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

AI and the 6-Steps Part 2 Abstract Carl and Fred  continue to discuss the use of AI in achieving high reliability. This podcast episode  continues the six-step reliability engineering process, focusing primarily on Step 4 (methods), Step 5 (planning), and Step 6 (execution). Key Points Join Carl and Fred as they discuss how AI can […]

AI and the 6-Steps Part 1 Abstract Carl and Fred explore how AI can be integrated into the six-step reliability engineering process, emphasizing that AI should be treated as a powerful tool that assists humans rather than replacing human judgment, experience, and critical thinking. This is part one of two parts. Key Points Join Carl […]

Material selection can determine whether a component performs reliably throughout its intended life. Mojan and Enrico compare experiences from consumer electronics, automotive systems, robotics, and plastics engineering to explore the relationship between materials and reliability. They discuss failure analysis, damage mechanisms, engineering requirements, supplier influence, and the practical challenges of introducing alternative materials into production. The episode highlights why engineers should treat material selection as an early Design for Reliability decision rather than simply continuing with materials used in previous product generations.

More data should help engineers make better decisions. But what happens when every dashboard is flashing red? Chris and Mojan explore alert fatigue as a reliability problem that extends far beyond software. From on-call engineers overwhelmed by incidents to maintenance teams living with condition-monitoring warnings months before a bearing might fail, the psychology is remarkably similar: warnings eventually become background noise. They discuss why constant firefighting prevents root causes from being addressed, why an RCM maintenance strategy should never become a laminated set of rules, and why better reliability engineering means delivering the right information to the right person at...

How reliable does a surgical robot need to be? Chris and Mojan use that question to explore a much broader reliability engineering problem. Patients care about safe outcomes and what happens when the robot fails; hospitals also care about availability, utilization, and downtime. The conversation then moves upstream to where many expensive failures actually begin: poorly defined hardware-software interfaces and requirements delivered too late. They discuss why cutting early reliability work often makes development slower, not faster, and how AI-enhanced FMEA can expose difficult failure scenarios early enough for engineers to prevent them rather than discover them during validation.

Measuring reliability should help engineers make better decisions—not simply generate an impressive MTBF or another test report. Chris and Fred explore how misleading metrics, late reliability demonstrations, and ever-expanding test programs can create the appearance of reliability engineering without actually making products more reliable. One organization accumulated 47 tests simply by adding another whenever a field problem occurred. The result was not better engineering, but designers learning to design for the tests. The episode argues for something more useful: measure what helps engineers prevent failures, make better design decisions, reduce development cost, and get reliable products to market faster.

Is it Reliable? Abstract When a customer asks “Is it reliable?” they’re not asking about MTBF or lab test results. They’re asking: “Will this do what I bought it for?” Join Fred and Mojan as they explore why product management is the most underrated function in reliability engineering. From HP’s obsessive attention to printer sound […]

Measuring Reliability Abstract “It’s impossible to measure.” “It’s too vague.” If you’ve heard these objections, you’re facing a fundamental misunderstanding about measurement. Join Fred and Mojan as they explore Douglas Hubbard’s principle that everything can be measured—the real question is whether the cost of measurement is worth the reduction in uncertainty. They discuss how to […]

Reliability-Centered Maintenance should do more than produce a list of preventive maintenance tasks. Chris and Carl examine how maintenance and supportability FMEAs feed RCM decision logic, and why maintenance thinking must begin before design is locked down. They challenge two particularly costly habits: treating initial maintenance intervals as permanent answers and assuming that more preventive maintenance always improves reliability. Without good failure data, servicing intervals may begin as educated guesses—and unnecessary intervention can itself cause failures. Effective RCM therefore needs feedback from field data, failure analysis, maintainability, and design to continually improve the maintenance strategy.

Is reliability engineering becoming less attractive as companies cut costs, demand faster development, and turn increasingly to AI? This episode argues that the opposite may be true—but only for engineers willing to move beyond comfortable tools and established routines. Chris and Enrico examine why cutting reliability capability can actually increase cost and delay development, why late-stage testing cannot substitute for early prevention, and why technical expertise must translate into business value. They also tackle AI: not as a replacement for reliability knowledge, but as a powerful tool for engineers who already know enough to question, challenge, and improve its ou...

Return Logistics and Failures Abstract Kirk and Fred discuss the challenge of getting field failures back to the engineers who can do detailed failure analysis. Key Points Join Kirk and Fred as they discuss how difficult it is to get failed products back and how many returns aren’t actually failures but still end up in […]

Weibull is Useful Abstract Kirk and Fred discuss the long-running question of Weibull analysis and whether it’s useful. Key Points Join Kirk and Fred as they discuss why Weibull is useful after failures occur in the field. Topics include: Weibull plots are used to determine whether a failure is an infant mortality failure, a random […]

GM knew its ignition switch failed an internal torque specification before the affected vehicles entered production. Yet the switch was approved, field problems followed, and an improvement costing less than $1 per vehicle was later rejected. Eventually, approximately 2.6 million vehicles were recalled, with enormous financial and human consequences. Chris uses the GM ignition switch failure to make a broader reliability engineering point: finding problems is not enough. Engineers and leaders must act while changes are still inexpensive. Earlier changes the economics—and waiting until testing or customers expose a failure can turn a simple engineering decision into an extraordinarily expensive one.

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...

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...

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...

