What Is Intelligent Manufacturing?

Intelligent manufacturing is ushering in the industry’s next breakthrough in digital transformation: taking factory operations from automated to autonomous. Many manufacturers have already laid the groundwork by implementing “smart manufacturing” technologies to improve their operations and automate repetitive processes. Intelligent manufacturing uses advanced artificial intelligence (AI) and machine learning (ML) techniques to further enhance manufacturing processes with the ability to adapt to changing circumstances on their own. This article describes the evolution of manufacturing, from low tech to smart to intelligent; the benefits of intelligent manufacturing benefits; and how various industries are putting it to work.

What Is Intelligent Manufacturing?

Intelligent manufacturing incorporates AI/ML in systems that are better at learning, adapting, recommending decisions and even taking autonomous action, all based on the data and patterns they observe. The leveling up from automated to autonomous is more than just a question of degree. Whereas most of today’s automated systems are limited to following a set of preprogrammed instructions and sometimes making recommendations, emerging autonomous manufacturing systems can make decisions and adjust some production or distribution processes without the need for human intervention.

This evolution of manufacturing revolves around the use of AI/ML but also draws on other advanced technologies, such as the Internet of Things (IoT), cloud computing and robotics. Already, some manufacturers are using rudimentary ML for pattern recognition and recommendations in smart manufacturing setups that feed real-time data from IoT devices into data analytics software.

Key Takeaways

• The digital transformation of manufacturing draws its power from AI/ML.
• Intelligent manufacturing promises benefits ranging from streamlined processes and supply chain resilience to autonomous operations.
• Companies just starting to explore intelligent manufacturing can gain valuable insights from pioneers in the field.

Intelligent Manufacturing Explained

According to a 2023 survey by the Manufacturing Leadership Council (MLC), 85% of manufacturers are either implementing or experimenting with AI/ML projects. Naturally, the extent of AI/ML adoption differs significantly across manufacturing companies of different types and sizes. Small and midsize manufacturers, for example, must balance the up-front costs of adopting intelligent technology against longer-term gains in other areas, such as reduced labor expenditures and improved resource utilization.

Still, up-front costs are dropping, with various vendors incorporating AI/ML into prebuilt, cloud-based tools for maintenance, control and operations. Subscribing to these services may make them more affordable than building smart or intelligent manufacturing infrastructure. Generative AI and drag-and-drop interfaces are also making smarter tools easier to use, reducing the cost of training workers.

• Intelligent Manufacturing vs. Smart Manufacturing

  "Smart manufacturing” is blurring into “intelligent manufacturing” as the industry evolves, so it’s understandable that the two terms are sometimes used interchangeably. This chart differentiates the two.

Core Components of Intelligent Manufacturing

A diversity of manufacturing processes across myriad industries, including food, technology and aeronautics, has spawned various approaches to smart and intelligent manufacturing — any one of which could involve a different combination of the following technologies.

• Artificial Intelligence (AI) and Machine Learning (ML)

  AI/ML is being integrated into most advanced manufacturing technologies. For instance, robots that drive along specific lanes or predetermined routes in a factory can instead use AI/ML to recognize patterns and anomalies in their environments and move about more freely, without causing accidents.

  AI/ML is also elevating data analysis, decision support and other capabilities in enterprise resource planning (ERP) systems and dedicated software suites used for managing production, warehouses and supply chains. In one example, new generative AI features are assisting production managers in the authoring of data entry and tracking activities, including scheduling, operational tasks and routing, as well as in creating consistent definitions for manufacturing cost templates to avoid errors.

  And with ML at their core, advanced AI techniques, such as deep learning, natural language processing, computer vision and causal AI, are also evolving to make manufacturing more intelligent. For instance, causal AI goes beyond ML’s capabilities to identify patterns and make recommendations with specialized algorithms that enable greater cause-and-effect reasoning.

