Episode 5: Are Data Analysis and Coding Skills Essential? with David Langer

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!

We’re back with a brand new episode that is also the very first episode of 2022. 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

In Part 2 of Episode 7, my recurring guest, the phenomenal Keith Fong and I pick up our chat where we left off in Part 1 and go into detail on how to successfully apply the active learning model in your day-to-day. We also go over an couple examples of successful active learning program implementation.

In Part I of Episode 7, my recurring guest, the phenomenal Keith Fong and I take you to a chat that will be worth your time! Learning is an essential activity for any individual or organization; it is through learning that you develop skills you don't currently possess or develop a higher level of competency in a given skill. Yet, one wonders why conventional training methods organizations typically utilize (classroom-type, lecture-type or through consultants) don't deliver on their promise of making the participants competent in the subject matter. Episode summary: Training in organizations Why is the lecture-type (a.k.a. industrial) approach to education and training not effective and is really "the easy way out"?  What is the active learning model? Leadership's reponsibility; everyone is a leader. "If the student doesn't learn, it is the teacher's fault" An example of a successful active learning program implementation (featured in Part 2 of the episode)

After a short hiatus, the podcast is back with a brand new episode! In this episode Derek Scott, Director at Paragon Process Improvements, joins us to share his thoughts on where he sees quality as a discipline develop amid current technological changes, what will become increasingly important and who might be the leaders of the future.

In this episode, I am joined by the mighty David Langer, who is the founder of Dave on Data, an education company focused on teaching the world the 20% of analytics that drive 80% of ROI. Having worked for companies such as Microsoft, Data Science Dojo and Schedulicity, Dave is considered a veteran in the data science and teaching space; what really makes him stand out from others is his unique and very effective approach to teaching. Get ready as Dave and I take you on an informative and fun chat about essential data analysis skills, R programming, Process Behavior Charts in non-manufacturing environments and other pieces of his great wisdom.

In this episode, John Allen of the New Science of Fixing Things and I talk about a powerful deterministic approach to diagnosing and solving tough problems to improve product quality, reliability and performance. John, who is also one of the original founders of Shainin, LLC, walks us through the approach that he and his group have developed, thanks to which teams are able to solve tough problems in very little time.