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By Debra Slapak, Dorian Gast
When I was a child, just as the breeze started to cool at the end of a long, simmering summer, the mail carrier would deliver a thick catalog encased in brown paper that concealed the small wonders of the world. I would carefully extract pages for my mom, wishing for gifts that might have come by sled from the North Pole, given how far in advance she had to plan for my winter birthday. How the world has changed! We’ve grown to expect instant gratification. This expectation–set against shifting global trends, surges in demand, and sometimes rapidly changing conditions–requires that manufacturers be nimbler and more resilient than ever.
There’s no time for downtime. To thrive in today’s fast-moving markets, manufacturers need to optimize everything under their control — from supply chains, materials, and order management to production processes, factory maintenance, order fulfillment, logistics, and services. To enable this level of optimization, manufacturers need visibility into the end-to-end production environment, including supply chains and distribution processes, as well as operations on the factory floor.
These are among the challenges that manufacturers can solve by using artificial intelligence (AI) to help drive process improvements. AI-driven applications and systems leverage massive amounts of data captured by sensors and devices spread across the end-to-end production environment. This combination of AI and data from the Industrial Internet of Things (IIoT) enables a wide range of use cases that solve critical challenges in manufacturing.
With AI, intelligent systems can take immediate actions to optimize everything from production monitoring and control to machinery performance and maintenance, to the management of supply chains, logistics, and factory security. These intelligent systems are essential to success in the new era of digitally-driven smart manufacturing — the Fourth Industrial Revolution.
AI use case examples
Here are some of the specific ways in which AI-driven applications help manufacturers address challenges.
Real-time location services
Real-time location services enable plant operators to see where materials, parts, and products are. With the right AI-driven technologies in place, manufacturers can gain an end-to-end view of the production process that extends from the materials and parts in the supply chain, through the factory, and on to the distribution processes that take products to distributors and end users.
Real-time transparency of material flow is enabled with systems and software that can access all relevant data sources required to show the amount of material and the status of product processing.
An AI-enabled integrated production dashboard gives operators a consolidated view of the materials flow, including the current location of all parts and components in the production process.
In the factory, all products and parts are automatically scanned, from the point where raw materials land on the receiving dock to where the produced goods are loaded onto outbound trucks.
In times of supply-chain disruptions and rapidly changing demand for products, the integrated production dashboard gives manufacturers the view they need to dynamically throttle or accelerate production processes. This wouldn’t be possible without AI and solutions that integrate IIoT data from systems throughout the manufacturing environment.
Automated guided vehicles
An automated guided vehicle (AGV) is a type of portable robot used in factories, warehouses, and other industrial environments to transport goods without the help of a human being. The robot follows marked lines or wires on the floor or uses radio waves, vision cameras, magnets or lasers for navigation.
Here’s an example. OTTO Motors creates autonomous vehicles that build maps of their environment and move independently around warehouses, manufacturing plants, and other industrial sites, doing work that frees humans to perform higher-value tasks. With the power of AI, the Canadian manufacturer is making it possible for companies of all sizes to adopt self-driving vehicles in their work environments.i
AI-driven applications enable automated repairs and upgrades delivered by remote operators for both the equipment within the production process and the finished products when they are in use.
Manufacturers can use smart systems in conjunction with IIoT data from sensors to prevent the failure of heavy machinery and shop floor vehicles by doing predictive maintenance.
Beyond the factory, a manufacturer might automatically pull data from IoT sensors in its in-use products to identify the need for physical maintenance or software upgrades. With this capability, a manufacturer sells not only cars, appliances, or computers, but also future uptime and ongoing performance improvements.
Supplier applications for production machines
In the smart factory, AI-driven applications provided by machine manufacturers are integrated into OEM’s production and process planning to reduce unexpected downtime with maintenance. Manufacturers work with machine suppliers to capture data from the machines and pull it into their manufacturing execution system (MES) and supervisory control and data acquisition (SCADA) systems. These capabilities help operators gain greater control over the operation, performance, and maintenance of the machines used in the production process.
Considerations for capitalizing on AI
As diverse as they are, all these AI use cases often lead to a common set of challenges for IT and OT leaders. To understand and address the challenges organizations face, I turned to a subject matter expert, Dorian Gast, the Dell Technologies business development manager for IoT in Germany. He outlines considerations for manufacturing organizations that want to capitalize more fully on their IIoT data and the power of AI.
Bring IT and OT together.
“To gain greater insights from AI systems, you need to bring together data from IT and OT — information technology and operations technology,” Dorian says. “By doing so, you can gain visibility and transparency across the manufacturing environment.”
The goal, he says, is to integrate IT and OT into one interconnected, open ecosystem. Doing so simplifies the IIoT solution. It makes it easier to achieve a unified view of production, maintenance, and delivery data, enabling organizations to manage and monitor complex processes and turn massive amounts of data into actionable insights that save time and money.
Leverage analytics at the edge.
Dorian advises manufacturers to automate data collection and move targeted data processing and analytics workloads closer to where data is generated. The goal is to use analytics at the edge and analytics in the cloud to get insights exactly where and when you need them.
“When sensors detect a piece of equipment that is on the verge of failing, or when a security application detects a threat, plant operators need to react to that information immediately,” Dorian says. “These are examples of cases where data should be processed at the edge. There’s no time to send all of the data to a remote analytics system.”
Choose the right partners.
“Work with technology partners who can help you solve the full spectrum of your smart manufacturing challenges, from data capture and integration to process automation and production control,” Dorian says. “Use IIoT and embedded technologies to connect machines, improve efficiency, and gain greater control over manufacturing processes.”
Start small, and then scale up.
Don’t overreach. Look for solutions and technology partners that enable you to start small and scale your solutions to grow with your evolving needs and skillsets.
“To jumpstart your smart manufacturing program, use open source technology or fully supported systems to set up proofs of concept (PoCs),” Dorian advises. “Try out as much as possible and adjust your strategy to become digital across all business units.”
At the same time, Dorian advises against too much caution, which can impede your path forward.
“Don’t wait until the competition has proven that a new business model works,” he says. “In the highly competitive world of manufacturing, it’s always better to be first— be a leader, not a follower.”
Don’t forget the big picture
The shift to AI-driven smart manufacturing processes requires rethinking past practices. With that in mind, Dorian advises against focusing on technology first.
“Start with changing the minds of people in your company, instead of talking about the technology,” he says. “Business leaders need to understand the business value and how smart manufacturing will make the company more efficient, agile, and more competitive.”
The technologies to power the factory of the future are here today. To increase productivity and gain greater control over the factory, manufacturers need to capitalize more fully on AI, machine learning, IIoT, and other emerging technologies. In this new era, the AI-driven factory is the enabler for flexible and sustainable production — flexible in terms of produced goods and sustainable in terms of costs and ecological key performance indicators.
Those who get started early down the path to smart manufacturing stand to gain a competitive advantage.