Artificial Intelligence in the era of Industry 4.0

By Lux Rao, Chairman of Manufacturing Working Group, The IET IoT Panel and Director and Leader, Dimension Data

Digital transformation is having a significant impact across all industries. Manufacturing organizations have begun to acknowledge, and even experience, the advantages of digital transformation. While it would be unwise to say that most manufacturing companies know where to go, it is fair to say that they understand the direction to drive towards Industrie 4.0

As the industrial landscape transforms, manufacturers are facing several new challenges. They’re not only grappling with new technologies, but with new market challenges and changing customer expectations. It’s no secret that the digital economy is fundamentally changing the way manufacturers do business. Digital disruptors are rewriting the rules of the game. To stay competitive, manufacturers today need to improve their business operations and get the most out of every asset, innovate faster to speed time to market, and reduce security and compliance risks.

Technologies such as AI/ML, Industrial IoT, Hyper Aware systems, IoT Platform enabling IT/OT Confluence, Data Analytics, Secure Operations are some of the technologies that are enabling Digitization.

While AI is poised to radically change many industries, the technology is well suited to manufacturing, says Andrew Ng, the creator of the deep-learning Google Brain project and an adjunct professor of computer science at Stanford University.

Landing.ai, a startup formed by Andrew Ng, focuses on manufacturing problems such as precise quality analysis. It has developed machine-vision tools to find microscopic defects in products such as circuit boards at resolutions well beyond human vision, using a machine-learning algorithm trained on remarkably small volumes of sample images.

“AI will perform manufacturing, quality control, shorten design time, and reduce materials waste, improve production reuse, perform predictive maintenance, and more,” Ng says.

Application of artificial intelligence technology in Manufacturing spans several use cases ranging from better-designed products, significant cuts in unplanned downtime to enhancing OEE (overall equipment effectiveness) and manufacturers are applying AI-powered analytics to data to improve efficiency, product quality and the safety of employees.

Predictive Maintenance is one of the most compelling cases for AI in manufacturing. It involves the use of advanced AI algorithms in the form of machine learning and pseudo neural networks to formulate predictions regarding machine failures and the ability to predict the next failure of a part, machine or system. Resulting in reduction of unplanned downtime, as well as for extending the Life Time Value of machines and equipment.

Robotic Manufacturing Processes: As industrial robots enter the production floor, the human-robot collaboration will have to be efficient and safe. AI will be the core in enabling robots to handle more cognitive tasks whilst making autonomous decisions based on real-time environmental data. This will enhance process efficiencies and create a seamless human-robot blended process.

AI is dependent on data – tons of it with most of them coming in from multitudes of sensors tracking operating conditions and performance of factory tooling, learning to predict breakdowns and malfunctions, and taking or recommending preemptive actions.

Much of the data will come from sensors embedded in the processing equipment not only at the factory but also at suppliers’ facilities, tracking parts inventories and other front-end inputs and monitoring product-quality issues at distributor locations or retail outlets.

The most important consideration is Securely Connect, Extract, and Manage Data that feeds into the Machine learning algorithms keeping the motions of machine comprehension churning thereby building manufacturing values, that are questioning beliefs that have stood the test of time ever since the first Industrial revolution, be it the OEE or the ability to go zero defect on production quality.

In an increasingly complex & competitive world, where the key differentiator is Agility, Automation & Adaptability to market demands, the defining impact is being driven by Algorithms, Applications and Analytics’ Platforms. AI is at the core of enabling organizations in enhancing product quality, securing & optimizing manufacturing operations, and enabling disruptive business models.