In another country far away, there were power plants that generated cheap electricity by unclear means. Since operation of the plants involved some risk, the Unclear Regulatory Commission was established to license power plants for operations. The URC decreed that any plant could have a license if its probability of an unclear accident was smaller than 0.000001 per year. The plant operators said they were uncertain.

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by Hemant Urdhwareshe Leave a Comment

B10 Life for Weibull and Lognormal Distributions

Dear friends, we are happy to upload this video on how to estimate B10 life when failure data follows Weibull or Lognormal Distribution. Your feedback on the video is welcome!

This video from the Institute of Quality and Reliability explains how to determine BX life for products that follow Weibull and log-normal distributions. BX life is defined as the time by which X percent of items are expected to fail. For example, B10 life means that 10% of items are expected to fail, which implies a 90% reliability.

We recommend watching following videos before watching this video for better learning experience:

Weibull Distribution Part-1

B10 Life for Exponential Distribution

Normal Distribution and Z-Score

Lognormal Distribution

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by Michael Keer Leave a Comment

3: Concept & Feasibility Phase – Part 2: Feasibility

by Mike Freier

The feasibility phase addresses two questions. First, can your design be made into a manufacturable product? And, second, how will you sell your product and to whom?

Validating the feasibility of manufacturing a new product is the next chapter in our series by Mike Freier on how to take a new hardware product from idea to scale.

[Read more…]

by Larry George 1 Comment

Kaplan-Meier Reliability: What Could Possibly Go Wrong?

SAS, JMP, R-”Survival”, Minitab, ReliaSoft, XLStat, and perhaps other statistics programs offer the Kaplan-Meier nonparametric reliability estimator as a default. Take credit for using nonparametric reliability estimation and avoiding unwarranted assumptions. What could go wrong using the Kaplan-Meier estimator?

Cohorts could be non-stationary, random processes!
Failures could be recurrent process counts, not dead-forever!
Lifetime data depends on the censoring process(es); e.g., competing risks!
Greenwood’s variance estimator errs! Covariances are missing!
Alternative estimators could be more efficient than Kaplan-Meier!

Are you using all the information in data available from population data required by GAAP? If you don’t have lifetime data, use periodic failure counts. This article describes an example where the Kaplan-Meier estimator from grouped lifetime data is less efficient than using periodic failure counts, even though you don’t know which cohort they came from!

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by Hemant Urdhwareshe Leave a Comment

B10 Life Exponential Distribution

This video from the Institute of Quality and Reliability explains how to calculate the B10 life (the time by which 10% of items are expected to fail) for an exponential distribution.

We suggest to view our video on Exponential Distribution for better learning experience. Here is the link to the video: Remember this Memoryless Exponential Distribution . Your feedback is welcome!

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by Ray Harkins Leave a Comment

Back to the Basics: Brainstorming for Continuous Improvement

Brainstorming may sound like a casual conversation technique, but when applied properly, it becomes a critical tool for continuous improvement across a range of creative and technical disciplines.
In a structured environment, brainstorming enables organizations to capture a wide range of ideas, perspectives, and solutions — ideas that might never surface in a traditional problem-solving meeting.

Let’s take a step back and revisit the basics of effective brainstorming, especially its role in driving meaningful change.

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by Carl S. Carlson Leave a Comment

Quality Objective 4: Lessons Learned

Every failure is a lesson learned about your strategy. Thomas Edison

In this article, I will discuss how to evaluate an FMEA against the FMEA Quality Objective for Lessons Learned.

By definition, a “lesson” means “something learned by study or experience,” and “learn” means “to gain knowledge or understanding of by study, instruction or experience.”

What are Lessons Learned?

Lessons Learned are specific data from the field (or from manufacturing plant history in the case of Process FMEAs) that documents relevant information about failures, from which knowledge can be gained and used in support of future product programs. [Read more…]

by Michael Keer Leave a Comment

2. CONCEPT & FEASIBILITY PHASE – Part 1: Concept

by Mike Freier

In this series on developing a hardware product from idea to scale, we begin with the essential but often overlooked Concept & Feasibility phase. This blog explains the concept half of the process, which addresses the customer, competition, and costs of your product.

