What Is AI in Manufacturing?
Artificial intelligence (AI) in manufacturing uses machine learning and deep learning algorithms to analyze large disparate datasets for patterns. AI can then act on that data to complete tasks, automate processes, or provide insights that manufacturers can use to benefit their business.
While AI can be used in many ways, some of the most common applications in manufacturing include factory automation, including scheduling and resource management, intelligent operations and management, quality and process monitoring, supply chain optimization, and data-driven decision-making.
Benefits of AI in Manufacturing
The benefits of using AI in manufacturing are numerous.
Improved Safety, Efficiency, and Performance
AI-enabled systems and devices are helping manufacturers to:
- Optimize production processes to increase throughput
- Monitor equipment to ensure optimal operation
- Predict maintenance needs to maximize uptime
- Automate repetitive or hazardous tasks to improve productivity, safety, and employee satisfaction
For example, AI-powered robots can be used to handle dirty, repetitive, or dangerous tasks to improve human safety and productivity.
AI-enabled video systems can monitor production environments for potentially hazardous conditions or to identify unauthorized access to restricted areas to prevent potential mishaps.
AI-based systems can monitor energy and materials usage and provide system or workflow adjustments to help reduce waste and improve energy efficiency, which also contributes to sustainability initiatives.
Consolidated Systems and Workloads
Connected manufacturing systems run smarter and more efficiently by consolidating and leveraging data from information technology (IT) systems and operational technology (OT) appliances into one converged platform.
Not only does this convergence help centralize management, simplify operations, and lower costs, but AI can then take advantage of the converged data to bring even greater intelligence to automated systems for higher-quality monitoring and alerting, higher levels of optimization, and greater cross-system analysis and reporting for better decision-making.
Improved Product Design and Testing
AI enables industrial designers to use computer-aided modeling, simulation, and engineering, including digital twins and physics-based AI to move more quickly from ideation through prototyping into production. Customer data and preferences can also be incorporated into the design process, sparking innovation and accelerating concept development.
Effective Supply Chain Management
By combining data analytics with machine learning and computer vision technologies, manufacturers can project market trends, assess potential risks, and use transportation logistics to develop hypothetical scenarios for managing the supply chain effectively.
AI-based systems can also help manufacturers ensure their supply chain is resilient, responsive, and customer centric through advanced data analysis. For example, AI can help with demand forecasting, materials tracking, or planned downtime based on current equipment usage and production schedules.
AI in Manufacturing Use Cases
The use cases for AI in manufacturing can be grouped into three main categories: factory automation, process automation, and improved product and customer experience.
Factory Automation
Manufacturers are moving into more fully automated production facilities using various types of robots. Autonomous mobile robots (AMRs), automated guided vehicles (AGVs), articulated robots—such as robotic arms, and collaborative robots that help humans do their jobs, also called cobots, are deployed on factory floors and in warehouses to help expedite processes, drive efficiency, and promote safety. They’re used across a variety of applications, including welding, assembly, materials transportation, and warehouse security. Other types of AI in manufacturing can support optimized uptime, demand forecasting, efficiency-loss forecasting, waste reduction, energy resources monitoring and management, and situational monitoring for disruptive patterns or activities.
It’s estimated that almost every factory loses at least 5 percent of productivity, with some experiencing as much as a 20 percent loss due to downtime. 1 Unplanned machine downtime costs manufacturers US$260,000 for every hour of lost production. 2
Process Automation
Using AI in process automation can increase production flexibility, reduce changeover time, and monitor machine conditions for predictive and routine maintenance. Assembly lines can be adjusted for speed, tasks, and accuracy to adapt to changing production demands. AI can also complete scenario drill-downs to project potential outcomes of process changes.
AI can also be used for quality inspections—during preproduction, production, preshipment, and at container loading and unloading—to guarantee product consistency and catch potential systemic discrepancies.
By using AI, manufacturers can optimize their operations, raw resources, delivery logistics, and assets with transparency and accountability. And AI can help with robotic process automation (RPA) for paperwork, like purchase orders, invoices, and quality control reports.
The American Society for Quality (ASQ) suggests that the cost of quality ranges between 15 to 20 percent of sales and can be as high as 40 percent in some organizations. 1
Improved Product and Customer Experience
Customers are the lifeblood of any business, including manufacturing. The most significant win is building relationships that retain loyal customers over time. AI in manufacturing can ensure consistency in customer experience and capture customer wants, needs, preferences, patterns, and history for future reference. This can guide predictive analytics in helping future product development, increased personalization, and current product improvements, all of which contribute to customer satisfaction and relationship-building.
AI-enabled chatbots and self-help portals can help increase customer support while reducing in-person calls to maximize employee time. Generative AI (GenAI) can be used to enhance customer experience by personalizing communications, marketing campaigns, and emails for greater engagement. AI-powered customer relationship management systems can streamline customer information capture and encourage cross-team collaboration for customer support.
The Future of AI in Manufacturing
Given the advances in technology, AI in manufacturing has the potential to touch nearly every aspect of production and operations, helping to make them more automated, intelligent, and efficient.
Some of the future opportunities to watch for when it comes to AI in manufacturing include:
- Augmenting human workforce capabilities—vs. replacing them—with cobots and AI-enabled tools to improve job safety, productivity, and satisfaction.
- Accelerating the time to market through AI, compressing the product development and production life cycles.
- Using AI, computer vision, and sensor data for spatial awareness to optimize plant floor layouts or to plan and visualize the design of a net new operation based on past operational data and unique specifications.
By embracing AI technologies that support industrial production, manufacturers can unlock new advantages from the line floor to final customers.