The Manufacturing Academy Article Series

As a teenager in the 1980s, I was an avid reader of Omni, a now defunct magazine dedicated to the future—a far-off world filled with super humans, artificial biospheres and frequent encounters with extraterrestrial beings. Omni catered to armchair futurists like me with science and science fiction stories by A-level writers like Bernard Dixon and William Burroughs.

Future-oriented mass media such as Omni and “Star Wars” gives its consumers a plausible vision of everyday life for future generations. What these sources don’t typically deliver, though, is the path of change to get there. 

Everyone working in a decision-making role has at least an intuitive understanding of the concept of opportunity costs-the value of the thing you didn’t choose. Simply stated, when you say ‘Yes’ to one thing, you simultaneously say ‘No’ to everything else you could have chosen instead. And those things to which you say ‘No’ have a value that you’re relinquishing. When I was a teenager, I heard an older gentleman quip, “When I said ‘I do’ to my wife, I was also saying ‘I don’t’ to all the other girls out there”. That man understood opportunity cost.

As the quality manger for a tier two automotive supplier, I recently had the opportunity to hire a quality supervisor following the retirement of a long-time member of our team. Our company’s human resource manager and I worked together through the entire selection process. Given the status of our region’s economy and the recent closure of several large factories, I wasn’t surprised when our mailbox started filling up with resumes in response to ads on the popular Internet job sites. The typical respondent was a mid-career professional with over 15 years of experience in manufacturing that had either been recently laid-off due or who wanted to move up in their career.

Abraham Lincoln taught the value of adequate preparation when he said, “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.” By training, quality professionals are often focused on verifying the correctness of a product. A traditional inspector at the Lincoln Timber Company might have dutifully marked in her audit log the date and time, the type and size of tree, followed by the comment, “Cut down.”

But Honest Abe would have advised her to take a closer look at the tools and process used to complete the job.

With all the buzz these days about design thinking, some of you may be wondering what it’s all about. How does it relate to design? And what can non-designers gain from it?

Design thinking is far more than simply designing products and services—it’s an approach to problem-solving that can be applied to an incredibly wide range of applications.

with co-author Mark Fiedeldey

The definition of the term “reliability” begins by specifying that reliability is a probability.  Therefore, the concept of a probability, while sometimes intimidating to reliability practitioners, is fundamentally important.

Probability can be defined as the extent to which an event is likely to occur. Just as an average is a measure of central tendency, probability is a measure of uncertainty in a particular event or outcome. That’s all … Nothing intimidating here.

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.