Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
An Interview with James Blackwell, CTO and Co-founder of Roost Inc.
James Blackwell is a wireless industry veteran with over 25 years of leadership experience in the design and manufacturing of Bluetooth and Wi-Fi systems, including CSR and Ozmo Devices.
“Always remember, your focus determines your reality” – George Lucas
In this article, I will outline how to evaluate an FMEA against the FMEA Quality Objective for identifying special characteristics. Special characteristics are often not well understood by FMEA teams, and yet have potential to be highly useful in FMEA applications.
Dear friends, we are happy to release our 90th technical video! In this video, Hemant Urdhwareshe, Fellow of American Society for Quality (ASQ), explains the various functions in reliability mathematics. Hemant has explained Probability Density Function (PDF), Cumulative Failure Distribution Function (CDF), Reliability Function and the Hazard Function. Hemant has also briefly explained the mathematical derivations of these functions as well as their relationships. In addition, the video includes an application example of estimating B02 life when the hazard function is known. The video is little long, so viewers are requested to be patient and watch the complete video. This will be very useful in preparing for the Certified Reliability Engineer Exam conducted by ASQ.
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Many companies, especially for high-complexity products such as energy storage and robotics, are moving away from outsourced manufacturing to establish their own internal operations. In fact, Kearney’s 10th Annual Reshoring Index finds that reshoring has substantially increased during the past decade.
This paradigm shift, driven by the need for closer design and production integration, poses opportunities and challenges. This article delves into the essential steps and considerations to set up an effective internal manufacturing facility tailored for the hardware-based product industry.
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Dear friends, we are happy to release our 89th technical video. In this video, Hemant Urdhwareshe explains the basic idea behind Goodness of fit testes and how to perform Goodness of Fit test. Hemant illustrates this with an illustration of Goodness of Fit test for Normal Distribution using Microsoft Excel. Hemant is a Fellow of ASQ and is certified by ASQ as Six Sigma Master Black Belt, Quality Engineer (CQE), Quality Manager (CMQ/OE) and Reliability Engineer (CRE).
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In the fast-paced world of technology and manufacturing, one of the biggest challenges hardware companies face is managing New Product Introduction (NPI) processes. A common misconception is that simply applying an Agile template used in software production to hardware production will solve all problems, but without thoughtful consideration, it’s a recipe for disaster. Whether it’s waiting weeks for a crucial part, revising designs after production has started, or dealing with field failures, these common pitfalls can lead to big delays, increased costs, and, ultimately, unhappy customers. This blog will dive into some of these common challenges, offering insight and strategies to avoid getting caught in a never-ending cycle of fixing and delaying.
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Dear friends, we are pleased to released our 88th technical video on a very important topic in Statistical Quality Control and Six Sigma: Normal Probability Plotting! In this video, Hemant Urdhwareshe explains the concepts and basic procedure of Normal Probability Plotting using probability paper with a simple real life case study! Hemant is a Fellow of ASQ and is certified by ASQ as Six Sigma Master Black Belt (CMBB), Black Belt (CSSBB), Quality Manager (CMQ/OE), Quality Engineer (CQE) and Reliability Engineer (CRE)!
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Imagine product or part lifetime as the service time in a self-service system. Suppose you could observe periodic input and output counts of that self-service system, without identifying individual service times. How would you estimate the service time distribution without following individual products or parts from input to output? The maximum likelihood reliability estimator for an M/G/Infinity self-service-time distribution function from “ships” (inputs) and “returns” (outputs) counts also works for nonstationary arrival process M(t)/G/Infinity self-service systems, under a condition! A constant or increasing arrival (inputs or ships) rate satisfies the condition. If you identify returns by failure mode then you could estimate make nonparametric estimates of field reliability by failure mode and quantify reliability growth, without life data, if the condition is satisfied.
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In the world of hardware new product introduction, a broken process can have severe consequences, jeopardizing the success of a product launch and hurting the reputation of a company. So, it is crucial to understand the warning signs that a process is disfunctional. Whether it’s slipping schedules, exceeding budgets, or missing requirements, recognizing the signs allows teams to intervene in time.
Learn the ten warning signs and understand the potential impacts they can have on your product launch. By familiarizing yourself with these signs, you can take proactive measures to address them before they escalate into more significant problems.
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Dear friends, we are happy to release our 87th video to provide brief guidance on selection of parts, processes and materials for assuring reliability! Hope you find this interesting! You can see our other related video on Miner’s Rule for Fatigue Life.
[Read more…]In an increasingly competitive market, the need to bring superior products to market swiftly, cost-effectively, and with minimal risk is paramount. This article presents a summary of the top ten best practices for managing risk in hardware development.
Read on to gain insights into navigating the maze of financial, operational, and market challenges that accompany hardware development. Based upon over 100 years of collective experience with hardware development, the Product Realization Group has distilled these insights from real-world experience. Learn how to reduce risk in your New Product Development and Introduction (NPDI) process and bring better products to market faster, at lower cost, and with less risk.
[Read more…]“Risk comes from not knowing what you’re doing” – Warren Buffett
In this article, I will outline how to evaluate an FMEA against the FMEA Quality Objective for identifying high-risk failures.
One definition for “risk” is “the possibility of loss or injury;” and “high” means “of a greater degree, amount or cost than expected.” Putting these words together, “high risk” means the anticipated loss or injury is too great. [Read more…]
Dear friends, I am happy to release this video on Introduction to Robust Design. In this video, I have briefly explained the philosophy of robust design which was originally created by Dr.Genichi Taguchi. It includes Taguchi’s definition of quality, the quality loss function, Signal to Noise Ratios (SN Ratio), Parameter Diagram, Steps in Robust Design, and an illustration of calculation of SN ratio.
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In today’s global economy, supply chain disruptions are inevitable. Companies must proactively plan for resilience and adaptability, whether due to geopolitical tensions, tariffs, component shortages, or factory challenges.
Product Realization Group’s veteran hardware and operations experts Michael Keer, Founder and Managing Partner, and Wayne Miller, New Product Development and Introduction, shared their insights on mitigating supply chain risk, drawing from decades of experience in manufacturing, operations, and supplier management. Below, we outline seven key strategies to safeguard your supply chain and keep your production on track.
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I learned actuarial methods for forecasting and spares planning while working for the US Air Force Logistics Command in the 1970s [AFLCM 66-17 and AFM 400-1]. I am grateful for the education, and I am sorry to report that the USAF has reverted to MTBF management.
The US AFLC actuarial methods were developed for engine management in the 1960s by RAND Corp. [Giesler] They estimated age-interval failure rates and made actuarial forecasts of engine demands depending on the flying-hour program plan. An actuarial forecast is ∑a(s)n(t-s), s=1,2,…,t, where a(s) is actuarial failure rate conditional on survival to age s and n(t-s) is the installed base of age t-s. Periodic meetings consolidated engine lifetime and failure data into agreements on actuarial failure rates, for forecasting engine demands and for war readiness spares requirements.
The USAF actuarial methods assume constant failure rates within short age intervals, Poisson demands, and ignore variance induced by variable flying hours per aircraft in the flying hour program. I later figured out how to estimate actuarial failure rates for all engines, major modules, and their service parts, with or without life-limits and without lifetime data; I computed the distribution of demand forecasts, not Poisson. I offered to show AFIT faculty, AFOSR, AFRL, and RAND how to extend actuarial methods to all service parts [George, 1993].
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