Manufacturing automation relies on systems integrators, and it’s not scalable
The manufacturing industry is undergoing technological transformation, but progress has been slow. From AI to robotics and automation, technological advancements have arrived, yet roadblocks exist that prevent widespread adoption. As part of my series to 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, with a particular focus on the development and early validation of a soon-to-be-released AI engine. While both Huang and I agree that manufacturing provides many high-leverage applications for AI, widespread adoption of the technologies that are on the market has been slow. Huang shared his perspective: “The reason we don’t see more AI in factories is that the systems integration effort is too heavy.”
In the manufacturing ecosystem, there are three major players. The technology vendors develop and provide new technology 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. In order to do that, they employ a middleman of sorts, a systems integrator. Systems integrators specialize in knowing about all of the available technologies and delivering combinations of them, tied together 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 a 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) – which makes it very slow and very 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 put it, “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 that manufacturer’s often cannot make minor adjustments or updates to their own 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.
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 like the one ASUS is developing. While in the United States, many manufacturing processes are at least partially automated, where ASUS builds, most tracking and assembly processes are still manual – including paper and pencil notations and manually generated reports. Needless to say, but 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, “Right now 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, but AI provides the opportunity for generalized technologies to set themselves up as they learn about an application from data – with little to no systems integration efforts.
It’s clear that 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. One such example is Instrumental. Instrumental aggregates visual data with drop-in camera systems and processes it with AI which enables process monitoring, detection of unanticipated defects, and increased yields. Instrumental also owns the entire deployment process and has prioritized frictionless and flexible implementation with fast ROI from the beginning – that means no systems integrators.
With the manufacturing industry facing heavy pressure from external forces to scatter portions of their supply chains, the status quo is just not going to scale. Now more than ever is the time to invest in AI technologies in order to scale, stay competitive, and get the benefits promised by Industry 4.0.