Creating a data-savvy culture across all sectors
FOR a long time, the dramatic growth of Chinese manufacturing industry was partly attributed to the speed and efficiency of the country’s logistics sector. But with rising labor and other costs, it is becoming increasingly difficult for Chinese manufacturers to maintain this competitive edge.
For Li Peigen, a brighter future is one that is driven by data. The ex-president of Huazhong University of Science and Technology spoke recently at a forum held at Shanghai National Accounting Institute, where he discussed the role data can play in the evolution of Chinese businesses.
Affectionately nicknamed “Uncle Gen,” the 69-year-old has been a key member of a task force that drafted the “Made-in-China 2025” white paper, a milestone document that comprises the guidelines on how China should react to the smart manufacturing revolutions sweeping across the world.
Data has been part and parcel of the smart manufacturing revolution, commonly referred to as Industry 4.0. Indeed, so crucial is its role that many experts have compared it to the “new oil,” as significant as the sovereign land, sea and air of a country, said Li. Data could well become a new core asset of our country too, he added.
Speaking about China’s efforts to prepare its industry for the coming age of Industry 4.0, Li said the success hinges less on developing the hardware and all the supporting physical infrastructure than enhancing awareness to create a data-savvy culture.
As Industry 4.0 is essentially about building a complex Internet of Things (IoT) that links every conceivable item in daily life, the unrestrained flow of data and information will become a new feature of the industrial landscape tomorrow. As a result, the entire logic and process of businesses will be affected. For example, in the past, once equipment or products are sold, they are almost disconnected from manufacturers in the sense that only occasional maintenance and repairs are provided.
This is changing in the era of IoT, where manufacturers are responsible for products throughout their life cycles.
As products generate data that are transmitted real-time to the suppliers, it has become possible to monitor the status of equipment and machines at every step — and possibly intervene when suppliers identify a potential risk that may disrupt the work flow and result in financial losses. This could be done only through data analysis based on the IoT, Li noted.
A more in-depth understanding of IoT and big data has led to the conception of what is known as “digital twin” among industrial professionals, who hail it as the trend for high-tech manufacturers.
According to GE’s definition, a “digital twin” is a dynamic digital representation of an industrial asset that enables companies to better understand and predict the performance of their machines and find new revenue streams, and change the way their business operates.
Li observed that with the aid of “digital twin” technology, manufacturers can test new ideas or make virtual changes to their digital prototypes on the computer instead of risking making costly mistakes by applying those changes directly to the physical items. First proposed by the US Department of Defense, the concept “digital twin” has tangible benefits to offer various industries.
“By 2035, when a newly built plane is delivered to an airline company, it will come with a detailed digital model of the real thing,” said Li.
He believed that data is critical to the operation of firms in every aspect of their daily work, including but not restricted to production.
“Data can exert its positive influence in supply chain management, client relationship management and human resources management,” he said.
It is highly recommended that business leaders leave some tricky tasks to big data so as to weed out the whims of human temperament.
A typical example is that inside Chinese workplace guanxi, or connections, sometimes could inhibit the objective appraisal of employee’s performance.
Li suggested that some tech-savvy firms leave the appraisal entirely to data. “Instead of assessing the work of employees manually, why not trust big data with the job, which has the obvious benefit of removing human error, discrimination or other irregularities from the process?” he asked.