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by Hemant Urdhwareshe Leave a Comment

Miner’s Rule for Cumulative Damage Theory and Fatigue Failures

Dear All, in this video Hemant Urdhwareshe explains the Miner’s Rule for cumulative damage under cyclic loading along with an application example. Your feedback is welcome! Hemant is a Fellow of ASQ, and certified by ASQ as Six Sigma Master Black Belt, CRE, CQE and CMQ/OE.

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by Michael Keer Leave a Comment

How to Get from Idea to Scale

By Mike Freier

Mike Freier, a PRG Partner, authored a series of blogs about how hardware products transition from an idea, through development, manufacturing and volume sales to their eventual end of life. Join us on this journey and learn how to avoid the pitfalls and risks that companies encounter on their way to a successful product launch.

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by Hemant Urdhwareshe Leave a Comment

RSM Part-2 Central Composite Design with Illustrated Application Example

Dear Friends, We are happy to share this second video on Response Surface Design. In this video, Hemant Urdhwareshe, Fellow of ASQ and Six Sigma Master Black Belt explains the structure of Response Surface Design with focus on Central Composite Design (CCD). Hemant has taken lot of efforts to illustrate this little complex concept with lot of graphics and animations so that viewers can understand it clearly and quickly. Hemant has also illustrated how to create, analyse and interpret results of Central Composite Design on Minitab with a virtual catapult.

We recommend that viewers should watch our previous videos:

(1) RSM Part-1

(2) Fractional Factorial Designs and Resolution Code

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by Ray Harkins Leave a Comment

Back to the Basics: Understanding the Deming Cycle (PDCA)

In the world of quality, reliability, product design, and manufacturing, improvement is a necessity, not a luxury. Few models have provided a stronger foundation for improvement than the Deming Cycle, commonly referred to as PDCA: Plan-Do-Check-Act.

Although simple in structure, PDCA represents a deep and disciplined approach to learning, problem-solving, and continuous improvement. Whether you are optimizing a production process, refining a laboratory method, or developing a new product, understanding PDCA is essential.

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by Hemant Urdhwareshe Leave a Comment

Response Surface Designs: Part-1 Addition of Centerpoint

Dear friends, I am posting a video after some gap! This is our first video in our series on Response Surface Designs. Response Surface Designs become necessary when we want to model a second order response in an experiment. In this first video, Hemant Urdhwareshe explains addition of centerpoint to a factorial design to assess presence of non-linearity in the response. The concept is explained with a virtual catapult with a two-factor two-level design.

Links to the related videos:

(1) DOE-1: Introduction to Design of Experiments

(2) DOE-8: Linearity and Orthogonality in Experimental Design

(3) DOE-6: Case Study in Creating Full Factorial Design in Minita b

(4) DOE-7: Analyse Factorial Design with Minitab

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by Hemant Urdhwareshe Leave a Comment

SPC-2: Proportion Defective and Number Defective charts (p and np charts)

Dear friends, we are happy to release this second video on statistical process control charts. In this video, Hemant Urdhwareshe illustrates how to use attributes charts, specifically p-charts for proportion defective and np charts for number defective! Watch our video SPC-1 for Introduction and Selection of Control Charts, and X-bar and R charts.

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by Ray Harkins Leave a Comment

Back to the Basics: Benchmarking for Improvement

Continuous improvement doesn’t happen in a vacuum. To raise the quality, reliability, and performance of their products and processes, organizations must look beyond their own four walls.
Benchmarking — the practice of studying others to improve your own performance — is a foundational tool for competitive advantage.

Whether comparing products, processes, or management systems, effective benchmarking helps organizations discover new ideas, set realistic goals, and accelerate improvement.

Let’s take a step back and explore the basics of benchmarking: what it is, how it works, and why it matters.

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