). 
  4. Poor or non-existent lubrication between sheath and cabledesign guide for cable lubrication, perform brake lever effort test (1). 
  5. The diameter of the brake cable is too small to be secure after locking nut is engaged – undertake brake mechanism tolerance study, perform brake calibration test (2). 

Now it is time to calculate the RPN by multiplying the severity, occurrence, and detection for each event:

  1. Cable breaks because of nylon abrasion – 10 x 2 x 1 = 20.
  2. Cable breaks because nylon becomes brittle – 10 x 4 x 4 = 160.
  3. Cable binds because of bend or kink in cable – 6 x 5 x 2 = 60.
  4. Cable binds because of inadequate lubrication – 6 x 2 x 1 = 12. 
  5. Cable slips because of small cable diameter – 9 x 7 x 2 = 126.

From these results, failure effects that result in accident or injury to the rider should be prioritized.

A cable that slips because of a smaller diameter is a high criticality combination because it scores relatively highly for both severity and occurrence.

In this case, one potential way to improve this process would be to redesign the cable locking mechanism from scratch.

Key takeaways:

  • A failure mode and effects analysis is a structured, evaluative approach to identifying failures in a product or process.
  • A failure mode and effects analysis forces teams to critically evaluate each step in a process. This is achieved by considering the modes, causes, and potential effects of process failures.
  • A failure mode and effects analysis can be performed using spreadsheet software. Teams must assign weighted scores to a range of variables and focus their efforts on process steps with the highest risk of failure.

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Personal SWOT Analysis

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Failure Mode And Effects Analysis

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A failure mode and effects analysis (FMEA) is a structured approach to identifying design failures in a product or process. Developed in the 1950s, the failure mode and effects analysis is one the earliest methodologies of its kind. It enables organizations to anticipate a range of potential failures during the design stage.

Blindspot Analysis

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Comparable Company Analysis

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A comparable company analysis is a process that enables the identification of similar organizations to be used as a comparison to understand the business and financial performance of the target company. To find comparables you can look at two key profiles: the business and financial profile. From the comparable company analysis it is possible to understand the competitive landscape of the target organization.

Cost-Benefit Analysis

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Agile Business Analysis

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STEEPLE Analysis

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Pestel Analysis

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A DESTEP analysis is a framework used by businesses to understand their external environment and the issues which may impact them. The DESTEP analysis is an extension of the popular PEST analysis created by Harvard Business School professor Francis J. Aguilar. The DESTEP analysis groups external factors into six categories: demographic, economic, socio-cultural, technological, ecological, and political.

Paired Comparison Analysis

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A paired comparison analysis is used to rate or rank options where evaluation criteria are subjective by nature. The analysis is particularly useful when there is a lack of clear priorities or objective data to base decisions on. A paired comparison analysis evaluates a range of options by comparing them against each other.

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