CTO of ASUS: Systems Integrators for Manufacturing Automation Don't Scale

The manufacturing industry is undergoing technological transformation, but progress has been slow. As part of my series to uncover little-discussed insights from technology leaders in manufacturing, I recently sat down with Tai-Yi Huang, CVP and CTO of ASUS, a top consumer electronics brand and manufacturer. Huang leads the charge in ASUS’s adoption of new technologies, focusing on the development and early validation of an AI engine. While both Huang and I agree that manufacturing provides many high-leverage applications for AI, widespread adoption of the technologies on the market has been slow. Huang shared his perspective: “We don’t see more AI in factories because the systems integration effort is too heavy.”

Technology vendors develop and provide new products like smart cameras, robot arms, or analytics software. The buyers are the brands or manufacturers who want to incorporate that technology into their process. To do that, they employ a middleman, a systems integrator.

Systems integrators specialize in knowing about all available technologies and delivering combinations of them with some custom engineering to provide a complete (but often non-reusable) turnkey solution for the manufacturer’s specific need. If the systems integrator selects a smart camera as part of the solution, they will also be the ones to do custom programming of the camera to work for the use case.

The problem is that every use case requires a new, custom setup (not to mention if changes are made to the process or parts, these setups have to be updated), making it very slow and expensive. As a result, it only makes sense to use systems integrators, and the automation they provide, for the most painful applications in the upper echelon of ROI. There are countless other potential applications in the visual inspection space alone where the cost of the technology isn’t prohibitive, but the cost of integrating it is. As Huang said, “The systems integrator effort is not scalable.”

Since systems integrators have been the main method that technology vendors use to deliver new technologies for decades, they have designed with them in mind. Many technology products for manufacturing prioritize delivery of advanced functionality over ease-of-use and intuitive design. Complex user interfaces mean manufacturers often cannot make minor adjustments or updates to their automated solutions without calling in an integrator and paying their fees. It also means that manufacturers are sometimes not getting everything they could be from the technologies they’ve purchased because additional setup would be expensive. Since the setups are not reusable, in some industries where the product lifecycle is short, these expensive lines full of equipment are essentially "disposable" and retain no value to the purchaser after the program is done.

The pervasive use of systems integrators also points to larger issues that affect everything from automation to AI. Most importantly, it points to a lack of readily available data. Data is the lifeblood of any AI engine -- ASUS and Instrumental alike. While many manufacturing processes are partially automated in the United States, most tracking and assembly processes in low-cost labor counties are still manual – including paper and pencil notations and manually generated reports. Needless to say, these paper or low-resolution MES (manufacturing execution systems) tracking systems are usually not good candidates for AI analysis. When paper and manual reports compiled in Excel are part of the foundational data system for a manufacturing process, it’s difficult to build anything more advanced on top. Even different sites owned by the same manufacturer may use different data systems. Huang explained, “Everything is unique, so each data solution is unique.”

So how do we unblock technology adoption to bring about the dividends of Industry 4.0? Huang believes AI will play a large role. The opportunity is tantalizing: AI can help to bridge the gap. Today we have applications that require custom setups by systems integrators. Still, AI allows for generalized technologies that might set themselves up as they learn about an application from data – with little to no systems integration efforts.

The industry needs fundamental changes in the complex ecosystem required to get technology on manufacturing lines. Technology solutions must decrease deployment friction and prioritize flexible solutions, such as self-learning or adaptable AI technologies. Manufacturing companies must also take fundamental steps towards digitization and remove data extraction hurdles. Manufacturers should start by investing in technologies that capture readily available and often overlooked data, like images, and favor options that can deliver concrete ROI.