Whatever industry you’re in, you’re going to need to buy supplies. If you’re a printer, you’ll need to purchase inks, various types of printing equipment, and paper. If you’re in manufacturing, you’ll need to obtain parts that you don’t make yourself. But how do you know you’re making the right choice when you have multiple suppliers vying to fulfill your orders? How can you be sure you’re selecting the vendor with the highest quality, or eliminating the supplier whose products aren’t meeting your expectations?

Let’s take a look at an example from automotive manufacturing to see how we can use data to make an informed decision about the options.

Camshaft problems

Thanks to camshafts that don’t meet specifications, too many of your company’s engines are failing. It’s harming your reputation and revenue. Your company has two different camshaft suppliers, and it’s up to you to figure out if camshafts from one or both of them are failing to meet standards.

When considering how good a production process is, it’s important to ask, “Can we expect the output to be fully conforming?” An assessment of process capability can answer this. Data are needed, but how many? Is “30” the right number? This article examines these last two questions.

First, why 30?

There’s an old joke about statisticians not knowing the difference between 30 and infinity, and figure 1 should shed light on its origin. Degrees of freedom, shown on the x-axis and hereafter referred to as “d.f.,” help to determine how precise, or “solid,” an estimate of standard deviation is, given its estimated uncertainty (the y-axis).¹ Figure 1 shows that by the time an estimate of standard deviation is based on 30 d.f., it’s about as precise an estimate as it’s likely to get. (If 30 d.f. aren’t sufficient, getting up to 120 d.f.—a fourfold increase—is necessary to reduce the uncertainty by half.) This is potentially important because an estimate of standard deviation is essential to make an assessment of process capability possible.

It’s a cold winter’s night in northern New Hampshire. You go out to the woodshed to grab a couple more logs, but as you approach, your hear a rustling inside the shed. You’ve gotten close enough to know you have a critter in the woodpile. You run back inside, bolt the door, hunker down with your .30–06, and prepare for a cold, fireless night.

Analyzing data using common tools like f-tests, t-tests, transformations, and ANOVA methods are a lot like that scenario. They can tell you that you’ve got a critter in the woodshed, but they can’t tell you whether it’s a possum or a black bear. You need to take a look inside to figure this out. Limiting data analysis to the results that you get from the tools cited above is almost always going to lead to missed information and, often, to wrong decisions. Charting is the way to take a look inside your data.

NFPA 70E, full title  “NFPA 70E—“Standard for Electrical Safety in the Workplace,” is a standard written by the National Fire Protection Association (NFPA). NFPA 70E was created primarily to assist companies and their personnel in avoiding potential fatalities and injuries from electrocution, arc-flash incidents, and arc blasts in the workplace. It covers subjects such as selecting appropriate personnel protection equipment (PPE), maintenance, employee training, risk assessments, and safe working practices.

Recent regulation changes

The 2015 edition of NFPA 70E shows a huge change in how electrical risk is evaluated. In the 2012 edition, quantifying a potential electrical risk was done via shock and arc-flash analysis. The 2015 edition streamlines requirements for arc and shock protection, and outlines revised program requirements with a greater emphasis on risk assessment—now referred to as a “flash risk assessment.”

I really can’t make this stuff up.

I wrote a post a couple of years ago titled: “How to Talk to Your Kids About... Quality Improvement,” in which I lamented about Community Hero Day in my daughter’s first-grade class and the need to explain to her why I wasn’t at the “community-hero level” of classmate Maggie’s mommy, the pediatrician.

Well, now it’s two years later, and my son, Thomas, is in class with Maggie’s little brother, Sam. Seriously. Again. But instead of Community Hero Day, however, the classroom had Engineering Day. Step aside, Maggie’s mommy!

Or maybe not. Enter Maggie and Sam’s daddy, the aerospace engineer.

Seriously, this family.