• Advanced Robotics and Automation

  Large and small manufacturers use industrial, and often mobile, robots to save on production costs. Robots with hydraulic arms lifting heavy loads, driverless forklifts navigating with precise sensors, drones surveilling inventories and others perform tasks such as assembly, packaging, painting, welding and buffing.

  As they are upgraded to AI/ML, robots become increasingly capable of adapting to their environments and handling more types of materials and objects by using training data and learning from experience, instead of requiring complex, task-specific code. Managers can also issue instructions in human language.

  For example, objects that used to be impossible for robots to identify and manipulate are now within their grasp. One robot can pick out a can of tennis balls from a bin of mixed sports equipment. Another trained on 3D simulations can select a single raw chicken wing out of a slimy pile, despite the fact that such a scenario could involve an infinite number of variations in how the poultry is all jumbled together.

• Internet of Things (IoT) and Connectivity

  Another powerful driver of intelligent manufacturing comes at the intersection of two related developments: the quantum leap in data collection from industrial IoT devices worldwide that literally number in the billions (and growing) and the unprecedented levels of connectivity among those devices, enabled by cloud computing, fifth- and sixth-generation wireless networking (5G/6G) and the latest generations of Wi-Fi standards (6 and 6E). The wireless technologies, for example, provide high-speed, low-latency, energy-efficient transmissions that can enable more widespread use and new applications, such as autonomous robots and machine-vision systems for real-time product inspection.

• Big Data Analytics

  The vast amount of real-time data involved in intelligent manufacturing is only as valuable as an organization’s ability to process it. Big data analytics, when integrated with ERP systems, can crunch all the input from sensors embedded in machinery, production lines, quality-control systems and more to optimize the production process, reduce costs and improve operational efficiency. AI/ML-based data analytics enable predictive recommendations and the continuous self-learning needed for systems to become self-optimizing. In one example, AI was used to make sense of hours and hours of video footage from a factory floor, combining the visuals with other types of production data and signals to create a 3D map of people, tools, productivity and cycle time.

• Cyber-Physical Systems

  A concept underlying much of intelligent manufacturing together is cyber-physical systems. These are physical assets, such as a pump or compressor, augmented with computing and control capabilities, such as embedded sensors for data collection and actuators for physical manipulation, all interconnected through the IoT for improved monitoring and control.

• Additive Manufacturing (3D Printing)

  While not yet mainstream, additive manufacturing has been finding success in some manufacturing sectors, such as for creating lightweight parts for aircraft and manufacturing patient-tailored prosthetics. The rise of IoT, 5G networks, AI/ML and other technologies will also boost the uptake of additive manufacturing, as they improve the real-time monitoring, control and communications involved.

  One appeal of additive manufacturing is its fundamental 3D printing approach, which deposits layers of materials, such as plastic, to produce an object sourced from a digital file. This technique makes 3D printing less wasteful than traditional manufacturing methods that essentially carve a product out of a block of material. Additive manufacturing also allows for greater design flexibility, more customization and reduced lead times.

Benefits of Intelligent Manufacturing

While many U.S. manufacturers have only just begun to leverage the possibilities of smart and intelligent manufacturing, 96% expect to increase investment in the years ahead, according to MLC. The benefits they seek range from better decision-making, planning and cost savings to new business opportunities and increased competitive advantage. Some additional benefits are broken out below.

