with co-author Mark Fiedeldey

Working with data, we often choose a model to represent that data. We then use the data to estimate the parameters of the chosen model and we then calculate a confidence interval about the model’s parameters. The confidence interval gives us a numerical assessment of how certain we are, based on our data, of the true value of the distribution parameter we estimated.

In measurement science, “bias” refers to the systematic error component of the measurement system. Unlike other types of measurement error that are randomly distributed, a bias predictably shifts a measurement in the same direction.

For instance, I recently facilitated a “round robin” measurement correlation study with two other companies, where we compared the outputs of our hardness testers using the same set of test samples. While preparing for that study, I realized that one of our hardness testers, on average, tested 1.1 Rockwell B points higher than the reference sample. It wasn’t testing exactly 1.1 points over, but instead ranged from .8 to 1.4 points over across a series of tests. In other words, we had two error components: a bias of +1.1 points and a random error of +/- .3 points. To compensate for this bias, we shifted down the output reading of the tester by 1.1 points, leaving only the random error component in our tester’s output values.

A version of this article was previously published in the December 2015 edition of Quality Progress magazine.

Last summer while visiting my hometown, I ran into Sam, an old friend who works in a senior technical position for a very large organization. In the course of our conversation, Sam told me about a recent discussion he had with his division’s manager about the possibility of moving up in the company. He was trying to find out if his boss would recommend him for a promotion to a particular supervisory level position that had just opened. His manager’s reply was provocative.  He said, “Sam, you’re excellent at your job. I don’t know what we’d do without you. But before I could recommend you for a team leader position, you’re going to have to work on your soft skills.”

Aside from meeting specific quality system standard requirements—such as those found in ISO 9001 or IATF 16949—well-designed quality system metrics also can serve as meaningful indicators of the strengths and weaknesses of an organization’s various processes.

As a quality manager, I often consider how precisely our quality system objectives and other metrics describe the effectiveness of our systems. One metric I started using recently—overall equipment effectiveness (OEE)—has provided our management team with a composite measurable that encompasses three major branches of our operation: maintenance, production and quality.

Coauthored with Mark Fiedeldey

The geometric distribution is a discrete distribution often encountered in reliability work, which contains some valuable properties worth understanding. Let’s take a look at some of these characteristics.

This model is useful when trying to answer the question, “How many trials are needed to get a particular result?” So, the number of trials is not set initially; we need to figure that out.

Coauthored with Mark Fiedeldey

The binomial distribution is a discrete distribution useful for estimating the probability of success or failure when these are the only two possible outcomes. Thus, the prefix, “Bi”. Understanding the binomial distribution — how it’s used and under what conditions – is therefore a valuable analytical tool for reliability engineers.

One of the most important skill sets you could master to prepare you for business leadership is sales and negotiation. Some of you may be thinking, “Gosh, I’m not going into sales or marketing. I can skip this blog post!”

But I want to assure you, regardless of your field, sales and negotiation are part of it. 

If you’re working in operations and want to expand your department, you need to sell that idea to your executive staff. If you’re a designer with a bright idea for a new line, you’re going to have to sell that idea to the people you work with … to the other managers, engineers and stakeholders in your organization.

Regardless of your technical background, the higher you move within an organization, the more you need to understand about the organization’s finances.

Accounting is often referred to as the language of business. And I believe it. Before I studied corporate finance and accounting, I often felt lost. I didn’t speak the language. I remember sitting in a board meeting where other managers and board members were reviewing the financials for the organization. And I simply didn’t understand what all the numbers meant. By the time I starting making some simple two connections, the meeting had moved onto the next topic.

No discussion about managerial skills would be complete without talking about time management. Effective managers are excellent at utilizing their time. More than just their time, successful managers proficiently steward the range resources under their control … the people, the equipment, and the finances. But time is the most valuable of them all.

The first and most important aptitude needed to move from a maker mentality to a management or leadership mentality is that of a lifelong learner.

Now, I’ve heard plenty of people say, “I learn something new every day.” And it’s a cute adage. But watching the History Channel or scrolling mindlessly through YouTube videos won’t likely produce a leadership mindset.

Instead, lifelong learning is an ongoing, self-motivated journey of acquiring new, particularly in-demand skills. Lifelong learners develop and employ strategies and plans to acquire new career skills. These plans may involve returning to university to acquire a new degree, but they certainly don’t have to.

Most successful engineers, analysts, programmers, and other types of makers of products and services will, at some point in their career, encounter a crossroads where they can either continue making, engineering, and programming, or they can begin managing those who do.

Many corporate professionals feel the frustration of wanting to move up the ladder, but never getting a crack at their boss’s role. In many companies, large and small alike, navigating into a managerial role feels like stumbling around a dark room looking for the exit. Some people find their way; most don’t.

“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.

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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.

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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.

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