f msreliabilit y.com http://www.fmsreliability.com/education/reliability-predictions/
If only we truly had working a crystal ball or another device to predict the f uture. From the general wondering
about the enemies next move, to the soldier hoping their equipment will work. In the corporate board room
estimating the competitions next move, to the maintenance manger ordering spare parts, we have many uses
f or knowing the f uture.
We of ten look to past perf ormance to provide an indication of the f uture. Has this mutual f und regularly
provided adequate returns? If so, we predict it will going f orward. And anyone that has reviewed mutual f und
perf ormance also has read or heard the admonishment to not use past perf ormance to estimate f uture
returns. Mutual f unds, markets, business and battlef ields all change and respond in sometimes unf oreseen
Of course when f aced with a decision we of ten do need to f orm some prediction about f uture conditions and
possible outcomes. Whether investing or ordering spare parts or preparing design f or production, we use
predictions about the f uture to help determine the right course of action.
While a young and new reliability engineer working at corporate headquarters, a senior reliability engineer in
division called to ask me if I could run a parts count prediction on one of their products. Specif ically a Bellcore
(now Telecordia) prediction on the products two circuit boards. I said yes, despite having never done one
bef ore nor really even knowing what a parts count prediction was or how it was usef ul. I had just that week
received a demo copy of Relex (now part of PTC) prediction module and this project would be a good way to
learn both about parts count predictions and the sof tware.
I quickly learned that the basic parts count prediction used the bill-of -materials and a database of f ailure rates
to tally the expected f ailure rate f or the circuit board. A multilayer ceramic capacitor had a f ailure rate of 5 FIT
(f ailures per 109 hours), and the analog ASIC was listed with 450 FIT. The sof tware helped match the
components to their f ailure rates and did the math resulting in a f inal estimate f or the expected f ailure rate of
the product when used by customers.
It took about 2 hours to make the prediction, of which half or more of the time was spent learning the
sof tware. Not having any inf ormation other than the BoM all the settings in the prediction sof tware were at
def aults, nominal temperature, derating, quality level, etc.
This was magic. Pour in a list of parts and af ter a f ew milliseconds of computing time we know the f uture. Or
My f irst check was on the notion that many of our product f ailed due to power supplies, connectors, and f ans.
The prediction results listed the power supply and connectors in the top f ive of expected f ailure rates, and
there wasn’t a f an in the system, so it seemed about right. The more complex components were expected to
f ailure more of ten or sooner than simpler components.
Where did the f ailure rates listed in the table come f rom? How did the f olks at Bellcore know enough to list the
values. With a little reading and a phone call I learned that periodically the team at Bellcore would gather f ailure
rate inf ormation f rom a wide range of sources, including GIDEP and major telecommunications companies.
They would sort and analyze the data and create historical models of the f ailure rates including the ef f ects of
temperature, derating, quality, etc. The equipment they studied was primarily used in the military and
telecommunications inf rastructure. Mostly boxes with circuit boards.
The electronics industry changes a lot in f ive years, yet it was clear that unless we caref ully resolved every
f ailure to the component level and knew the use conditions we would be hard pressed to do better than the
team at Bellcore. The product I did the prediction was similar to products in the telecommunication industry, not
exactly, yet close enough it seemed.
Then I wondered about the calculations being done once the sof tware had the BoM. Apparently the approach
was rooted in the time prior to computers and used a f ew simplif ying assumptions to make the calculations
easy to accomplish with mechanical adders and a slide-rule. One of the properties of the exponential f unction
is the ability to add exponents. So, if we assume every f ailure rate is constant over time we can use the
exponential distribution to model the f ailure rate. Then f or a list of component f ailure distributions we simply
add the f ailure rates. Then we can estimate the reliability at any time period of interest by calculating a single
product and single exponent.
Lambda being the f ailure rate and t being time.
This assumption assumed that components and theref ore products enjoyed a
constant f ailure rate. Despite knowing this was not true f or any of our products
based on caref ully qualif ication and f ield data analysis, f or the parts count prediction we made this
assumption. This cast a serious shadow over the accuracy of the prediction. See the site NoMTBF.com f or
much more inf ormation and ref erences that detail additional concerns.
There were additional questions that f ound inadequate answers f urther eroding my acceptance of the results
the parts count prediction produced. I didn’t want to send back a report with f aulty prediction and I didn’t know
how to proceed. Furthermore, I recalled that admonishments including with f inancial historical data, and
wondered way we even tried to estimate the f uture of f ailure rates.
Value of Predictions
First I called the reliability engineer that requested the prediction. He thanked me and said what I did was f ine.
He agreed with my concerns and that the result was not even close to what the actual f ailure rate. He assured
me that he and the team would not take the value to seriously, in f act they were not going to use it at all.
Well, gee thanks. Why did I just spend my morning doing this prediction f or them.
The prediction report was requested by a major customer as a condition of the purchase. They didn’t really
know what to do with the reported prediction other than they wanted to make sure we did the parts count
prediction. It was to simply check of f the box in order f or the sale to occur. Nothing more.
Second, I talked to my mentor as a troubled young engineer. He said we basically understood any prediction
was wrong. Just as all models are wrong some are usef ul, some reliability predictions are also usef ul. In this
case the value of my two hours was to help secure a multi million dollar sale by meeting of the customer
The value of any prediction, whether a parts count or physics of f ailure model, was not in the actual resulting
value. The value was in what we did with the result. For reliability engineering work, even a parts count, even in
it’s simplest f orm, encourages using less parts and operating at lower temperatures. Both are good f or
product reliability in general, thus the resulting behavior to reduce part counts and temperature rise increase
We use reliability predictions to estimate a product’s perf ormance. There are many ways to create an estimate
and all of them are most certainly wrong. Yet, there are times when the prediction provides insight or
inf ormation that permits critical improvements, and other times it is just a check box. As reliability prof essionals
we should work to enable decisions with the appropriate tools and analysis. We do this by matching the
approach to the task and the task’s importance. We disclose assumptions, limitations, accuracy, and options.
We enable decision makers to understand the validity of our work and the lack of a crystal ball.
How do you see the f uture? Any stories about predictions you’d like to share?