Understanding the causes of faults and defects, and then improving the system or process so it won’t happen again, is central to lean manufacturing. This article looks at some of the methods used to identify the root causes of issues so that you can prevent downtime and move toward zero-defect manufacturing.

‘Lean” is such a convenient term; everyone uses it based on their own definition. People frequently use “lean” in place of “efficiency,” probably because it sounds more cool. Another round of cost cutting? Sure, let’s tell everyone we’re “going lean,” again.

Lean is a proven, powerful productivity approach (we probably owe post-WWII modernity and the internet age to lean), yet most people don’t know what lean is really about beyond the hype. And in this age of hyper-competition, not knowing or using tools that are proven to work is a big disadvantage.

So people should learn and practice lean. But there’s one complexity: Today’s lean is a mix-up between two different but same-sounding management concepts—lean manufacturing and lean startup. Lean startup is a recent-decade thing—it was inspired by, and hence not disassociated with, lean manufacturing, but it serves a somewhat different purpose and audience. Lean manufacturing traces its roots to Japan’s post-WWII industrial recovery with the aid of some key American industrial engineers.

Let’s clarify.

The Automotive Industry Action Group’s (AIAG’s) and German Association of the Automotive Industry’s (VDA’s) new Failure Mode and Effects Analysis Handbook (AIAG, 2019) offers significant advances over FMEA as practiced 15 or 20 years ago.¹ The publication is definitely worth buying because the new approach includes valuable methodology; this article will cover the most important points and highlights.

New features

The new process is qualitative rather than quantitative, which overcomes a major drawback of the previous approach. The older occurrence ratings were based on the probability of a failure, and the older AIAG manuals even tabulated recommended nonconforming fraction ranges. If, for example, the failure was 50 percent or more likely, the occurrence rating was 10 (worst possible on a 1 to 10 scale), while one or fewer per 1.5 million opportunities earned a rating of 1. These probabilities can be estimated from a process capability study, assuming that one is available; otherwise, one might easily have to guess.

‘I have been offered a significant increase in salary by another employer and am giving my two-week notice.”

My guess is that this is the most common reason given when employees quit their current job. But is salary the real reason most employees quit? I have always suspected and believed that, given a fair salary, people do not quit their jobs for money. So why do they leave?

I was recently drawn to explore this topic a little more deeply after reading an article about IBM Watson’s latest feat. A new, proprietary IBM AI algorithm can predict with 95-percent accuracy which workers are about to quit their jobs. The algorithm has been successfully deployed to predict IBM employees who are a flight risk and then to propose actions to managers to engage and retain those employees. Exploring a little further the topic of employees quitting, I discovered a recent Harvard Business Review blog by Jon Christiansen. Through looking at 15 years of data, Christiansen identified eight reasons he believes employees quit.

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.

As usual with Quality Digest’s diverse audience, this year’s top stories covered a wide range of topics applicable to quality professionals. From hardware to software, from standards to risk management, from China trade to FDA regulations. It’s always fun to see what readers gravitate to, and this year was no different.

Below are five articles that garnered a lot of interest from our readers. As you can see, the topics are quite diverse.

Improve Risk Management and Quality Across the Value Chain by Increasing Visibility
by Kelly Kuchinski

Eric, a 40-something married father of three, runs a successful startup. Given his demanding career, he and his wife decided she would be a stay-at-home mum. Eric believed the attention he devoted to his family was adequate, and that he had fully harmonized his work as CEO and life as a family man.

On a recent family trip, Eric continued working as much as he could, as he always did. While taking a conference call, he dropped his phone and, without hesitation, leapt to catch it at the risk of hurting himself. Seeing this, his 13-year-old son blurted out, “I don’t know if you would have jumped after me like that.” Only then did Eric realize that his son didn't think he prioritized their family. Eric had been oblivious that his family felt neglected; he had been unaware or was in denial.