Your Reliability Engineering Professional Development Site
Hopefully, you have already seen that utilizing R for engineering can unlock a host of powerful tools and techniques and give you the power of data visualization.
In using R, you will likely work with various objects (data files, scripts, analyses, reports etc.). As with other things, creating structure for how you accomplish things in R can be important for both effectiveness and efficiency.
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If there is one graphical technique that deserves a lot more attention that it gets and that every engineer needs to utilize in their day to day, my vote would definitely go for the mighty Youden Plot.
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Today we look at how to draw simple flowcharts in R.
I think I am not far off when I say that flowcharts are an essential tool in the engineering toolbox. They provide a visual way to describe a set of activities, or a a process if you will. This can range from listing sequential steps in a manufacturing process to laying out a project plan to describing a decision making process (think decision trees).
“If you can’t describe what you are doing as a process, you don’t know what you’re doing.”
– W. Edwards Deming
It comes as no surprise that engineers love to use flowcharts to describe or document stuff. If you’re like me, you’ve probably used various Microsoft Office applications to draw flowcharts. I, for example, have mastered creating flowcharts in PowerPoint over the years. Some prefer Visio or maybe some other application (Figma, Miro and the like).
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Today we look at a couple different ways to visualize the distribution of your data.
Understanding the distribution of your data can be useful for engineers undertaking various tasks. The fact of the matter is that there are many different ways in which one can get an idea of the distribution of the data they’re interested in, one of which is density curves.
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This is the sixth edition of the R for Engineering newsletter, and today we look at the ultimate prioritization tool – Pareto charts!
Pareto charts are a core tool for anyone who makes decisions, whether it is selecting a project or problem to solve, combing through last year’s spend or deciding on what equipment to purchase this year. The list goes on; bottom line is that Pareto charts simply allow you to focus on what’s important and cut through what may be interesting but unimportant.
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In the last edition of R for Engineering, we learned how to draw small multiple plots in R and harness the power of comparison. We went from a busy graph to being able to use ggplot’s faceting functions to create a small multiples plot. If you need a recap, here’s a link to the last edition.
That is to say, nature’s laws are causal; they reveal themselves by comparison and difference, and they operate at every multi-variate space-time point.
– Edward Tufte
Small multiples have many uses in engineering, but the one I personally use them the most for is characterization and diagnosis. In my line of work, which is quality engineering, the ability to diagnose problems in physical systems (both product and machine/process-related) is a critical skill, and I will go as far as to say that diagnosing problems is a critical skill in any engineering discipline.
[Read more…]In this week’s edition, I introduce you to the concept of small multiples, and, more importantly, how to make them in R. This is one of those really low effort-super high return kind of features of R that can make you look like a rock star of data visualization. So, without further ado, let’s jump right into it!
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In this week’s edition, we dig into a scenario you’ve probably run across when working in Excel or other software, for example Minitab —at least I have, many times.
Say you have a complete dataset. The data has been collected, and you’re now getting ready to run plot it or run some sort of analysis on it. It should be plug and play, but it ends up not being the case as the data is not formatted in the right way, and you’re not able to run your analysis (it happens pretty frequently if you ask me).
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If this is your first time reading my newsletter: I am thrilled that you decided to give it try!
If this is not your first time: I’m glad you’re still here!
We’ve got a few things to go through in this week’s edition.
However, before we get into the cool stuff, that is showcasing useful functionality and interesting use cases, I feel it would behoove me to lay down some of the foundational things you’ll need to do to get you started in R, should you be interested.
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It all started two years ago. I had been in engineering, mostly in quality engineering, all my career, and at that point decided I would try and expand my analytical capabilities as an engineer. Not that I didn’t already have tools at my fingertips; I would use Excel, a lot. I was actually pretty good at it, having developed even custom applications with macros and all the bells and whistles. I had Minitab, which most engineers in my line of work also use. If it’s not Mintiab, then it is JMP or one of those statistical applications. They’re all fine.
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In this episode I got to interview the amazing Fred Schenkelberg, who is a reliability expert at FMS Reliability and the owner of the super popular reliability and quality engineering professional development online platform, Accendo Reliability (make sure to check it out if you haven’t already!).
The episode centers on using and making sense of sensors in manufacturing. The application of sensors in manufacturing has become quite widespread. The goal is to collect large amounts of data from manufacturing equipment with the goal for those data to become information, knowledge and aid decision making. Potential areas are equipment maintenance/reliability, product performance and reliability, quality, efficiency, traceability, regulatory etc.
