Speaking Of Reliability: Friends Discussing Reliability Engineering Topics | Warranty | Plant Maintenance

What does reliability mean when the customer thinks your product has failed even though it still technically works? And how should engineers measure reliability when failures involve software defects or patient safety rather than conventional hardware wear-out? Chris and Enrico continue their examination of reliability metrics by looking at customer ratings, brand reputation, medical devices, and software. They discuss why customer expectations matter, why medical reliability and safety overlap, and why software cannot simply be forced into traditional hardware reliability models. The discussion also highlights a growing systems-engineering problem: reliable products increasingly depend on hardware and software teams working together from the beginning.

Reliability metrics are supposed to help engineers make better decisions—but what happens when the metric itself points you in the wrong direction? Chris and Enrico examine how consumer products, electronics, automotive applications, and defense measure reliability, from warranty claims and FIT rates to MTBF. They challenge assumptions about infant mortality, extremely long predicted component lives, and the usefulness of reducing complex failure behaviour to a single number. The episode makes the case that reliability engineering should start with the business or engineering problem and then select the appropriate metric—not simply inherit whatever metric an industry has traditionally used.

Can generative AI become a useful coach for engineers preparing presentations, podcasts, or other professional communication? Carl and Enrico discuss the role feedback has played throughout their careers and compare traditional human feedback with the increasingly accessible feedback offered by AI. They examine why criticism needs to be accurate and actionable, whether direct negative feedback can sometimes motivate greater improvement, and why professionals should continue asking others for candid opinions regardless of experience. The discussion also considers the limitations of AI feedback: without understanding the purpose and context of a communication, AI may optimize for the wrong objective. Human listeners can also recognize qualities such as passion, credibility, and inspiration that are difficult to capture through a purely structural assessment.

What happens when a reliability engineer works in an organization where problems are hidden, procedures become checkboxes, or management is reluctant to hear bad news? Carl and Enrico discuss what separates a strong reliability culture from one that merely follows processes. They examine the importance of technical competence, speaking openly about risks, using design reviews to create shared ownership, and understanding what managers and other stakeholders actually care about. The discussion also explores how engineers can influence organizations that are resistant to change. Rather than trying to transform culture through procedures alone, the episode makes the case for combining strong reliability methods with communication, practical solutions, stakeholder awareness, and visible project successes that demonstrate why reliability matters.

Decision Room Carry Problem Abstract Dianna and Mojan discuss the decision room carry problem. Key Points Join Dianna and Mojan as they discuss “decision room carrying,” comparing how decisions are made in one room versus how they are executed by the teams responsible for carrying them out. Topics include: Why decisions made in isolated rooms […]

When to Use Sensors Abstract Not every single piece of data is worth collecting. Join Mojan and Dianna as they explore how to think strategically about sensors and monitoring systems. From health tracking wearables that collect “almost too much data” to manufacturing floors transformed by AI-enabled sensors, they discuss when sensors actually drive better decisions—and when […]

A widespread internet outage affecting services such as Microsoft Teams, Zoom, Reddit and Discord wasn't caused by hackers or a software bug—it was likely triggered by damage to critical fibre optic infrastructure. In this episode, Chris Jackson explores how a single hardware failure exposed hidden dependencies throughout the internet and why redundancy is often far more fragile than engineers assume. Through real-world examples from aviation, automotive systems, nuclear power and Netflix's famous Chaos Monkey program, Chris explains how common-cause failures defeat backup systems and why understanding these hidden connections is essential for building truly reliable products and infrastructure.

FMEA & AI: What is the Human Role? Abstract Carl and Dianna discuss the emerging role of artificial intelligence in FMEA, emphasizing that AI should enhance—not replace—the human expertise required for effective risk analysis. Key Points Join Carl and Dianna as they share their experiences with AI support for engineering analyses like FMEA. They conclude […]

How to Select FMEA Abstract Dianna and Carl discuss how to select FMEA. Key Points Join Dianna and Carl as they discuss how to select FMEA strategically, focusing on how to move beyond random or checkbox approaches to provide real engineering value. Topics include: The philosophy of doing fewer FMEAs really well rather than many […]

The Evolving Nature of FMEA Abstract Carl and Fred discuss how the fundamentals of FMEA have remained constant over the decades while the methods, tools, and applications have evolved to better support product development in today’s fast-paced engineering environment Key Points Join Carl and Fred emphasize that successful FMEA depends on critical thinking, knowledgeable teams, […]