Helen Roxburgh
Dasault Systèmes Executives at Manufacturing in the Age of Experience

Lean Management and Deep Data Mining Essential for the Future of Manufacturing

Nov. 8, 2017
Researchers tell a digital manufacturing conference in China that only around 30% of Chinese manufacturing companies were ready to move up the value chain from older models of manufacturing and consider smart manufacturing.

A lean management structure and forward-looking corporate culture is essential in building a smart manufacturing industry. This is according to experts at Dassault Systèmes’ Manufacturing in the Age of Experience conference, where the move to smart manufacturing was a central theme, and experts outlined how the shift to a fully digitized company needed flexible and strong management. 

“Lean management requires lean culture, a strong will, and a highly efficient organization,” said McKinsey’s Forest Hou, who opened the Shanghai conference’s second day by presenting research suggesting most Chinese manufacturers are not yet ready for full digital automation. McKinsey surveyed 400 companies in China, the U.S., Germany and Japan, and found that companies in China showed the greatest confidence that they could achieve a smart manufacturing model. However, Hou warned that “the real readiness is a lot lower than the self-evaluated readiness.”

“I think around 60% of companies in China are still moving from 1.0 to 2.0, and there is a very weak base for lean management,” he told delegates, concluding that only around 30% of Chinese manufacturing companies were ready to move up the value chain from older models of manufacturing and consider smart manufacturing.

His conclusions were supported by data from Qi Wu, Senior Managing Director of Accenture Great China, who outlined a survey of 174 companies by his firm that found only 4% could be categorized as pioneers in this field.

“Smart manufacturing should integrate every level of the business model,” he said. “The successful Chinese companies spend more time in planning, in gaining insights into the impact of digital transformation. Those behind just launch a digital system, but don’t take the time to draw up a wider masterplan.”

The research is concerning for policymakers promoting Made in China 2025, the government’s ambitious plan to modernize the country’s factories and manufacturing industry using new technologies.

However, China is leading over many other markets in the field of robotics and AI, conference delegates heard. The Made in China plan has set national goals of producing 100,000 industrial robots per year and placing 150 robots in operation for every 10,000 employees by 2025. China, which surpassed the U.S. as the world’s largest exporter of manufactured goods just ten years after joining the World Trade Organization, also ranks among global leaders in high-tech product exports.

China accounts for about 30% of robots worldwide, and already uses AI technology like facial recognition across a broad range of operations. Similarly, use of the Internet of Things has rocketed amid strong state policy support, and the market size has been forecast to exceed $231.4bn by 2020. China already manufactures many of the world’s IoT devices - market research firm IDC has predicted that Chinese manufacturing spending on IoT will grow by around 14% per year, reaching $127.5bn by 2020. What is needed, experts agreed, is for manufacturers to apply this technology and smart processes across their own corporate culture.

“The first step is to have a look at where you have an opportunity for digitization,” Brian Haacke, Industry Sales Director, EMS/ODM Value Chain, Dassault Systèmes, told the conference. “If you are imagining moving up the value chain, you are going to be coming in touch with more digital companies, more innovative data, and you need systems in place to collaborate.”

Data was another key theme on conference’s second day, including the importance of analyzing data to develop processes.

“Traditionally, manufacturing has been looking at how to plan and act at line and factory level,” says Karine Gosse, R&D VP, DELMIA, Dassault Systèmes. “But it is extremely important to add to this the learning phase and the optimization phase.”

“If you don’t know what you want and how to measure it, then having automation or smart manufacturing won’t help you achieve it,” agreed Morgan Zimmerman, CEO, EXALEAD, Dassault Systèmes. “You need to have reached a good level in analytics first, in order to move to the next stage of automation.”

The conference was brought to a close by a tour of Dassault Systèmes’ latest virtual simulation applications in the 3DEXPERIENCE playground, and a demonstration by Zimmerman and Gosse of how Dassault Systèmes’ AI systems are being used to make real-time improvements. Zimmerman gave an example where changing the data mining process and analytic structure for one client increased the company’s precision levels by 80% over ten months.

“Most companies have knowledge sitting in their systems and their companies that they don’t know how to use - AI allows us to have full cognitive learning,” concluded Zimmerman. “The only single thing that matters is data. What is delivering value is not designing the algorithm, but the ability to connect to more data, and the ability to learn all the time.”

About the Author

Helen Roxburgh

Helen Roxburgh is a Shanghai-based freelancer, writing for publications including The Guardian, The Financial Times, and News Deeply, and since 2016 she has been the Books Editor at Time Out China. As well as writing about China-based issues, she has reported from across Asia, including India, South Korea and Malaysia, particularly focusing on real estate and urbanization.

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