Artifical Intelligence

Artificial Intelligence

and the Fourth Industrial Revolution

We spoke with Nigel Moulton, Global CTO, Modern Data Center Business Unit, at Dell Technologies about AI’s place in 4IR

Artificial Intelligence is essential to 4IR as a systemic element present in so much of its expressions. For example, it helps facilitate the design process with ‘generative design’ software to identify design opportunities as well as problems and supply a range of solutions during both the initial design and testing.

Nigel Moulton, Global CTO, Modern Data Center Business Unit, Dell Technologies

Predictive maintenance uses algorithms to predict potential failures in specific elements of manufacturing and generating alerts. Similarly, quality control uses algorithms to monitor quality and identify quality defects in real time without suspending manufacture and generating relevant data for analysis. The safety and optimisation of robotics and human collaboration is also an area where AI has a place. Collaboration is, indeed, a major element of manufacturing to which AI contributes, enabling customized solutions as the result of the interaction of different specialists and specialisms. AI extends beyond the factory to optimise the supply chain and distribution network as well.

Nigel Moulton, Global CTO, Modern Data Center Business Unit, Dell Tehnologies, believes that AI will play a pivotal role in 4IR as the technology will be deployed to provide insight that will enable manufacturers to adapt industrial and business processes to become more efficient, or safer, or both: “There is no ‘one size fits all’ approach as each company will use a combination of AI and IoT generated sensor data to shed light and better understand a process. The intended outcome will however determine the way technology is employed, whether that’s an AI to sense, reason (infer) and adapt to produce an output that drives real-time reaction; augmented by Machine Learning (ML) to store and analyse the AI data to improve the outcome over time; or Deep Learning (DL) to infer outcomes through multiple algorithms and data sets.”

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Artificial IntelligenceIssue no 10 - April 2019 industry 4.0

Video: Artificial Intelligence: Next Industrial Revolution?

When and how much

Can AI be applied piecemeal? Can a manufacturing plant become partly smart, but remain partly pre-4IR as companies implement new technologies according to their budgets, idiosyncrasies and the limits of their imagination?

The answer probably lies in bespoke solutions that, nevertheless, embrace the need to build a smooth integrated manufacturing framework that works and will continue to work for years to come. This would imply the application of AI with the intention of evolving eventually into a complete system where the revolution has no more corners into which it has not gone. Each company is different and has its own priorities, which are reflected in its practices, policies and infrastructure. It is worth, therefore, investing time as much as money into determining the best course and most appropriate time schedule for complete adoption.

“There is an emerging use case for applying smart technologies to a discrete process or supply chain to help quantify, codify and then scale best practice from a ‘known good’ throughout the organisation,” reveals Moulton.

“In this case, smart technologies can instrument and measure something that you already do really well, help to quantify why and how it’s a core competency and then to map that competency throughout the entire supply chain process. This may well include bringing in third party suppliers, encouraging them to be a part of the instrumentation process.”

Moulton notes that businesses that leverage the intimate knowledge of the manufacturing processes held by factory-level workers to identify feasible AI initiatives to make targeted improvements to productivity tend to experience better results. “This approach, combined with partnering with trusted technology vendors, can significantly shorten the path to becoming an AI-driven company,” he says.

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industry 4.0 Issue no 10 - April 2019Artificial Intelligence

Benefits of AI

AI is about partnership and integration. The 4th Industrial Revolution is digitally driven and shows no signs of slowing down. The potential for AI application is far from exhausted but continues to supply answers to new problems as they arise and new areas of manufacturing as they emerge.

AI is about partnership and integration. The 4th Industrial Revolution is digitally driven and shows no signs of slowing down. The potential for AI application is far from exhausted but continues to supply answers to new problems as they arise and new areas of manufacturing as they emerge.

Data and connectivity are of prime importance. Data whether live or historic, much gathered by sensors and processed remotely, needs collection, collation and analysis. AI makes manufacturing more flexible by introducing prediction and reactivity, responding to eventualities and providing solutions.

The 4th Industrial Revolution involves a fresh layering of manufacturing input - human, mechanical, electrical, digital and robotic. AI is the ubiquitous agent of change and facilitation whereby the establishment and integration of the layers are established to comprise a single manufacturing entity. Whereas mundane labour tasks will be performed increasingly by machines rather than human beings, the human workforce will be redeployed rather than replaced. The smart factory has become a machine itself, run by AI and requiring: minding, maintaining, administering and monitoring by human beings, but ever more remotely.

Generative design and training

AI-driven generative design is an evolution of AI-human partnership where we set design objectives for the AI to consider, explains Moulton. “This approach can significantly accelerate the development time and reduce the overall cost of manufacturing dramatically. But it’s worth noting that the human ultimately remains in charge making the final design decision.”

Monitoring and testing in real time?

“AI systems deployed in conjunction with IoT sensors can repeatedly measure and react with extraordinary speed and accuracy across a huge spectrum of environments,” the Global CTO comments. “But AI is only as good as the algorithms that it is deployed with. Perhaps surprisingly, much depends on the definition of real time, the associated thresholds, how they are measured and what constitutes a deviation. When combined with techniques such as Machine Learning AI systems can in some instances infer outcomes that are too difficult or nuanced to be detected by human monitoring techniques.”

“AI systems deployed in conjunction with IoT sensors can repeatedly measure and react with extraordinary speed and accuracy across a huge spectrum of environments,”

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Artificial IntelligenceIssue no 10 - April 2019 industry 4.0

AI in the UK

The UK is a world leader in AI and 4IR, so well placed as a global smart manufacturing hub. Innovative companies are already located at the beating heart of technological innovation and primed to be at the forefront of change.

The UK government has set out its vision and strategy around Artificial Intelligence as a ‘grand challenge’, says Moulton. “This is why AI is now taking centre-stage for the UK government’s industrial strategy focusing on clean growth and the future of mobility. The strategy also includes opportunities to look after and managing an aging society and how to exploit the datasets in government and the private sector using AI to improve productivity and generate economic value.

The UK is in great shape to explore these challenges because we have world-leading universities and leading private businesses. We also have some of the most digitally minded consumers and businesses in the world that has firmly placed our country as one of the leading nations around digital transformation.”

What next?

“The next phase of AI in manufacturing is reaching scale to deliver the economics of mass instrumentation of an entire manufacturing environment,” says Moulton.

“AI as a technology has matured to the point where it should not be considered a science project, and the costs associated with the required instrumentation engines – IoT connected sensors – are at a point where they can be considered marginal versus the returns that can be realised.

As companies start to recognise the process improvements and the associated returns to the bottom line of a well instrumented, constantly iterating industrial process AI, ML and DL will become embedded in business practices and viewed as commonplace. In fact, it will be considered retrograde to not have them.”

Find out more:

delltechnologies.comlinkedin.com/company/delltechnologiestwitter.com/DellTech

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