With consumers beginning to seek customization and autonomous consumption and the lack of labor becoming a growing problem at the manufacturing end, manufacturers are spurred to become adaptive to a quickly-shifting and widely-varying environment as manufacturing systems become more sophisticated than ever. Thanks to the ripened, new technology, manufacturers may deploy advanced sensing technology in conjunction with AI algorithms and robots to raise information visibility and system controllability, furthering the development of smart manufacturing for Industry 4.0. According to forecasts by TrendForce, the global market scale for smart manufacturing will register a CAGR of 10.7% up to 2022 and near US$370 billion.
Based on the integration of the virtual and the real, smart manufacturing has a wide variety of applications, with use cases ranging from large-scale smart factories, smart supply chains, on-site disaster recovery to automated delivery vehicles and simple robotic arms. A summarizing glance of industry dynamics and indicatory events in 2019 like Hannover Messe shows collaborative robots (or 'cobots'), digital twins, predictive maintenance (PdM), drones, manufacturing execution systems (MES), AI applications etc. to be the focus of current smart manufacturing development. Universal Robots, Siemens, STMicroelectronics, Xilinx, GE and other suppliers are also constantly updating their product portfolios and strengthening market presence.
Edge AI Saves Computing Resources, Becoming the Cornerstone of Predictive Maintenance
As smart manufacturing will open the floodgates to an immense sea of data, latency and bandwidth costs have already causing companies to turn their backs on cloud and turn to edge computing. Big data, precision analytics and higher-performance hardware have been the three big driving forces pushing AI from the cloud down to end devices and encouraging the combination of edge computing and AI.
Edge computing is AI computing with regional relations. Through the direct collection and processing of data at its source and the combination of parameter learning and other AI techniques, a device may detect flaws immediately and predict usage conditions, avoiding the need for constant connection to the net and reducing computing resources required while still possessing some degree of decision making capacity and immediate responsivity. This will form an important basis for predictive maintenance, and may even improve real-time collaboration between industrial robots. Replacing the action of sending data to the cloud with the local retention of data may also better satisfy manufacturers' need for increased data safety and privacy.
Taiwan Chip Suppliers Best Able to Enter Edge AI; Flexibility to Form the Edge for Small and Medium-sized Enterprises
The joining together of smart manufacturing and edge AI presents advantages for manufacturers, such as real-time decision making, lower costs, operational reliability and enhanced safety. It also allows precision machinery to become an honest-to-goodness smart system. From chip giants from NVIDIA, Intel, Qualcomm, NXP to leading cloud providers AWS, Google and Microsoft, many companies are eagerly pouring investments into this field. If Taiwan suppliers want to grab a slice of the edge AI market, chips will become their best point of entry as well as the initiator for the unification of suppliers up and down the supply chain, considering the advantage and government-granted resources Taiwanese chip suppliers have in the industry.
TrendForce points out that Industry 4.0 has always been pushing businesses to embrace digitalization, whether from automation to smart automation. Other technologies including IoT, big data and robotics have also become important nodes along the road to smart manufacturing. Yet whether it be the deployment of industrial IoT, the introduction of smart manufacturing or the construction of smart factories, the time consuming and costly transition make for a high bar for companies. In the process of deployment and execution, one's technological maturity may be measured and assessed with tools provided by the Industrial Internet Consortium (IIC) etc., and one's pace and direction may be adjusted accordingly. One could, for example, determine whether to adopt passive, preventive or predictive maintenance according to one's progress of infrastructure completion.
Furthermore, since many traditional manufacturing industries lacking digital roots accomplish smart manufacturing through the introducing of digital tools and integrating of different fields of industry, companies may easier insert themselves into the supply chain of industry giants or collaborate with them if they possess advantages such as cross -field integration or strong adaptability to various ecosystems. Taiwan's small and medium-sized companies are equipped with sufficient industry knowledge and the adaptive flexibility that will allow them to help customers scratch that place tickled by digitalization the most, where giants are unwilling and small suppliers are unable.