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Why Use Nonparametric Reliability Statistics?

Why Use Nonparametric Reliability Statistics?

Fred asked me to explain why use nonparametric statistics? The answer is reality. Reality trumps opinion, mathematical convenience, and tradition. Reality is more interesting, but quantifying reality takes work, especially if you track lifetimes. Using field reliability reality provides credibility and could reduce uncertainty due to tradition and unwarranted, unverified assumptions.

Data is inherently nonparametric. Cardinal numbers are used for period counts: cohorts, cases, failures, etc. Accounting data is numerical; it is derived from data or from dollars required by GAAP (Generally Accepted Accounting Principles); e.g., revenue = price*(products sold), service cost = (Cost per service)*(Number of services), or numbers of spare parts sold. Why not do nonparametric reliability estimation, with or without lifetime data?

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The Rising Tide of Young Professionals: Overcoming Intergenerational Communication Gaps in Reliability Engineering

Young professionals (under 40) will make up over half of the workforce by the end of next year. That means communication between young professionals and experienced professionals is essential for workplace success. Younger professionals dominating the workforce means that reliability engineering is undergoing formative changes. These are a few general thoughts on the impacts of the changing workforce and five ways it will impact reliability engineering.

Some General Thoughts on Intergenerational Communication

Here are a few thoughts on intergenerational communication from my recent interview on The YoPro Know Podcast.

On Using Social Media

Companies are generally awkward with social media and how to reach the younger generations. On the one hand, companies know to try. On the other hand, the HR people and some of the technical managers are really bad at it.

Companies should do a gap analysis on reaching young professionals. There are inadvertent things companies do that really cause them to stumble inadvertently. Most companies do too much of a cookie-cutter approach to recruiting and retention.

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Boeing is Steering Harder into its Spiral of Death

Boeing is Steering Harder into its Spiral of Death

Boeing has somehow managed to make the bad public relations created by those pesky onboard batteries catching fire in 2013 practically disappear. Not through good management. But through a never-ending series of disasters and catastrophes that shows no sign of letting up which is dominating Boeing’s news cycle that there is no remaining airtime for missteps like those battery fires.

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The Language We Use Matters

The Language We Use Matters

During RAMS this year, Wayne Nelson made the point that language matters. One specific example was the substitution of ‘convincing’ for ‘statistically significant’ in an effort to clearly convey the ability of a test result to sway the reader. For example ‘the test data clearly demonstrates…’

As reliability professionals let’s say what we mean in a clear and unambiguous manner.

Thus, you may suspect, this topic is related to MTBF.

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ISO 31000 Risk Treatment

ISO 31000 Risk Treatment

Guest Post by Peter Holtmann (first posted on CERM ® RISK INSIGHTS – reposted here with permission)

This article is the twelfth of fourteen parts to our risk management series. The series will be taking a look at the risk management guidelines under the ISO 31000 Standard to help you better understand them and how they relate to your own risk management activities. In doing so, we’ll be walking through the core aspects of the Standard and giving you practical guidance on how to implement it.

In previous articles we’ve looked at the core elements of the risk management framework and the role of leadership and commitment, integration, design, implementation, evaluation and improvement more specifically. We’ve also briefly looked at the risk management process in a general sense, the importance of communication and consultation, how to set your scope, context and criteria, as well as the identification, analysis and evaluation of risks. In this article, we’ll be looking at treating risk. [Read more…]

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Maintenance Backlog Management in a Nutshell

Maintenance Backlog Management in a Nutshell

Guest post by David Finch, MSc, MIEAust, CEng, FSOE, AIMM

Maintenance Work Backlog Management: a Start in Managing Maintenance!  A maintenance manager can commence a maintenance improvement programme by simply creating opportunities for individuals and groups to make high performance contributions. One way to do this is to manage the Maintenance Backlog.

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Statistical Tolerancing using Monte Carlo Simulation

Statistical Tolerancing using Monte Carlo Simulation

One of the weak areas in designing parts is deciding tolerances of various parts. Most engineers are familiar with Arithmetic Tolerance stack up analysis wherein they check impact of maximum and minimum values of various tolerances on assembly of parts. However, this can often result in high manufacturing cost. Thus, it may be more appropriate to analyse tolerances using statistical tolerance stack up approach. This can be performed using Monte Carlo Simulation. In one of the previous videos, I had shown how to predict reliability using Monte Carlo simulation. In this video, I will explain how to perform statistical tolerance stack up analysis using Monte Carlo Simulation. I have used Simular software to demonstrate this with a practical example. Simular is a free software (emailware) which can be downloaded from https://www.simularsoft.com.ar/. However, one can use other software such as Crystal Ball, @Risk etc. for such analysis. Statistical tolerance stack up is usually an integral part of Design for Six Sigma (DFSS) projects. I hope viewers will find this video useful. Feedback is welcome!

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What is the “M” In RAM? – The Concept and Importance of Maintainability

What is the “M” In RAM? – The Concept and Importance of Maintainability

In line with the RAM acronym sequence, we often start and go no further than the “R” in Reliability. In doing so, we forget about the “M”. The question often asked is: “what is the reliability of the system?” But rarely asked is: “what is the maintainability of the same system?” Myself, am guilty of this omission. Hence this article to remind myself and you the reader, of the importance of Maintainability in industry.

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Leadership and the Failure of the Apple Car

Leadership and the Failure of the Apple Car

Apple recently did something that it isn’t in the habit of doing. And that is – admitting failure. After spending $ 10 billion on ‘Project Titan’ whose aim was to produce a ‘really cool car,’ Apple decided to pull the pin and cancel it.

Simplifying Apple’s Project Titan ambitions to something as pithy as creating a ‘really cool car’ might seem a little condescending. The problem for Apple was that unfortunately, this was the practical truth. And that is why it failed.

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Warranty – The Bad Old Days and What Came After

Warranty – The Bad Old Days and What Came After

If I must date myself, I cut my teeth as a product development engineer in the automotive industry in the bad old days when warranty terms were shockingly poor.  Let’s examine this through the lens of my first new car which I purchased in 1984.  Why was my warranty coverage so bad, what drove the significant improvements seen in the industry since that time, and how do we now validate our products to keep it that way?

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P Diagram and Robustness 

P Diagram and Robustness 

When facing yet another field issue with a high price tag, my Chief Technical Officer asked me, “How do we get more predictive so we can identify and prevent these failures from occurring in the future?”  Similarly, I had a friend who was trying to optimize a key customer feature of a future product.  He ran robustness experimentation considering over 40 noise and control factors that the team had brainstormed.  And yet, when field trials started, the device had several failures of unknown cause.  Of the more than 40 factors that they had considered during brainstorming, they missed the noise factor that was triggering these failures.  I’ll turn to you and ask the same question:  How do we get better at predicting future failures and preventing them from occurring?  If we had infinite knowledge, we could see these failures before they occurred.

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Do Not Want Equipment Failures

Do Not Want Equipment Failures

I am a rock climber. Climbing relies on skill, strength, knowledge, luck, and sound gear. Falling is a part of the sport, and with the right gear, the sport is safe. So far, I’ve enjoy no equipment failures.

I do not know, nor want to know, the MTBF (or MTTF) of any of my climbing gear. I’m not even sure this information would be available. And, all the gear I use has a finite chance of failing every time the equipment is in use. Part of my confidence is that the probability of failure is really low.

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