DEFINITION
An End-of-Life Assessment consists of determining when the product or pieces of the product will start approaching End-of-Life.

SITUATION
After the product has been in the field for a period of time, product End-of-Life decisions must be made. The Reliability Prediction and FMECA predict when End-of-Life is likely to occur and Accelerated Life Tests measure End-of-Life (for those components susceptible to it). Now, decisions must be made based on these analyses and tests, as well as the trend of data from the field to determine when to stop sending failed products back out in the field, and when to start offering upgrades and trade-ins.

OBJECTIVE
We Perform End-of-Life Assessments to
• determine when a product is starting to wear-out in case the product needs to be discontinued.
• monitor preventive maintenance strategy and modify as needed.
• monitor spares requirements to determine if a change in allocation is necessary.
• tie back to End-of-Life Analysis done in the Design Phase to determine accuracy of analysis.

VALUE TO YOUR ORGANIZATION
Knowing when a product has reached or will reach End-of-Life is crucial because an unexpected End-of-Life situation can lead to unhappy customers and high field return/high warranty $.

RELIABILITY INTEGRATION
An example of Reliability Integration during the End-of-Life Asessment is as follows:

End-of-Life used in conjunction with Reliability Predictions
During the Design Phase, we perform reliability predictions. One element of the prediction is to estimate the End-of-Life. This can then be compared with actual data once we start collecting field data.

METHODOLOGY
End-of-Life Prediction
The Reliability Prediction and FMECA (if performed) shall be reviewed to determine which components in the system have a dominant wearout mechanism. For these components, an End-of-Life Prediction shall be performed. Any data from Accelerated Life Tests shall also be used to help in this prediction process. Note that this service is usually performed during the Design Phase along with the FMECA and as part of the Preventive Maintenance strategy. However, if it has not been done up to this point, it must be done here so that we have something to compare with the field data. See the "Bathtub" Curve below for where the end-of-life portion of the curve lies.

End-of-Life Assessment
Field data shall be plotted against time to determine if the product is approaching wearout. This shall be compared with the predicted estimate for confidence in results. Then, an estimate shall be made as to when End-of-Life is occurring, along with the slope of the end-of-life curve. See Weibull Plot below for an example of a product entering its end-of-life phase. From this, the cost impacts shall be examined and recommendations shall be made for when to stop sending failed products back out in the field and when to start offering upgrades and trade-ins.

Specific failure trends shall also be compared with predicted trends and corrective actions shall be taken.

CASE STUDIES/OPTIONS
The following case studies and options provide example approaches. We shall tailor our approach to meet your specific situation.

1) Modifying Preventive Maintenance Strategy Based on End-of-Life Results
A Semiconductor Manufacturing Equipment company had instituted a 5 year Preventive Maintenance schedule on their belts and asked us to review their field data to find out how much fallout they were getting in the 5 years to determine if the Preventive Maintenance time needed to be changed. We determined from the data that the 5 years initially set forth was too optimistic and that it needed to drop down to every 3 years.

2) Making Design Changes Based on End-of-Life Results
A Networking Equipment company was experiencing a high fallout on their CPU fan. We reviewed the field data and plotted it and determined that the fan was approaching End-of-Life after 2 years. No End-of-Life prediction was ever performed and no Preventive Maintenance strategy was ever put in place. We performed an End-of-Life Assessment on the fan and determined that the fan was undersized for the application. We recommended a slightly larger fan that had a 5 year life and helped them institute a strategy on how to roll-out this new fan.

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