If you are wondering whether your organization could benefit from formal root cause analysis (RCA) and corrective action training, read on to see if any of these issues are present in your day-to-day operations. RCA and corrective actions are some of the most useful tools for continual improvement.

Here’s why you should include them among your company’s (and all employees’) tool set.

1. High number of NCRs in your company

It’s true that the number of nonconformance reports (NCRs) will depend on the volume of operations a specific company has. Therefore, the “number” of NCRs is a relative figure. However, if you know you have a high number of NCRs, the issue may be that you are not performing effective RCA and corrective action.

What a year.

No matter your job, your industry, or your political beliefs, this year has been a heck of a ride. The (still ongoing) trade war with China, manufacturing gains (and losses), the 737 MAX, Hong Kong riots, North Korea, Brexit, impeachment. What a mixed bag of ups and downs that has influenced all aspects of our life. Including our work. (For insight on how our editorial director deals with all this on a personal level, read her op-ed here.)

But on a workaday level, we each continue to do our job as best we can. That’s what we are paid to do. We take pride in the product or service we provide and try our best to deliver what our customers want when they want it, despite what turmoil swirls around us. And quality professionals are critical to that effort. Whether you are an inspector, auditor, calibration tech, metrologist, quality manager—or for most of our readers in small companies, all of those—the economy would suffer without the work you do to oversee quality. A robust economy is built in large part upon the quality of the products and services it produces.

When we look at a Personal Kanban, its simplicity belies its power. Visualizing our work as individuals, as teams, and even as teams of teams creates trust, reliability, and understanding. When we want to coordinate work, these are serious prerequisites.

The image below is from a construction trailer, they are engaged in a lean construction exercise known as a “pull-plan.” Each color is a different contractor; each diamond is a delivery or a milestone. In this case we have five different contractors whose daily work relies on the completion of daily work done by the other contractors.

A construction team planning work openly and honestly

To spell this out, their work directly relies on people in other companies, every day.

Historically, this had led to predictable delays as different companies worked at different speeds for different reasons. You might recognize this from different departments in your company or different people in your family. Our work often relies on other people who are often simply ignorant of our needs.

A new research effort at the National Institute of Standards and Technology (NIST) aims to address a pervasive issue in our data-driven society: a lack of fairness that sometimes turns up in the answers we get from information retrieval software.

A measurably “fair search” would not always return the exact same list of answers to a repeated, identical query. Instead, the software would consider the relative relevance of the answers each time the search runs—thereby allowing different, potentially interesting answers to appear higher in the list at times.

Software of this type is everywhere, from popular search engines to less-known algorithms that help specialists comb through databases. This software usually incorporates forms of artificial intelligence that help it learn to make better decisions over time. But it bases these decisions on the data it receives, and if those data are biased in some way, the software will learn to make decisions that reflect that bias, too. These decisions can have real-world consequences—for instance, influencing which music artists a streaming service suggests, and whether you get recommended for a job interview.