To get the most value from Industry 4.0, organizations must have IoT data capture technology, analyze that data with AI, and do it in real time.
The increasing sophistication of AI means that organizations of all sizes can much more easily use AI to solve complex, critical problems.
AI has played a pivotal role in navigating the particularly challenging landscape over the past year. Retailers have relied on AI to help them optimize order shipments, reimagining their stores as distribution hubs, and ensuring people can still receive products even during purchases. Online shopping paused. In the utility sector, AI has been increasingly deployed to keep the grid running by managing issues such as risks from the external environment or starting to prepare for events earlier. adverse weather. AI is also helping to build better from scratch. For example, AI analyzes and tracks requirements for complex engineering equipment used in non-failure scenarios like airplanes, ventilators, and space shuttles.
As AI is combined with other assistive technologies, we are starting to see some ramifications of the so-called Industry 4.0. When combined with IoT, AI can analyze sensor data and predict failures of industrial assets such as plant equipment, HVAC systems, and assembly lines. It can optimize the scheduling of asset work orders, analyze failure risks, and allow managers to prioritize repairs according to different criteria. Visual inspection is being used to detect manufacturing defects and help keep workers safe by analyzing real-time video feeds.
Cameras, beacons, and sensors can monitor a facility 24 hours a day, seven days a week. With the help of AI that can separate signal from noise, organizations can ensure no valuable insights are left behind and start automating increasingly complex parts of the manufacturing process and their fabrication. These Industry 4.0 building blocks are mature and enterprise-ready if companies invest in the necessary underlying digital infrastructure.
Unlock Industry 4.0 with Hybrid Cloud
AI and IoT are two of the key building blocks towards large-scale industrial automation, which is what we often mean when we talk about Industry 4.0. However, achieving any of the above applications at scale also poses new challenges that require a third building block: the hybrid cloud.
Think about the amount of data input on a factory floor, from IoT sensors that monitor temperature and utilization rates to cameras that capture visual data and monitor workplace safety. Extrapolate that over a larger organization with a number of different facilities, maybe even different types of facilities, and the amount of data processed will grow exponentially. The AI models needed to organize all the data became significantly more complex. And perhaps most importantly, time becomes an issue. A model that can tell you a month from now that employees are gathering in a certain aisle isn’t particularly helpful. To take advantage of predictive insights, it is necessary to be able to act on those insights immediately, that is, to be able to compute at the edge, where these insights are gathered.
These three components – the ability to collect and store huge and ever-changing amounts of data, the ability to run models or other software on that data, and to be able to do it wherever you want – love requires an infrastructure that spans from the edge to the data center and the cloud.
To function effectively, organizations need a seamless management plane across all infrastructure. The hybrid cloud supports this with a common container-based platform that can run across all infrastructure locations. It provides workload-based auto-scaling and the flexibility to run a platform on any cloud, public, private, or at the edge.
In the current context of Industry 4.0, hybrid cloud can connect the dots. It delivers the data, artificial intelligence, tools, and software employees need, wherever they need it. The more you facilitate people’s work, the more time, attention, and energy they can devote to solving more interesting, complex, and costly problems.
