Jobs at Apple has done it. You can, too.

Change an industry. The advent of iTunes and iPods forever changed how the world buys and listens to music.

While Jobs had the resources of Apple to help make the change happen. It still started as an idea (may or may not have been Jobs’ idea, I don’t know). It grew and created enough momentum to effect a change across an entire industry.

Change is hard.

If you have tried to help your team move in a new direction or consider the reliability risks present in the current design, then you know change is difficult to make happen. You most likely have been successful a few times, and not a few also. I know I’ve crashed into the rocky spit more often than I can count. [Read more…]

The Variety of Statistical Tools to Support Your Decision Making

My wife and I moved to a new home last year. We have yet to organize our tools.

The bedroom and kitchen are now organized. We, for the most part, can find the sweater or pan that we’re seeking.

No so for our tools in the shop. We have an assortment of hand tools for painting, home maintenance, yard work, and woodworking. In our previous home, we had the tools on pegboards, on shelves, in cabinets. We could find the right tool for the job at hand quickly. We’ve avoided the tool aisle at the hardware store recently, as we were sure we had the tool we need in the jumbled mess in our garage already. Still haven’t found it, though.

Have you noticed the number of statistical tools available? It’s like visiting a well-stocked tool store. There are basic tools like trend charting and advanced tools like proportional hazard models. Let’s explore the available tools a little so you can quickly find the right tool for the question or problem you are facing today.

We are well aware of cases when Accelerated Testing is successfully creating by statisticians, here I would like to propose an engineer’s approach.

We have created a new product, there is (is) an order for it (50 products at a price of $200). Can we start manufacturing and delivery? No, we can’t. We are stopped by significant warranty obligations and lack of confidence in the reliability of the product during the warranty period.

This is a typical task with uncertainty.

Two people have shaped how I guess an answer.

Their comments and guidance have tailored how to form a quick estimate, my ability to articulate a hunch and the effectiveness of those guesses.

You probably guess or make a rough estimate regularly. How good is your gut feel? Do you keep track and score yourself?

Making an estimate should be second nature for you. It’s not something to do in public, too often. The practice can aid you in numerous ways. [Read more…]

Do make compromises around gathering and analyzing data since you only need to report MTBF?

Do you use MTBF (exponential distribution) based test planning when you know the product has a non-constant hazard rate?

These questions came up this week via email looking for advice when directed to ignore the actual situation and just do what the customer wants.

I’m traveling this week, rather jet-lagged today, so going to keep this one short.

How would you answer these questions? What advice would you give someone using exponential based reporting, test planning, or data analysis approaches knowing the customer expects that process yet the data and your experience suggest you should use another method  (Weibull or MCF, for example)?

Please add you comments below and let’s prepare a list of what one should say or use to respond to such actions.

By it’s cover no doubt. The title and cover are important, this is true. When you judge a reliability book we often first see and evaluate the cover.

The author? Do you buy the book based on who wrote or edited it?

Do you have a quick scan or check for key features before you add the book to your library? I’m curious how you select a book to use a reference for your work. The books we read and use for work shape our work, thus it’s important to have the right works at our disposal. [Read more…]

“Do not improve reliability as it cuts into our repair activity profits.” Is this a way to run a reliability program?

I’ve seen this in action and that company is no longer in business. In another situation the field service department withheld vital information to improve products lest his department (and self-importance) dwindle.

Is this a bad business model, or is it just my thinking it not so smart? [Read more…]

Accelerated Testing: Statistical Models, Test Plans, and Data Analyses by Wayne Nelson

Published by John Wiley & Sons in 1990 this 601 page book started my career in reliability engineering.

I didn’t know it at the time in the early ‘90s, yet my assigned task to create an accelerated test for a new product would spark an interest in cheating time. Wayne’s book helped make that first accelerated test successful.

The Accelerated Testing book is a compendium of different ways to conduct accelerate tests with a focus on the planning the test and analyzing the data. Wayne is a reliability statistician, and as he will tell you, not an engineer. Thus the book tends to focus on the math.

What I enjoy about the book is the math is not the dry academic derivation driven material, it is full of examples and immediately useful formulas. There is just enough explanation to help the math wonks pursue their interest, and enough practical information to allow engineers to develop and conduct meaningful experiments. [Read more…]

Neither includes using MTBF, btw.

And, I’m not thinking about the common language definition either.

Plus, I may have this all wrong. Here is the way I think about the reliability of something. More than ‘it should just work’ and different than ‘one can count on it to start’. When I ask someone how reliable a product is, this is what I mean.

By explaining my basic understanding we can compare notes. It is possible, quite possible, that I will learn something. As you may as well. Let’s see. [Read more…]

The Damage Done by Drenick’s Theorem

Have you ever wondered by we use the assumption of a constant failure rate? Or considered why we assume our system is ‘in the flat part of the curve [bathtub curve]’?

Where did this silliness first arise?

In part, I lay blame on Mil Hdbk 217 and parts count prediction practices. Yet, there is a theoretical support for the notion that for large, complex systems the overall system time to failure will approach an exponential distribution.

Thanks go to Wally Tubell Jr., a professor of systems engineering and test. He recently sent me his analysis of Drenick’s theorem and it’s connection to the notion of a flat section of a bathtub curve.

Wally did a little research and found the theorem lacking for practical use. I agree and will explain below. [Read more…]

3 MTBF Stories

Everyone loves a great story. Storytelling has been a long tradition to pass along knowledge and wisdom.

There are good stories, tales of inspiration. There are sad stories, tales of caution.

There are fables, ghost stores, legends, epic poems, and more. When considering the reliability performance of your product or equipment, you probably have a few stories that you can tell. “That time … “

Simple join colleagues for lunch and ask about the ‘major disasters’ of the past. The stories help us to remember and hopefully avoid repeating mistakes.

Here are three stories with MTBF as a central figure. It is a site and blog that does take about MTBF, so it fits. To start, let me introduce you to Martin, a new reliability engineering reporting to his first day of work at a bicycle design and manufacturing company. Two sad stories and a good one. enjoy. [Read more…]

Different Data Same Decision

Let say you have some time to failure data on your equipment. A common action is to calculate the MTBF. All well and good until you expect to make a meaningful decision based on the calculation.

Using just the mean of the data, the MTBF value is likely to provide you with a less-than-useful bit of information. Thus, your decision will be rather random or worthless.

Let’s explore just how this simple calculation of perfectly good data can mislead your decision-making. [Read more…]