After announcing partnerships in 2021 with Hero Moto in India and Gojek in Indonesia, the electric motorcycle and battery swap company Gogoro aims to start producing electrical motorbikes in China, India, and Indonesia in 2022. What is its secret sauce for expanding its supply chains at such lightning speed? The answer lies with its smart manufacturing partners, PowerArena and NTT Data.
Charlie Huang, senior manager of Smart Factory Platform at Gogoro, which defines itself as a global technology leader in battery swapping ecosystems that enable sustainable urban mobility, said in a forum that the company established capability of smart manufacturing over the past 5 years and will replicate the manufacturing processes to overseas market with minimal language barriers.
“Gogoro originally wanted to outsource the hardware manufacturing part of the operation, but nobody wanted to do that for us in the beginning,” Huang said Gogoro then decided to make everything by itself – the electric vehicle parts, the batteries, the motor, etc. in Taiwan, and implemented smart manufacturing production lines with the help of artificial intelligence (AI) startup PowerArena and cloud infrastructure service firm NTT Data was key to maintain quality and efficiency.
In a typical Gogoro factory, there are 7-8 production lines that manufacture different products, and each electric motorcycle consists of more than 20,000 parts and components, such as vehicle parts, batteries, controllers, power amplifiers, etc. “Just try to imagine how you are going to manage all those warehousing work without digitalizing the process if you are to produce 1,000 units of electric motorcycles every day!” Huang said Gogoro not only has adopted Enterprise Resource Planning (ERP) system to manage processes from procurement to shipping but also digitalized the whole process of warehousing.
In addition, the company applied AI on its Manufacturing Execution System (MES) to monitor the production lines and regulate the manufacturing processes for maintaining quality, according to Huang, who worked at Ford and ASML before joining Gogoro. He emphasized that the Gogoro smart factory MES is a human-centered solution – a tool to assist workers, not to replace them. “Not all of the processes are able to go full-automation,” Huang explained. “But the key is to assign people for more valuable and meaningful work, and leave the tedious, monotonous and repetitive jobs to the machines.”
“We build our motorcycles on automated guided vehicles (AGV), and the MES would record the manufacturing process when each unit checks in and out of an AGV workstation,” said Huang.
Image recognition AI on production lines
The AI module of PowerArena solves a critical pain point for Gogoro, as the visual AI application captures the actions of workers between two workstations and would identify mistakes or lapses. “There are some processes that need to be collaborated between workers of two workstations, for example, fastening the screws on the wheels. Before using PowerArena’s AI, the MES could not record this part of the process because it is only capable of recording actions on AGVs,” said Huang.
There are also other smart manufacturing solution providers that use image recognition AI to solve problems on the production lines, how does PowerArena differentiate from other competitors?
Ian Peng, PowerArena’s business development manager, explained that PowerArena is very much focused on smart manufacturing on day 1, with team members from factory management backgrounds and have accumulated years of experience, they applied machine-learning and edge computing technologies to tailor their integrated solutions to meet the needs of customers in the manufacturing sectors.
“Right now, there are two streams of smart manufacturing AI solutions in the market, one is auditing the result for quality assurance, and the other is closely monitoring the manufacturing process to prevent mistakes. PowerArena’s solution belongs to the latter model,” Peng said the latter’s advantage is capable of knowing what had gone wrong when one piece went awry and stop workers from continuing the mistake right away.
“Mistakes are inclined to be systemic,” Peng pointed out that it is human nature to follow their habits. “Without correcting the habits right away, it would be too late to do quality check at the terminal point and the company would have to throw away a lot of defectives if the processes cannot be undone.”
Besides Gogoro, PowerArena as an AI startup established in 2017, has already got several large electronic manufacturing services (EMS) customers adopting their systems in their factories in Southeast Asia and China. Harold Cheng, business development director of PowerArena, said the company will expand its business in the United States in 2022, helping customers including tire and glass manufacturers, as well as auto parts and components makers to accelerate the digital transformation of their production lines.
PowerArena joined Epoch Foundation’s Garage+ accelerator and is currently raising pre-A funding with a target of US$3 million.
Duplicate production lines with minimal effort
Charlie Huang of Gogoro said with the help of PowerArena and NTT Data, the company’s smart factory platform can be replicated rapidly to establish production lines in China, India, and Indonesia, or wherever the company is expanding to in the future.
Gogoro’s production process module can accurately control the production line process, including equipment utilization management, getting the status of the production line in real-time through the dashboard, and can also communicate with suppliers through the cloud. “We can know when to get raw materials and components and can even install a tracker on their truck fleet to know when the materials will actually arrive at my production line or put them in the warehouse,” Huang said by using Google Map, it is easy to estimate how long it will take to get the components into the warehouse, so he can do the Toyota Just-in-time integration.
“We have included AI in all of our manufacturing execution systems, and we have systematized human discipline, machines, material records, methodology, and environmental factors together. Even if my production line has to move to another country, such as India or Indonesia, to me it is just a change in the language of user interface,” Huang said with an ear-to-ear smile.
Source: digitimes.com