Fred shares with us his experience and wisdom on:
– Misapplication of sensors in manufacturing
– How experimentation prior to setting up a sensor is critical for understanding what you need to (and more importantly, what you don’t need to) collect data on
– Merely looking at sensor data doesn’t replace hands-on, in-person interaction with manufacturing equipment
– Strategies for deciding on what is important to collect data on
In this episode, I have Dianna Deeney join us, with whom I unpack in detail the question: “What is the future of quality?”. Dianna recently posted an online article with the same title, and since it really resonated with me, I thought she and I could talk through her excellent findings on the show.
Dianna is a mechanical, quality, reliability engineer, consultant and fellow podcaster. She advises engineers in product development. Her work includes consulting and training on how quality and reliability engineering methods can be integrated early into the design process. She believes that the chance of design success is improved through increased collaboration with cross-functional teams, data-based decisions, and application of risk-based analyses – all of which she has seen to be capable through quality thinking. She consults under Deeney Enterprises and is founder of Quality during Design (https://www.qualityduringdesign.com), an online platform that shares resources, practical advice, and problem-solving techniques to strategically apply quality and reliability engineering methods to product design.
Episode highlights:
– Trends that have shaped the voice of the customer in recent years
– Thoughts on the use big data in quality; challenges and opportunities
– Recent trends in product development and innovation
– How does the quality function best fit within the organization?
– Dianna’s practical advice as to how quality professionals can navigate the ever-changing landscape and essentially the future of how we do business
In today’s episode, my guest Riley King and I explore the application of Bayesian statistical methods in the medical device industry. Riley is a thought leader in medical device design and development and has worked with class III medical devices for over 14 years.
Riley shares with us
· His experience in medical device R&D and working in a quality leadership position in medical device
· The use of conventional, frequentist statistical methods in medical device development and some of the challenges he has faced when trying to apply conventional statistical methods
· His explanation of Bayesian statistics, the difference between frequentist and Bayesian methods, and why use Bayesian methods
· Real-life medical device use cases where Bayesian methods can be useful
· His tools of choice, which includes the programming language R and specific packages for Bayesian methods
· His learning journey and book/resource recommendations
· Tips for those in a similar line of work interested in learning and applying these techniques
· Trends he believes will shape the future of medical device technology and development
Riley has a blog called [R]eliability – A Random Walk in the Medical Device Space (https://rileyking.netlify.app/) where he talks in detail about his experiences. Make sure to check it out; it is awesome!
My guest on this episode is Tim Sheehan.
Tim is an extremely well-rounded quality engineer with significant experience in many facets of quality. More importantly, he has recently learned a set of skills that really makes him stand out from his peers and capable of designing, developing and delivering complete data solutions at his company.
With digital technologies gaining ground in the manufacturing floor, and pretty much everywhere these days, us in quality have this ever-growing need to become more data literate and even pick up advanced data skills, such as visualization, programming skills (e.g. SQL and R), setting up databases and data pipelines. And that’s exactly what Tim did; he took the initiative and picked up these skills and ended up delivering a much-needed custom data solution at his company essentially closing the gap between IT and the quality function.
In this episode we cover in detail:
– The details of and learnings form the data project Tim has been working on
– The difference between purchasing an off-the-shelf data solution from a vendor and being able to develop one in-house
– How the digital revolution is not necessarily just about data; it is still very much about people, for people.
– What a great data analytics solution looks like
– Why having the technical skills to develop a data solution AND the domain knowledge to know what to build may be the best of both worlds
– Tim also shares his advice with quality folks wanting to become more data literate and gain tech skills so they themselves can design and deliver data solutions at their companies
2021 posed many challenges but also presented opportunities to many of us. There is a lot of talk of 2021 being the year of the great resignation, and while this episode is not necessarily about that, I think we can definitely say that folks now have more choices as to their careers, whether it is changing jobs, starting a business or branching out and learning something new. Regardless of what your goals are, today’s episode has valuable advice in store for you, delivered by my guest by Anthony Carra, with whom I unpack the topic of Developing the Mindset of a Consultant (Regardless of Whether You Are One). Anthony is the Owner, Principal Consultant and Instructor at Business Improvement Group Consulting, and he is ready to share with us extremely valuable advice on how to develop and apply the mindset of a consultant, whether you are starting your own consulting business or trying to deliver value to your organization.
In this episode we cover:
– Whether you are a consultant or employed full-time, treat it like it is your own business
– Why consultants sometimes get a “bad rap”
– You can either be and stay an SME in a particular thing or you need to “give away your SME” so you can move on to doing other things
– Anthony’s top 3 pieces of advice for developing the mindset of a consultant