Over the past year, artificial intelligence (AI) has played a crucial role in addressing this challenge. Retailers are relying on AI to help them optimize order fulfillment, reimagine their stores as distribution centers, and ensure people receive products even when in-store shopping is disrupted. In the utilities sector, AI is increasingly being used to manage vegetation risks or prepare in advance for adverse weather events to keep the power grid running. AI is also helping to fundamentally build better systems. For example, AI analyzes and tracks the demand for complex engineering equipment used in no-failure scenarios such as aircraft, ventilators, and space shuttles.
When artificial intelligence (AI) is combined with other enabling technologies, we are beginning to see some of the consequences of what is known as Industry 4.0. When combined with the Internet of Things (IoT), AI can analyze sensor data to predict failures in industrial assets such as factory equipment, HVAC systems, and assembly lines. It can optimize the timelines of asset work orders, analyze failure risks, and allow managers to prioritize repairs under different criteria. Visual inspection is used to detect manufacturing defects and to aid worker safety by analyzing real-time video.
Cameras, beacons, and sensors can monitor a facility 24/7. With the help of artificial intelligence, businesses can sift through noise to ensure no valuable insights are missed and begin automating increasingly complex parts of manufacturing and production processes. These Industry 4.0 building blocks are mature enough to prepare businesses if they invest in the underlying digital infrastructure they need.
Unlock Industry 4.0 with Hybrid Cloud
Artificial intelligence and the Internet of Things are two key components of large-scale industrial automation, which is what we usually refer to when talking about Industry 4.0. However, to realize any of the above applications at scale, new challenges are introduced, requiring a third building block: hybrid cloud.
Consider the amount of data input into a factory floor, from IoT sensors tracking heat and occupancy to cameras collecting visual data and monitoring workplace safety. Extrapolating from large organizations with multiple (and potentially different) facilities, the amount of data to process grows exponentially. AI models that need to process all this data become increasingly complex. Perhaps most importantly, time becomes an issue. A model that tells you a month later that employees are crammed into a particular aisle isn't particularly useful. Leveraging predictive insights requires the ability to act immediately based on those insights, which means being able to compute at the edge of how those insights are collected.
These three components—capable of collecting and storing massive and changing volumes of data, capable of running models or other software on data, and capable of doing anything you want—require an infrastructure footprint extending from the edge to the data center and the cloud.
To improve efficiency, organizations need a seamless management plane across all infrastructure. Hybrid cloud facilitates this by providing a universal, container-based platform that can run across all infrastructure locations. It offers the ability to autoscale based on workloads and the flexibility to run the platform on any cloud—public, private, or edge.
In an Industry 4.0 environment, hybrid cloud connects these points. It makes the data, AI, tools, and software that employees need available anytime, anywhere. The easier you make people's jobs, the more time, attention, and ability they can dedicate to solving more interesting, complex, and costly problems.
