Today, the survival of many organizations depends on their plans to leverage cutting-edge artificial intelligence (AI) technologies to transform their workplaces into augmented environments.

A recent IBM study found that, as a result of AI and intelligent automation, 120 million workers will need to develop new skills or even be transitioned out of companies to different jobs during the next three years. Half of the surveyed organizations had done little to rethink their training strategies to respond to this urgency.

For that digital transformation to happen, organizations must avoid the costly “buy, not build” talent strategy that involves opting for expensive new hires instead of retraining their current employees.

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.

Left to their own devices, humans tend to fall prey to biases that make them poor decision makers. For instance, among other foibles, most purchasing managers routinely under-order. In fact, past research has shown that managers are typically 10 to 20 percent off the mark when it comes to ordering the optimal quantity of products.

However, somewhat surprising, the same research has also shown that this suboptimal ordering only results in a 1 to 5 percent loss in expected profit. This conundrum has left many an executive with a dilemma. As the CEO of a medium-sized online florist shop told me: “I cannot micro-manage all my people. Before I go and intervene, I need to know the impact on my business.”

Fair point. Managers who base purchasing decisions on their gut feelings—either because they have never devised a rational ordering policy or regularly choose to override it—make a lot of mistakes. But if it only costs the firm about 1 percent of extra profit, executives may be reluctant to stir the pot. In most SMEs, a CEO’s path is strewn with seemingly juicier projects in terms of ROI.

‘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 industrial manufacturing had a buzzword of the decade, it might be “Industry 4.0.” The concept is inescapable, yet it can be hard to define, especially for small and medium-sized manufacturers (SMMs). After all, SMMs’ capabilities, needs, and budgets look very different from the large companies that often drive the latest innovations and trends. However, Industry 4.0 is so pervasive that many smaller manufacturers know more about the technologies than they might think. Below, we define Industry 4.0, then explore ways that SMMs can and do implement it already.

What is Industry 4.0?

To date, there have been four major technological trends during the past few hundred years that have revolutionized industry and manufacturing. The first was the combination of mechanization with both steam and waterpower. The second joined mass production and electricity. The third was the rise of electronic and information technology (IT) systems, and with them automation.

The United States and China have reportedly reached a so-called phase one deal in their ongoing trade war.

While few details have been disclosed, the agreement principally seems to involve the United States calling off a new round of tariffs that were slated to take effect on Dec. 15, 2019, and removing others already in place in exchange for more Chinese purchases of U.S. farm products.

Good news, right? The end of the trade war is nigh? Don’t get your hopes up.

Although business leaders in both countries will be temporarily relieved, the underlying tensions between them will not end easily.

As an economist who closely studies the U.S. relationship with China, I believe there are fundamental issues that won’t be resolved anytime soon.

Doing it in phases

Tariffs and other trade issues have received most of the attention during the trade war, but the more fundamental—and difficult—challenges are with lax intellectual property protection and China’s industrial policy.