(Clockwise from top left) Moderator D.J. Clark, multimedia director at China Daily Asia Pacific; Pana Janviroj, executive director of Asia News Network; Neale G. O’Connor, professor and head of the Department of Accounting at Monash University Malaysia; Wang Yu, research fellow at the College of Intelligence and Computing of Tianjin University; Grace Chai, senior reporter of business news at China Daily Asia Pacific; Umar Saif, founder and CEO of SurveyAuto.com; Ly Ly Cao, reporter at the Viet Nam News (bottom), join a webinar titled “The Future of AI in Manufacturing Industries” on Friday. GEORGE CHAN / CHINA DAILY
As artificial intelligence technology looms as the next big thing to reshape the traditional manufacturing industry, market players, companies and universities should take a good, hard look at its revolutionary power and embrace the game-changing technology with more concerted efforts, experts told the China Daily Asia Leadership Roundtable on Friday.
The online event, themed “The Future of AI in Manufacturing Industries”, was jointly organized by the Tianjin Municipal People’s Government Information Office, China Daily, and Asia News Network.
Companies could predict market demand, and design the manufacturing process from production capacity to the supply chain through machine learning and big data analysis from the consumption data collected in mom-and-pop stores by distributors
Umar Saif
founder and CEO of SurveyAuto.com
“People have doubted that AI could go this far, yet in only five years, AI has entered into all kinds of services such as finance and manufacturing,” said Umar Saif, founder and CEO of SurveyAuto.com, a big data service provider using machine learning and AI technology. He also is the chief digital officer at Jang Media Group.
He said that companies could predict market demand, and design the manufacturing process from production capacity to the supply chain through machine learning and big data analysis from the consumption data collected in mom-and-pop stores by distributors.
“Capturing data and learning from data could predict what the demand looks like,” Saif said, adding that the lack of real-time customer data has caused the production process to lag in markets where digitalization is still underdeveloped.
Neale G. O’Connor, professor and head of the Department of Accounting at Monash University Malaysia, highlighted the major challenge facing manufacturers, most of whom are small- and medium-sized enterprises, to scale up and move into intelligent manufacturing is the hard fact that owners themselves simply don’t want to combine with another factory to make it larger.
“There is a legacy mindset,” O’Connor said. “My point is that a factory doesn’t necessarily have to be fully robotized. Instead, it still can be labor-intensive. It’s just a matter of picking strategic parts of the production line to digitalize and collect more data.”
To revolutionize the factory, O’Connor said, there is no need for owners to reach out to a leading strategic consultancy like McKinsey & Co and Boston Consulting Group. Instead, he said, this is exactly where millions of undergraduate majors in engineering could come in.
“We are talking about 5 million undergraduates in China, a large portion of whom are engineering majors. Do give them an opportunity to come into the factory to do an internship or even to do a collaborative project,” he said.
O’Connor also underscored the concept of “cobots” to explain the employment impact of automation and intelligent manufacturing.
Cobots, or collaborative robots, are robots that work with people in a shared workspace. Known as people-focused robots, they are created with the goal of helping increase productivity, rather than replace human workers.
Citing a projection from the United Nations more than six years ago, O’Connor said there will be 40 million fewer manufacturing workers in China over the next decade.
“The actual supply of labor is naturally going down in the country,” he said. “I think a lot of factories are not at a stage where they will replace people with robots. Instead, they’ve got to replace the manual of data on the processes and replace it with a digital copy.”
He recalled his visit to a Taiwan-based consumer electronics manufacturer HTC over eight years ago. He remembered the company was automating the testing elements of the smartphone, rather than automating the whole smartphone assembly.
“When companies are automating different stages of the assembly line, it’s not like you just replace the whole assembly line with robots. Instead, you automate it strategically,” he concluded.
Application of smart tech
Wang Yu, research fellow at the College of Intelligence and Computing of Tianjin University, provided several vivid examples of manufacturing companies using AI to upgrade their production.
He mentioned an old and well-known Tianjin bicycle manufacturer named Flying Pigeon, which used to need several hours to assemble one bicycle in the 1990s.
