on Tools & Techniques

A listing in reverse chronological order of articles by:

Dennis Craggs — Big Data Analytics series

Perry Parendo — Experimental Design for NPD series

Dev Raheja — Innovative Thinking in Reliability and Durability series

Oleg Ivanov — Inside and Beyond HALT series

Carl Carlson — Inside FMEA series

Steven Wachs — Integral Concepts series

Shane Turcott — Learning from Failures series

Larry George — Progress in Field Reliability? series

Gabor Szabo — R for Engineering series

Matthew Reid — Reliability Engineering Using Python series

Kevin Stewart — Reliability Reflections series

Anne Meixner — Testing 1 2 3 series

Ray Harkins — The Manufacturing Academy series

by Larry George 5 Comments

Multiple-Failure-Mode Reliability Estimation

Multiple-Failure-Mode Reliability Estimation

“It is the policy of my Administration to respond to the coronavirus disease 2019 (COVID-19) pandemic through effective approaches guided by the best available science and data” [Biden Executive order, 2021]. That epidemic inspired the simultaneous nonparametric estimation of survival functions from case to recovery and case to death, without lifetime data (figure 1)!

Why not do the same for multiple-failure-mode data? This article shows nonparametric, multiple-failure-mode, maximum likelihood reliability estimation in a spreadsheet. Data are system first-failure times and the corresponding failure modes that caused the first system failures (table 1). However those data are dependent. I will explain the likelihood function, lnL, and how to find the maximum likelihood reliability estimates for all failure modes simultaneously.

by Hemant Urdhwareshe Leave a Comment

Hypothesis Testing Part-5: Chi-Square Test of One Variance

Hypothesis Testing Part-5: Chi-Square Test of One Variance

Dear friends, we are happy to released this fifth video in our series on Hypothesis Testing! In this video, Hemant Urdhwareshe explains the applicability of Chi-square test of one variance with an illustration! Hemant is Fellow of ASQ and is one of the few Certified Six Sigma Master Black Belts from ASQ having six certifications from ASQ.

by Ray Harkins Leave a Comment

Gage R&R Analysis as a Tool for Understanding Measurement System Variation

Gage R&R Analysis as a Tool for Understanding Measurement System Variation

The term Measurement Systems Analysis refers to a collection of experimental and statistical methods designed to evaluate the error introduced by a measurement system and the resulting usefulness of that system for a particular application.

Measurement systems range from the simplest of gages like steel rulers to the most complex, multi-sensor measurement systems. Yet regardless of their sophistication, all gages are flawed and fail to deliver a perfectly accurate result to their users. This idea is best expressed by an equation fundamental to measurement science,

by Hemant Urdhwareshe Leave a Comment

Hypothesis Testing Part-4: The Paired t-test

Hypothesis Testing Part-4: The Paired t-test

Dear friends, we are happy to release our fourth video in our series on Hypothesis Testing! In this video, Hemant Urdhwareshe explains the application of Student’s t-test for paired t-test using tables of t-distribution and using Microsoft Excel Add-ins. The concept is illustrated with an application example of comparing assessment of answer papers by two professors!

by Larry George 4 Comments

Statistical Software Problem?

Statistical Software Problem?

When a system fails for the first failure in one mode at time t, this data is right censored data for other failure modes! How to estimate reliability functions for all failure modes from first failure data?

Google AI says, “’Competing risks’ refers to a statistical scenario where a subject can experience failure from multiple possible causes, but once one failure occurs, it prevents the observation of any other potential failures, essentially creating “multiple failure modes” that compete with each other to be the first event observed; this means analyzing the probability of a specific failure type needs to account for the possibility of other competing failures happening first.” “Use appropriate statistical methods: Employ statistical models specifically designed for competing risks analysis…”

by Hemant Urdhwareshe Leave a Comment

Hypothesis Testing Part-3: Two Sample t-test with tables and using Excel

Hypothesis Testing Part-3: Two Sample t-test with tables and using Excel

Dear friends, we are happy to release our third video in our series on Hypothesis Testing! In this video, Hemant Urdhwareshe explains the application of Student’s t-test for comparison of two independent samples using tables of t-distribution and also using Microsoft Excel Add-ins. The concept is illustrated with an application example of comparing tire life of two makes.

by Ray Harkins Leave a Comment

Understanding the Six Types of Measurement System Error

Understanding the Six Types of Measurement System Error

Our work as quality and reliability engineers, or as countless other technical positions across every industry, relies heavily on the instrumentation we use. Torque meters, tensile testers, micrometers, spectrometers and coordinate measuring machines provide critical data about the variation within the processes we design and maintain.

But these tools execute measurement processes which, like all processes, introduce variation into the results they generate. This fact – that every gage contributes variation to the values it reports – is the basis for Measurement Systems Analysis (MSA), a collection of statistical tools and approaches designed to isolate and quantify sources of measurement error.

by Hemant Urdhwareshe Leave a Comment

Hypothesis Testing Part-2: One Sample t-test, t-distribution, Degrees of Freedom and P-Value

Hypothesis Testing Part-2: One Sample t-test, t-distribution, Degrees of Freedom and P-Value

Dear friends, we are happy to release this second video on Hypothesis Testing! In this video, Hemant Urdhwareshe explains One Sample t-test along with illustrations of Student’s t-distribution. Hemant has also explained the concept of degrees of freedom and p-value in this video.

