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For the last three decades, the relatively low wage of the Chinese worker has propelled the country’s manufacturing sector. In 1990, China accounted for 3% of worldwide manufacturing by value. By 2011 it had become the world’s largest manufacturer.

As Western Europe and the US embrace Industry 4.0 and the Smart Factory, respectively, China is adopting “Made in China 2025.”

In this article we explore whether digitalization (including IIoT, Artificial Intelligence, Machine Learning, etc.) will tilt the manufacturing balance in favor of Europe/the US relative to China. In particular, we will evaluate the role of IIoT Predictive Maintenance in reducing manufacturing costs. We will also address whether the confluence of higher wages in China and greater manufacturing efficiencies in developed countries level the manufacturing playing field.

The “Secret” to China’s Success
It’s no secret. The average Chinese worker is often forced to work long hours, at low wages, and in unsafe working conditions, thereby creating favorable economics for China’s manufacturing sector.

At the same time, there has been wage pressure in the last few years. Between 1990 and 2016, the average monthly salary in US dollars rose from $37 to $854. During this period, the average monthly salary in Vietnam rose from $54 to $210. In terms of China’s unit labor production costs, they have risen from 47% of U.S. levels (1990) to 75% of US levels (2016).

The result? Multinationals that produce labor intensive goods such as apparel and textiles have shifted some production to countries with lower pay scales in Southeast Asia. It should be noted that many low-cost manufacturing centers benefit from laws constraining workers’ rights to unionize and negotiate higher pay rates. For instance, in an NGO report in the Economist, almost 30% of workers in Malaysia’s electronics industry are forced laborers.

Historically, China’s relatively low labor costs have masked a flaw its in manufacturing sector: low productivity rates. A.T. Kearney states that due to “bloated cost structures” and “inefficient manufacturing processes” Chinese manufacturers are as much as 70% less efficient than manufacturers in developed economies. One example: a Chinese steelmaker uses 300% of the water and 200% of the energy of its German counterparts.

What to Expect from “Made in China 2025”?
In 2015, China unveiled its 10-year “Made in China 2025” plan designed to to transform China from a manufacturing giant into a world manufacturing power. Nine prioritized tasks were included improving innovation, integrating Information Technology and industry and strengthening the industrial base. In 2018 the plan was updated and now includes the goal of becoming the number one in industrial “research and development.”

China’s ambitious plans have caused consternation in certain Western capitals. In fact, it has been described by US Trade Representative Robert Lighthizer as “a very, very serious challenge, not just to us, but to Europe, Japan and the global trading system.”

Putting aside the government’s plan, the reality is more complex:
• In a ranking by Accenture of 20 leading economies’ strength of enabling conditions for the widespread adoption of the Internet of Things, China was ranked 14.
• Only 10% of Chinese manufacturers have started the transition to the IIoT.
• 6% of Chinese manufacturers have a roadmap for Industry 4.0 versus a fifth or more in the US, Germany and Japan (2017)
• According to a report by McKinsey & Company, Chinese auto manufacturers lack the digital grounding to “analyze, manage, and use data collected from production lines.”

The Emerging Role of Artificial Intelligence in the Industrial Domain
Let’s turn our attention back to developed economies including Western Europe and the US. Historically, industrial machines have generated terabytes of sensor data that were not operationalized. At the same time, industrial plants are plagued by an average of 17 days a year of unscheduled downtime. For example, in the automotive industry the cost of unscheduled downtime is $22,000 a minute and can reach as much as $50,000. From a revenue perspective, the untapped potential from increased uptime is a driving force in the adoption of Industrial IoT.

When AI and Machine Learning algorithms are applied to industrial sensor data, signs of evolving machine failure are detected. Furthermore, when correlations between patterns of anomalous machine data are performed, the root cause of failures can be determined, thereby enabling factory technicians to remediate before machine failure occurs.

Why is this important? If IIoT-based Machine Learning is used to its full potential in developed economies, the relative advantage of lower costs from China (and other Southeast Asian manufacturing centers) can be reduced and offset by increased productivity. IIoT may not close the gap for labor intensive industries such as textiles and apparel, but for higher value-added manufacturing plants, it could level the playing field.

Avoiding the Hype
With so much hyperbole around Industry 4.0 we do not wish to add to the noise by suggesting that China’s manufacturing sector cannot adapt to digitalization. It can, and should seriously act on it. It would not be wise to bet against China, especially given its success in recent years of using its economic power to gain political influence and access to markets across the globe. Furthermore, cash-rich Chinese companies can play catchup by acquiring innovation. This was seen in 2017 by the acquisition of several German companies including robotics manufacturer Kuka for 4.4 billion euro and EEW Energy for 1.4 billion euros.

Summary and Conclusion
We are now experiencing the fourth industrial revolution. For a moment, let’s consider the harsh but inciteful words of Mao Zedong: “A revolution is not a dinner party, nor a literary composition, nor painting nor embroidering. It cannot be done so delicately, so leisurely, so gentlemanly and gently, kindly, politely and modestly. Revolution is insurrection, the violent action of one class overthrowing the power of another.”

Although it is premature to reach conclusions about the long-term impact of Industry 4.0 / Made in China 2025 on specific countries, a shakeup in global manufacturing is a likely outcome.

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David Almagor

David Almagor

A serial entrepreneur with over 30 years of experience in managing complex R&D as well as business entities, and taking them from startup inception to business success. Previously – Founder and CEO of Panoramic Power (acquired for $65M). BSc EE, Technion. MSc EE, PhD Electrical Engineering, University of California San Diego