“That (speed) is not acceptable nowadays; that is too slow. But since about eight years ago, Flying Pigeon has transformed its manufacturing to intelligent manufacturing. What they can do is that they assemble a bicycle in 15 to 17 seconds,” Wang said.
Wang also said that adopting AI technology will help the country achieve its targets in reducing carbon emissions and reach carbon neutrality by 2060.
He said that with AI, the amount of power needed can be predicted, thus improving the efficiency of generating power and reducing carbon emissions.
“Not only in the power generating field, but for many other industries and applications, we can also use such a strategy to reduce carbon emissions, for Tianjin and for any other cities in China,” he said.
Wang opined that China’s advantage in developing AI lies in the sheer size of its market, and a massive consumer base that can help test and promote AI research.
Despite all these impeccable strengths, Wang warned that the world’s second-largest economy should not be so arrogant to believe that it is leading the world in the field of AI.
“What this means is that the new algorithms, the new models, designed by the researchers, may be applied to some specific scenarios, but for some complex ones, such as manufacturing, we still have a long way to go,” he said.
The application of high technologies, including AI manufacturing, is also taking off in emerging markets, which used to be known for their labor-intensive industries.
“Vietnam is now one of the prominent investment markets and it has for a long time been famous for its cheap labor. Many people would come to Vietnam for it, but things are improving as companies are adopting more high technologies,” said Ly Ly Cao, a reporter at Viet Nam News, noting that the government has launched supportive policies for companies adopting high technologies.
Cao said that the coronavirus pandemic has accelerated the process as companies turn to technology to make up for the loss in manpower. “Because of COVID-19, the number of workers in factories is lower, and they may not come back to the factories after the pandemic,” she said.
However, the application of high technologies is still showing a disparity between big companies and small ones in Vietnam due to the high cost in staff training and the difficulty in accessing high-quality big data for the latter, Cao said. “Only a handful of big companies can apply the high technology while SMEs, which accounts for 97 percent of the total companies, are reluctant to invest in advanced technology,” she added. “(The application of) high tech is improving, but it’ll take a long time (for them) to enter the SMEs.”
Grace Chai, a Shenzhen-based senior business reporter of China Daily Asia Pacific, recalled an interview experience with a highly educated AI professional, who at that time was learning how to weld from the workers of a welding factory every single day.
In his quest to develop an AI-empowered welding robot, the professional initially planned to find experienced welders with a certain degree of understanding of AI technology but failed. As a result, he had to learn the technique himself and translated the manufacturing jargon as well as workers’ accumulative experiences to codes and algorithms.
Shortage of talent
Chai highlighted the shortage of AI talent with high academic degrees and a keen willingness to devote themselves into the manufacturing industries. “Meanwhile, technicians from China’s vocational schools usually do not learn much about this advanced technology,” she said.
Citing the statistics from market intelligence provider IDC, Chai said the manufacturing industry accounted for only 9.5 percent of China’s total AI market in 2018. The top three are the government, the internet, and finance industries.
Chai said she firmly believes that the development of intelligent technology is primarily focused on talent, especially in manufacturing industries.
Local governments in China have been implementing innovation-driven development strategies and building “smart cities” and smart factories, she said. To reach the goal, it is imperative to cultivate and attract more AI experts from around world, she added.
In particular, Tianjin aims to become a national advanced manufacturing research and development base by the end of the 14th Five-Year Plan period (2021-25) and make every effort to build a national pilot zone of new-generation artificial intelligence innovations.
“To provide high-end talent for the mission, local universities have already taken effective actions,” Chai said. “I have learned six higher education institutions in the city have set up dedicated colleges of artificial intelligence.”
But as an emerging field, artificial intelligence lacks an education and teaching model that can be copied, Chai said. She has noticed that some colleges innovatively developed a cooperation model linking schools and enterprises, integrating the cooperation into the teaching structure and curriculum design.
“I believe they are on the track of providing what the market really needs, and the trend presents a bonanza for research and educational institutions, and companies in Hong Kong, as well as other Asian cities,” Chai said. “They could team up with Tianjin and other mainland cities so that they could tap the market together and achieve a win-win outcome.”
Contact the writers at sophia@chinadailyhk.com