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, […]

AI Techniques and Projects Abstract Dianna and Chris discuss AI techniques and projects they’ve been working on. Key Points Join Dianna and Chris as they discuss why AI in reliability should be treated as a powerful enhancer for pre-existing knowledge rather than a shortcut for the engineering basics. Topics include: AI-assisted FMEA approach that transitions […]

DFR and Reliability Engineers Abstract Enrico and Fred discuss the role of reliability engineers and implementing design for reliability. Key Points Join Enrico and Fred as they discuss how reliability professionals can assist, not do, design for reliability. Topics include: The various tools involved with a good design for reliability program. The problem with a […]

What makes Accendo Reliability valuable? In this episode, Chris and Fred explore how a free, practitioner-driven platform helps engineers solve real-world reliability problems every day. The discussion goes beyond website traffic and content metrics to examine the true value of shared knowledge—helping engineers learn faster, make better decisions, and avoid costly mistakes. They also discuss why practical experience often carries more weight than academic theory alone, and how contributors benefit by building trust and credibility within the industry. If you've ever relied on an article, podcast, webinar, or case study to solve a problem, this episode highlights why knowledge sh...

Physics-of-failure models are often presented as the ultimate solution for predicting reliability—but are they always necessary? In this episode, Chris and Fred explore a listener's question about finding reliable references for physics-of-failure models, particularly for electronics and PCB assemblies. The discussion highlights the spectrum of available models, from simple empirical equations to highly detailed simulations requiring extensive material and manufacturing data. More importantly, they explain why engineers should start by defining the decision they need to make before searching for increasingly sophisticated models. If you're trying to improve reliability, select materials, design accelerated tests, or understand failure mechanisms, this ep...

FMEA Compensating Provisions Abstract Carl and Fred discuss the concept of compensating provisions in FMEA—design features or operator actions that mitigate the consequences of failures and improve safety. Key Points Join Carl and Fred as they discuss how to identify and evaluate Compensating Provisions in FMEAs. Topics include: Compensating provisions is an FMEA element present in military […]

FMEA Scales Abstract Carl and Fred discuss whether FMEA teams should use the 1–10 Severity, Occurrence, and Detection (SOD) scales in some FMEA Standards, or whether they can tailor the scales to better fit their applications. Key Points Join Carl and Fred as they discuss the essence of FMEA scales and how to apply them […]

AI Names ‘Best’ Reliability Engineers Abstract Kirk and Fred discuss a recent AI search topic: who is the best reliability engineer? Key Points Join Kirk and Fred as they discuss how AI tends to provide answers with some bias towards the person inquiring and maybe a little bit of confirmation bias. Topics include: Why do Fred […]

Electronics in Today’s Vehicles Abstract Kirk and Fred discussed a recent search topic on electronics reliability when we found an article on the durability of automotive electronics. Key Points Join Kirk and Fred as they discuss automotive reliability and how many failure causes stem from variations in the long vertical supply chain in manufacturing. Topics […]

They Are Just Not Listening Abstract “They’re just not listening.” But what if the problem isn’t their ears—it’s their incentives? Join Fred and Mojan as they explore why people resist change, what actually motivates behavior, and how to drive change when you don’t have formal authority. Key Points Join Fred and Mojan as they explore […]

MTBF to Weibull? Abstract Enrico and Fred discuss a listener’s question about the process to convert a MTBF value to a Weibull curve. Key Points Join Enrico and Fred as they discuss a few questions concerning a proposed method to derive a Weibull curve from a MTBF value. Topics include: The available data Making assumptions […]

Best Lessons Learned Method Abstract Capturing lessons learned can be a monumental waste of time—or it can be transformative. Join Fred and Mojan as they explore what separates the two through a cautionary tale: when three engineering teams independently removed electrostatic discharge (ESD) protection from a product to save costs, it nearly bankrupted the division. […]

Damage Modeling Abstract What exactly is “damage” in engineering, and how do you predict when it will end a product’s life? Join Mojan and Fred as they explore damage modeling through the lens of reliability engineering. They discuss why understanding damage accumulation is critical for both designers and field teams, and how monitoring thresholds throughout […]

Weibull and Crow-AMSSA Key Points On the Use of Weibull vs. Crow AMSAA (Crow-AMSAA) for Repairable Systems – Philip Sage introduced the topic after seeing a LinkedIn discussion emphasizing Crow AMSAA over Weibull for repairable systems. – The post initially appeared to strongly advocate against Weibull, but on a closer read, it functioned as an […]

Complex Product Reliability Abstract Enrico and Fred discuss the difference between complex and complicated products and what it means for reliability engineering. Using examples ranging from rockets and home appliances to computers and fountain pens, they explore the limits of component-level analysis, the role of system testing, and why surprises remain unavoidable even in well-engineered […]

What is Reliability? Abstract What is the definition of reliability? Outside of the field of reliability, who defines it or what a failure is? Join Mojan and Fred as they explore how reliability is perceived across the product development ecosystem—from customers to suppliers to design engineers—and why the classic “but it met the spec!” defense […]

Yes, Culture Can Change Abstract Carl and Fred discuss why organizational culture is not fixed, and how reliability-focused leadership, communication, and sustained engagement can gradually transform the way teams think and operate. Key Points Join Carl and Fred as they discuss how reliability culture can change in a company of organization. Topics include: Organizational culture […]

Why Gaps? Abstract Carl and Fred discuss why effective reliability planning begins with identifying the gaps between an organization’s current capabilities and its long-term reliability vision. Key Points Join Carl and Fred as they discuss why identifying gaps are often missed in reliability planning and how to remedy. Topics include: Reliability planning should begin with […]