From smart factories to fully automated supply chains, manufacturing businesses are eager to improve productivity and decrease costs by incorporating AI. From supply chain to production to logistics, warehousing and delivery; manufacturers are struggling with how to securely connect all the locations, data sources, devices and vehicles required to derive value from AI solutions. AI infrastructure for manufacturers requires a strong bond between cloud service providers, cloud interconnect providers and communication service providers (CSPs).
Currently those interconnections are being defined and configured for every manufacturer each time a data source is added. That creates a time-consuming and expensive problem when trying to capture data from IoT devices and sensors, moving vehicles and multiple data centers. AI infrastructure for manufacturing requires access to real-time data from IoT sensors and mobile sources. Capturing and transmitting that data halfway around the world to be processed by an AI engine doesn’t help a manufacturer react quickly to support a heavy equipment operator or factory robot. The AI infrastructure must be distributed such that processing can occur at the edge of the network and actions taken, with results and data contributed to a data center for future use.
Manufacturing Challenges AI Infrastructure at the Edge
Configuring AI infrastructure presents obvious requirements for security, reliability, flexibility and availability. Even manufacturers that aren’t operating factories 24 hours per day are sending and receiving data at all times and global partners require access day and night. While the local network for much of this access is wireless, there are instances in factories or warehouses where magnetic interference or location prevent or impede wireless connectivity. In those cases, CSPs install cable or fiber to support the location.
While most of the data collected ends up at a central location, manufacturers often require processing at the edge to enable rapid notification of critical or dangerous conditions to operators or maintenance personnel. Specifically, manufacturers collect data from:
- Equipment – Heavy construction equipment, tractors and factory robots all have numerous sensors that must be monitored. Analyzing this data using AI solutions and previously collected data enables operators to anticipate problems and schedule predictive maintenance before production and schedules are impacted. Data, problems and solutions need to be made available to both the operator and the business.
- Technicians – When a problem occurs, technicians in the field will query the processors in the machine using an application on a phone or tablet, take pictures, and with the help of AI, determine the problem and solution before they leave.
- Operations – With the help of AI, manufacturers will know when preventative maintenance is required and can order the necessary parts and schedule shutdowns based on the production schedule, availability of alternate equipment and staff. Specialized parts can be ordered from the field that meet the specific demands of that installation and environment. For example, a part for a wind turbine operating in the North Sea is different than a similar part for a wind turbine operating in an Iowa corn field.
- Logistics – Whether monitoring a security system or tracking bar codes, data collected inside a factory or warehouse needs to be available to AI solutions. That data must be aligned with other sources in a timely manner to ensure the right products are delivered to the right place in the right way at the right time. For example, AI that is tracking containers will notify operations that a container left on the tarmac in Dubai that must remain chilled needs to be moved to a refrigerated warehouse.
Secure transactions and the ability to integrate data from the field to the edge to multiple data centers operating anywhere in the world requires careful alignment of data centers, virtual connectivity providers and local CSPs. An integrated AI infrastructure solution for manufacturing aligns all of these elements to ensure seamless, reliable and secure operations.
Vultr AI Infrastructure for Manufacturing
AI infrastructure for manufacturing requires global presence to establish connectivity to data, resources and AI solutions from devices in the field, factory or vehicle. Adding the data centers at the edge of the network and diverse supplier or partner data centers worldwide is a challenge for any business and manufacturers have neither the time nor the resources to accomplish this complex integration on their own. CSPs aligned with Vultr and their Cloud Alliance partnership, including Console Connect, are in a unique position to deliver secure, sovereign AI infrastructure as a fully integrated service customized for manufacturing companies.
A fully integrated AI infrastructure stack from Vultr, Console Connect, and proven CSP network services results in a reliable and secure AI infrastructure configuration that is tuned to the needs of manufacturers. AI infrastructure connects smart factories, smart supply chain, smart vehicles, smart sensors and remote personnel to the full power of AI solutions.
Instead of a closed architecture with complex interdependencies, Vultr and its partners have implemented an open architecture that enables access to multiple cloud platforms and is customizable across all levels of the cloud stack from the infrastructure layer to the application layer. CSPs deliver secure, private networks to tie it all together. The Vultr solution readily integrates with CSP systems to enable billing, customer care and reporting to ensure that service level agreements (SLAs) are met.
The Vultr solution is powered by top-of-the-line NVIDIA GPUs and enables AI, IoT and wireless connections at the edge to improve response time, reduce latency and accelerate real-time decision making in always-on manufacturing environments. Vultr’s cloud infrastructure solution for manufacturing, optimized by NVIDIA GPUs like the GH200 Grace Hopper™ and H100, facilitate accelerated AI training and complex simulations, enhancing collaboration across distributed teams and speeding up the time-to-market for new products.