We recommend viewers to watch Hypothesis Testing, Part-1 video before this video.

We are sure, you will find this useful!

by Steven Wachs Leave a Comment

Sample Sizes for Hypothesis Testing

Sample Sizes for Hypothesis Testing

As an industrial statistics consultant for the past 25 years, I have frequently fielded questions related to sample size determination. Unfortunately, I have encountered many instances where simple rules of thumb were used for any purpose (like always use 30). Sample size guidance really depends on what the goal of the study is, the type of data we are dealing with, what statistical method we are using and some other factors as well. Common activities which typically require sample size determination include:

Hypothesis Testing (including Equivalence Testing)
Estimation of statistics like means, standard deviations, proportions
Calculation of Tolerance Intervals (range of data a process uses)
Designed Experiments (number of replicates)
Statistical Process Control Charts (e.g. X-bar charts)
Acceptance Sampling (to disposition lots or batches of raw materials or finished products)
Reliability Testing to estimate Reliability performance
Reliability Testing to demonstrate Reliability performance

All these applications require different assumptions and calculations to determine an appropriate sample size. In this article, we focus on Sample Size determination for Hypothesis Testing. It is assumed that the reader is already familiar with Hypothesis Testing.

by Hemant Urdhwareshe Leave a Comment

Hypothesis Testing Part-1: Introduction and One-Sample Z-Test

Hypothesis Testing Part-1: Introduction and One-Sample Z-Test

Dear friends, Our best wishes to all for a great Quality Month and Year Ahead for your Quality Initiatives! In this Quality Month, we are starting a new series of videos on Hypothesis Testing in our Channel! we are happy to release our first video on Hypothesis Testing! We will be releasing a complete series of videos on Hypothesis Tests! In this first video on the subject, Hemant Urdhwareshe explains the basic concepts and discusses an illustration of One-Sample Z-Test!

by Ray Harkins Leave a Comment

Understanding the Difference Between Statistical and Practical Significance

Understanding the Difference Between Statistical and Practical Significance

Data-driven decision-making is central to designing and improving products and processes. Professionals are often presented with statistical analyses, with key outputs such as p-values or confidence intervals that indicate whether results are “statistically significant.” However, statistical significance doesn’t always translate into meaningful changes on the shop floor or within a product’s design. Understanding the difference between statistical significance and practical significance is crucial to making well-informed decisions that genuinely impact the business.

by Steven Wachs Leave a Comment

Stability Studies and Estimating Shelf Life with Regression Models

Stability Studies and Estimating Shelf Life with Regression Models

Stability studies are used to understand and model the degradation of key product characteristics over time. They are often used to determine the product’s shelf life (the length of time a product may be stored without becoming unfit for use or consumption).

Shelf-Life studies should identify the potential “failure modes” and how they will assessed/ measured. Examples of characteristics that are measured often include appearance attributes, texture, taste, microbial counts, and product effectiveness/performance.

by Hemant Urdhwareshe Leave a Comment

Acceptance Sampling plan (Part-2)

Acceptance Sampling plan (Part-2)

Dear friends, we are happy to release this video on Acceptance Sampling plans for Attributes. In the video, Hemant Urdhwareshe explains how to select appropriate sample size using Sampling Plans such as MIL-STd-105E, ANSI/ASQ Z1.4, IS 2500 Part-1 (or ISO2959 -1). The video also explains interpretation of Operating Characteristics from the standards and in Microsoft Excel. Additionally, Hemant also illustrates how to generate a Sampling Plan in Minitab software.

by Steven Wachs Leave a Comment

Why Simple Experimentation Typically Fails

Why Simple Experimentation Typically Fails

**(and Why Design of Experiments is so Superior)**

In my 30-year career as an Industrial Statistics consultant, I have frequently been told by clients that they have performed Design of Experiments (DOEs), to try and resolve design or manufacturing issues. What has become clear is that many engineers and scientists apply a rather liberal definition to DOE and include any type of experimentation in what they deem to be “DOE”.

The reality is, simplistic or haphazard “experiments” rarely are effective in solving problems, especially complex ones. Statistically based DOE provides several advantages over more simplistic approaches such “trial and error” or “one-factor-at-a-time” experimentation. These advantages include:

The use of statistical methodology (hypothesis testing) to determine which factors have a statistically significant effect on the response(s)
Balanced experimental designs to allow stronger conclusions with respect to cause-and-effect relationships (as opposed to just finding correlations)
The ability to understand and estimate interactions between factors
The development of predictive models that are used to find optimal solutions for one or more responses

Each of these advantages are discussed in a bit more detail below.

by Hemant Urdhwareshe Leave a Comment

Acceptance Sampling Plans for Quality Control (Part-1)

Acceptance Sampling Plans for Quality Control (Part-1)

Dear friends, I am happy to share our first video on Quality Control Acceptance Sampling Plans! In this video, I have explained some basic concepts and terminology of sampling plans. I have also illustrated use of Microsoft Excel to construct Operating Characteristic Curve and AOQ Curve of a sampling plan It is not possible to inspect 100% parts received from suppliers. Obviously, processes need to be capable to produce consistently good quality parts that conform to the specifications. However, there are quite a few processes where the capabilities are either marginal or low. Also, controls at suppliers may not be adequate due to many reasons. Therefore there is still a need for statistical Acceptance Sampling Plans.