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 Matthew Reid 1 Comment

Reliability Engineering using Python

Software tools are a cornerstone of modern Reliability Engineering, enabling reliability practitioners to perform their analysis without getting bogged down in the details of the underlying mathematical processes. There are many software tools available for reliability engineering, some of which are tailored to this application, while others are more general statistical tools which can be adapted to the needs of reliability engineers. One thing these tools have in common is their graphical user interface (GUI). The GUI requires only a basic level of knowledge to operate, but with a few clicks of the correct buttons, the desired task can be achieved with relatively little mental effort. It is the user friendly GUI that draws reliability engineers to select such applications as their tools of choice for performing reliability engineering analyses.

[Read more…]

by Dennis Craggs Leave a Comment

Introduction to Normal Probability Plots

Introduction

When analyzing a continuous variable or type of measurement using statistics, an analyst often assumes data is normally distributed. But, how can this normal assumption be verified? While there are numerical normality tests, an alternate approach is to use graphical methods. The old adage, “A picture is worth a thousand words”. This captures the idea that the human mind is good at discerning patterns.

[Read more…]

by Steven Wachs Leave a Comment

Pre-Control: No Substitute for Statistical Process Control

Statistical Process Control (SPC) charts allow timely detection of assignable causes of process changes (e.g. shifts, trends, variation) so that root causes may be determined and corrective actions taken before product performance is adversely impacted. Proper use of SPC identifies and eliminates “special cause” sources of variation. To achieve desired process capability, sources of “common cause” variation may need to be identified as well, using tools such as Design of Experiments to develop process understanding and predictive models that explain the source of the unwanted variability. [Read more…]

by Steven Wachs Leave a Comment

Analyzing the Experiment (Part I) – Main & Interaction Effects

We are ready to learn how to analyze the data collected during the experiment. This is the most exciting part of DOE! We will cover the analysis in this article as well as the next several articles.

The following are the main steps to perform during the analysis:

Calculate the Main Effects and Interaction Effects
Test the Effects for Statistical Significance
Interpret the Significant Effects (Often with Plots)
Develop Predictive Model(s)
Perform Model Validation (Residual Analysis)
Find Solutions and Perform Optimization to find Best Solution(s)
Validate the Solutions
Determine if Follow-up Experimentation is Necessary

[Read more…]

by Carl S. Carlson Leave a Comment

Facilitating FMEAs

“Team leadership is the secret that makes common people achieve uncommon results.” – Ifeanyi Onuoha

One of the key factors for successful application of FMEAs is skillful facilitation of FMEA teams. The skills needed for excellent facilitation are different from the skills needed to be a good FMEA team member. [Read more…]

by Ray Harkins Leave a Comment

Right to Repair

“Everything’s computerized now. The backyard mechanic is a thing of the past!”, I recall my father declaring more than 20 years ago while leaning over the open hood of my mother’s Ford Windstar. He was referring to the relative simplicity of repairing the largely mechanical systems in automobiles from the 1950’s and 60’s versus those of the early 2000’s that were equipped with digital sensors and a central processor. Of course, today’s plug-in EV’s, advanced driver assist systems, 360-degree cameras, and “guardians” like General Motors’ OnStar make my mom’s old minivan look like an Anglia in comparison.

[Read more…]

by Steven Wachs Leave a Comment

How can an OC Curve be Used to Manage the Risk of Undetected Special Causes?

When applied properly, SPC provides manufacturers a proven method to increase profitability and achieve a deeper understanding of their processes.. Additionally, SPC can prevent problems—saving companies money that would have been lost in scrap, rework, warranty, litigation, and market share decline. A key factor in obtaining these SPC benefits is the proper deployment of control charts. Correctly designed control charts identify significant changes to a process. These can be changes that are still within specification, but are statistically different than where the process was previously running. By identifying the changes, personnel can determine what caused the change and potentially improve the process or prevent the production of inferior products. [Read more…]

by Larry George Leave a Comment

User Manual for Credible Reliability Prediction

The ASQ Reliability Division (RD), copyrighted the 2003 monograph “Credible Reliability Prediction” (CRP) but lost all copies circa 2014. I pestered the RD to let me republish CRP, because people asked “How do I make credible reliability predictions?” Copyright reversion to authors is accepted practice when a publisher no longer supports a document.

[Read more…]

by Steven Wachs Leave a Comment

Conducting the Experiment

In this week’s article entry, we discuss some guidelines for conducting an experiment. As we discussed in an earlier post, planning the study is critical for a successful outcome. A good plan makes the conduct of the study straightforward.

Below are some key aspects of conducting the study: [Read more…]

by Larry George 2 Comments

Credible Reliability Prediction?

ASQ Reliability Division published “Credible Reliability Prediction” (CRP) in 2003. Harold Williams, Reliability Division monograph series editor, wrote, “[CRP] …delineates statistical methods that effectively extend MTBF prediction to complex, redundant, dependent, standby, and life-limited systems… This is the first text that describes a credible method of making age-specific reliability predictions…. This monograph presents insights and information inspired by real applications and [still] not covered in contemporary reliability textbooks.”

[Read more…]

by Ray Harkins Leave a Comment

Closing the Manufacturing Skills Gap

Manufacturing companies are struggling with the persistent and growing problem of finding employees with the skills needed to sustain and grow their businesses. This problem is commonly called the “skills gap.” Other sectors like construction are also affected by this skills gap. But at the macroeconomic level, the skills gap in manufacturing is particularly profound because of the growth in other sectors that manufacturing influences.

[Read more…]

by Steven Wachs Leave a Comment

Important DOE Techniques

In this article post, we discuss several important techniques to consider when conducting and analyzing an experiment. They are summarized in the table below and next we discuss each one in a bit more detail. Some of these techniques are bit more advanced (e.g. Blocking, Covariates), but they are introduced here. [Read more…]

by Steven Wachs Leave a Comment

How Should the Sample Size be Selected for an X-bar Chart? (Part II)

An earlier article focused on the conceptual application of appropriate sample sizes for X-bar charts. As we discussed, the purpose of control charts is to detect significant process changes when they occur. When the proper sample size is selected, X-bar charts will detect process shifts (that have practical significance) in a timely manner. [Read more…]

by Ray Harkins Leave a Comment

You’re Using the Wrong Average!

Imagine riding your motor scooter one sunny afternoon to your auntie’s home who lives near the sea, 20 miles from your home. On your trip there, the wind is at your back and the terrain slopes downward, resulting in an average speed of 25 mph. On the way home however, you’re motoring on a slight upward slope and into the wind, resulting in an average speed of 15 mph.

[Read more…]

by Carl S. Carlson 2 Comments

Relationship between FMEA and Risk Management – Part 2

FMEA has an important relationship with risk management. This article provides an example of this relationship.

The suggested sequence is to first read the article “Relationship between FMEA and Risk Management” to learn about the interactions between FMEA and Risk Management at a high level. [Read more…]