Beyond Limits Inc. is a California Institute of Technology (Caltech)/Jet Propulsion Laboratory (JPL) spin-out company leveraging 20 years of deep space expertise in Cognitive AI. The company’s technology has been proven in the most extreme operating conditions, from NASA Mars Landings to remote subsurface environments in the energy sector.
In 2020, Beyond Limits announced its first wave of initiatives and partnerships to establish and expand their presence within the Asia Pacific (APAC) region. It’s regional headquarters in Singapore helps them drive strategic investments for joint ventures and partnerships in Asia. Beyond Limits also have operations in Hong Kong, which focuses on expanding AI solutions into verticals; Taipei centres on advanced manufacturing, while Shenzhen, Tokyo, and Singapore addresses energy and healthcare sectors.
In an interview with International Metalworking News for Asia (IMNA), Leonard Lee, President of APAC, highlights the key aspects of its technology, and the manufacturing trends they are seeing in APAC.
IMNA: How has the nature of manufacturing in APAC changed, what trends do you see?
Lee: The manufacturing industry is constantly undergoing change, and with rapid digitalisation, the sector is proving themselves as frontrunners when it comes to streamlining processes and optimising entire operations. Adopting next-generation AI software and data analytics have allowed manufacturers to eliminate potential bottlenecks and improve quality management in their manufacturing cycle. In Asia Pacific, manufacturers are facing an increased production output, despite having a declining workforce.
The shortage of talent has been a longstanding issue for the sector. However, it is raising alarms due to the rise of consumerism in the region and the demand for increased output. Simply put, younger talents rarely view manufacturing as their default choice of career, and the majority within the sector are an aging workforce. As a result, the talent pool has dwindled to only a few select individuals. With output demand soaring, manufacturers need these individuals to be quickly onboarded with the right skills and knowledge base. The underlying issue is that this expertise and skills are not easily transferable. In this regard, Cognitive AI in manufacturing is already making its mark.
To illustrate, manufacturing generally across all industries would involve individuals with high expertise and accumulated experience they received by working in a variety of operations setups and scenarios. With the declining workforce, there’s only so much that existing employees can do when facing a particular issue in a challenging situation. This is where the experience of older operators needs to be captured somehow to be used in their absence. New operators must be equipped with the ability to resolve pressing issues that could possibly lead to bigger problems across the board, from an operation or profitability perspective.
Yet there is a gap in manpower capabilities where older or more experienced operators have left or been laid off. They have been replaced by new operators who have limited experience. Enabling digital solutions that are paired to Cognitive AI, allows the technology system to capture human knowledge – which, in this case would be the experience of the more senior operators – translating it to new joiners. With all the dynamic factors with manufacturing operations, Cognitive AI can provide advice or recommendations to young operators on what actions to take to either correct an error or optimise to maximise certain goals.
IMNA: What kind of role do you see technology playing here, what can you do to help?
Lee: Take Automatic Optic Inspection (AOI), for quality control (QC) in Surface Mounting Technology (SMT), as an example. It is easy to buy into the idea that smarter, stronger, and more inspection equipment reduces the quantity of human labour in the QC stage. On the contrary, highly sensitive AOI equipment generates a large number of false alarms that require double inspection from humans.
Instead of improving the accuracy of AOI and reducing false alarms, Beyond Limits collaborated with one the world’s largest electronic Original Design Manufacturer (ODM) in Taiwan to deploy Knowledge Base Operation (KBOps), which extracts and digitalises knowledge from seasoned domain experts, for root cause analysis.
In this case, AOI equipment becomes more than just inspecting units. It captures features previously unavailable to human eyesight for KBOps to learn and document. With cognitive reasoning, Beyond Limit's hybrid AI is capable of providing optimised solutions and actionable insights for SMT manufacturers, reducing downtime, human labour, and material waste.
IMNA: How can manufacturers ensure they have the right level of AI?
Lee: The manufacturing industry has widely accepted that an imminent need exists for an industrial, state-of-the-art software solution that brings several steps or processes into a singular, cohesive platform. At the same time, they need to integrate their extremely valuable institutional knowledge to leverage their most valuable asset – historical data records. Through the implementation of next-generation AI software, manufacturers have unlocked process automation and automated scheduling towards quality monitoring and defect management, as well as applications to shorten design time and customise experiences. However, while the sector has largely embraced the adoption of machine learning, it needs to be even more timely to shift towards cognitive AI solutions.
Cognitive AI is essentially a glass box, versus conventional AI which is a black box. Conventional AI takes a whole bunch of data, churns it, and then comes up with a recommendation, without our knowing how it arrived at that point. Whereas a glass box, or what we call Explainable AI, has an audit trail, and gives us the ability to figure out and understand how it arrived at that recommendation. With Cognitive AI, there is a human reasoning component which is set in place.
For manufacturers, tapping into this insight will allow them to elevate their processes. Through knowledge capture of all existing data, including operators' expertise and skills, the Cognitive AI system is then able to take all this data and provide recommendations for an action depending on the situation. This is especially useful for the multitude of scenarios that could occur within a manufacturing plant, ranging from production schedules and equipment failures to the interdependence of the various machinery, which will then impact the ability to provide output.
IMNA: How can small and medium-sized companies in APAC improve their manufacturing?
Lee: When considering small and medium-sized companies (SMEs), especially in the manufacturing sector, there is a need to transition from legacy technology to a modern solution that allows SMEs to remain competitive in terms of output and efficiency. According to the DBS Digital Readiness Survey, the region’s SMEs are lagging behind large corporates and middle-market companies in terms of digital readiness. Only four in 10 SMEs (41%) have a digital transformation plan in place, and one in 10 have a clearly defined digital strategy (12%). Hence, for manufacturers to improve their outputs, they first need to upgrade their processes internally, so that they can start their digital transformation journey.
Unfortunately, many are facing difficulty in considering where to even begin. The first step, which is also the most crucial step for starters, is always ensuring the availability of data. Manufacturers must look at implementing solutions that arrange and compile data that is scattered in silos and place the existing data in a single repository. This key step would allow for additional technology implementations to run smoothly, such as the adoption of next-generation AI software to churn out recommendations for improving efficiency throughout the company.
IMNA: Finally, how do you see the future, with special reference to Beyond Limits?
Lee: We believe that the move towards digitalisation will only continue to advance as more organisations embrace technology and improve business outcomes. In the manufacturing sector, driving operational excellence, using techniques like Lean Six Sigma, is no longer sufficient to drive the level of productivity and efficiency needed. Combining the best features of technology and human knowledge, Cognitive AI would allow manufacturers to elevate their efforts on their smart manufacturing journey. Digitalisation is a "must have," and cognitive AI is the key differentiator. There is no doubt we can look forward to the progress of the smart manufacturing industry in Asia Pacific, while we continue our efforts to shape this dynamic region into the next global smart manufacturing hub.
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