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Methods to Estimate ALT Value
Here is an example of how to determine the f uture value of a specif ic reliability task. Many of us f ace the
challenge of how to justif y spending product development resources to provide insights and inf ormation to the
rest of the team. Accelerated lif e testing (ALT) is particularly dif f icult, as it is time consuming, expensive and at
times statistically complex. Having a clear method to estimate the value serves your career and the
organization well, as both attempt to make the right investments.
ALT and Market Share
A design team working on a medical device understands that the market share is related to the product
reliability. The current product perf orms adequately yet, has the highest f ield f ailure rate of similar products.
Customers complain about the poor reliability, and the market share ref lects their comparative reliability
ranking. The most reliable product is also the one with the highest market share.
The design challenge is to create a product that is more reliable than the competition, at about the same price
point, and if possible with improved f unctionality. The early concepts all include a novel design using an
unproven sealing material (in terms of reliability). The uncertainty suggests the implementation of an
accelerated lif e test to estimate the expected product reliability.
Achieving a higher reliability is expected to result in more than tripling the market share in the f irst year. This
would result in sales of the $3k/unit product to jump f rom 10k per year to approximately 30k per year, meaning
an additional $60m in revenue. Furthermore, the increase in sales would require more than doubling the
manuf acturing capacity, at a cost of $5 million. The decision to increase the manuf acturing capacity is
dependent on the estimated product reliability. In order to have the capacity available to meet the expected
demand the decision has be made and the $5 million committed prior to the start of production.
Reliability Goal and ALT Discussion
The current product achieves 90% reliability over two years. The best competitive product is estimated to
achieve 98% reliability over the same period. The goal f or the new design is 99% reliability over two years or
better. This is a major goal and simply conducting an ALT is not going to achieve the result. Yet, a key element
is the understanding if the goal has been achieved or not. The $5 million investment in manuf acturing depends
on knowing if the design will or will not meet the goal.
In this case ALT can answer the question, as it’s f ocused on the expected dominant f ailure mechanism . The
f ailure mechanism and the stresses are all known. The new design using novel material does leave the
uncertainty around how the design will actually perf orm. A well-designed ALT has the capability to ascertain the
expected reliability perf ormance.
ALT is of ten an expensive test to conduct. The test design, samples, product operation jigs (robots,
actuators, sof tware, etc.), monitoring equipment and f ailure analysis all add to the cost. Let’s assume the total
test planning and setup cost is $50k.
The high reliability to demonstrate will require a signif icant number of samples. The f ollowing f ormula 
provides a rough estimate of the number of samples needed f or a test to demonstrate 99% reliability with 90%
conf idence assuming no f ailures of any tested samples.
n is the sample size
C is the statistical conf idence
R is the reliability
The 230 sample number is based on a success testing approach assuming the f ailure mechanism and
associated stress is well understood. Reducing the sample size with the use of degradation testing, or some
other method may increase the testing complexity and result in lower overall costs.
The cost of the subsystem that holds the seal is $200 per unit. 230 x $200 estimates the cost f or samples of
$46k. The total cost of the ALT is approximately $96k.
In this situation the test results provide a binary result. The population either does or does not achieve at least
99% reliability. Keeping in mind the ALT is run with a sample to represent the population, there is some
uncertainty about the results. Statistical error may lead to f our outcomes as shown in table 1 .
The unknown actual Reliability
is less then 99%
Test Result Is TRUE Is FALSE
R >= 99% Type I error Correct
R < 99% Correct Type II error
Table 1 Statistical Errors
Assuming the test design used 90% conf idence and has a 90% power, we have a 10% chance of thinking the
reliability is less than it actually is, and to not invest in added manuf acturing capacity (lost opportunity f or
increased sales). Further, there is a 10% chance of thinking the reliability is better than 99% when it is not, thus
investing in added capacity when demand will not materialize.
Going in to the ALT we have a 50/50 chance that the new material and design will meet the 99% reliability goal.
Combining that with the uncertainty of statistical error and a $5m decision, we can calculate the value of the
The Return on Investment (ROI) is the ratio of the expected return over the cost. $4m over $96k results in an
ROI over 41.
Of course, the $5m decision isn’t the only f actor in the value of the ALT. It also provides a base line f or f urther
testing (test cost savings); it may provide inf ormation on the amount of margin the design has over the goal,
and permit f urther design enhancements. It also conf irms the change in reliability permitting proactive changes
in warranty accruals and service and repair operations.
See also Reliability, HALT and Derating Value articles.
1. Nelson, Wayne. Accelerated Testing: Statistical Models, Test Plans, and Data Analysis. Edited by S S Wilks
Samuel. Wiley Series in Probability and Mathematical Statistics. New York: John Wiley & Sons, 1990, pg. 3.
2. Wasserman, Gary S. Reliability Verification, Testing and Analysis in Engineering Design. New York: Marcel
Dekker, 2003, pg. 209.
3. Ott, Lyman. An Introduction to Statistical Methods and Data Analysis. Belmont, Calif .: Duxbury Press, 1993,