• Accelerated product development cycles: The use of “digital twins” can help companies develop products faster. These virtual replicas simulate all of the characteristics of a physical product, so production managers can test out different assembly methods and machine configurations more easily and quickly. Already an advanced technology, digital twins powered by AI/ML are expected to become even more capable of learning, identifying opportunities and offering suggestions to improve products and production.
• More resilient supply chain: Collecting and analyzing data in real time can minimize disruptions and defects — whether by “smart” applications that identify patterns and recommend fixes or in “intelligent” applications that probe more deeply and actually perform the needed adjustments.
• Reduced operational costs: From predictive maintenance that picks up on malfunctioning or failing equipment that could result in costly downtime, to data-driven decision-making based on advanced analytics, to AI/ML-powered simulations, intelligent manufacturing aims to reduce operational costs without sacrificing product quality and reliability.
• Improved worker safety: The more that robotics incorporates AI/ML, the better robots can respond to their environments and the wider the range of potentially dangerous tasks they can offload from human workers — keeping staff away from heavy-moving machinery, toxic chemicals and other risks, for example. Another important development has been the introduction of collaborative robots. These “cobots” are designed with safety features to work alongside humans without harming them, while also using natural language for human-machine communications in place of complex coding. In a third example, intelligent safety cameras can shut down equipment when observing employees who are without proper personal protective equipment.
• Minimized downtime: Predictive maintenance, mentioned above, represents one of the biggest AI/ML applications in manufacturing. Predictive maintenance systems use sensors to monitor a machine’s vibrations, temperature, pressure or sounds and combine such real-time data with AI/ML training data. As a result, the system can know when a ball bearing will wear out and predict a machine’s failure within a specific confidence level and time frame, reducing downtime. The next step? Prescriptive maintenance, which recommends what to do next. In some advanced applications, it might even integrate with automation systems to trigger specific actions.
• Empowered workforce: Amid concerns that intelligent manufacturing will replace workers, MLC argues that AI/ML should actually help fill persistent skills gaps in the industry. Conventional wisdom has it that replacing repetitive tasks in modern factories can free up workers to do more significant jobs. Indeed, more than a third of the manufacturers in the MLC survey said they expect to retrain or reassign over 10% of their workforce as they ramp up their use of AI/ML.
• Improved customer satisfaction: In the end, the smoother, more efficient operations of smart and intelligent factories translate into greater customer satisfaction, as production managers get a better handle on managing risk, running their operations reliably, controlling quality, lowering costs and leveraging other promised benefits of advanced technologies.

Intelligent Manufacturing Use Cases

By leveraging real-time data and automation, manufacturers can significantly improve efficiency, reduce costs, and enhance product quality. A revolution is taking place in the manufacturing industry, as demonstrated by the following examples occurring on today’s factory floors.

• Automotive Industry

  A luxury automobile manufacturer provides a twist on the “smart car.” Its autos actually communicate while in production, providing real-time information about their own assembly status, identifying assembly errors and reporting them. And cloud-based AI/ML innovations at all of the group’s plants automate and optimize processes. For quality control, for example, camera systems monitor conveyor belts, while sensors with acoustic analytics conduct audio-based quality checks to record, analyze and classify all driving noises.

• Aerospace and Defense

  One of the world’s top aerospace manufacturers prominently displays its commitment to AI and the “factory of the future” with a focus on six technical areas: knowledge extraction, computer vision, anomaly detection, conversational assistance, decision-making and autonomous flight. The company has created two advanced robots, which it describes as mobile and nimble, that use AI/ML while moving around an aircraft to drill into its fuselage with greater precision and consistency.

• Consumer Goods

  A global leader in consumer goods has implemented AI/ML and other intelligent manufacturing technologies across various facets of its business. One of its factories has implemented “lights-out production” that allows it to run manufacturing operations 24/7 with minimal human intervention. In a separate factory, the company uses digital twinning, in which AI/ML predicts the optimal process parameters for new laundry detergent formulations, eliminating the need for physical trials and speeding time to market. To reduce the factory’s distribution errors, caused by what the company estimated to be 600 daily decisions, 13 critical variables and constant changes, the company now uses an AI/ML algorithm to predict ideal allocations and routes using real-time data.

• Pharmaceuticals and Healthcare

  Pharmaceutical companies are using AI/ML for intelligent manufacturing, as well as in their better known applications for drug discovery. For instance, one leading drug company is employing AI/ML, robotics, computer vision and IoT edge computing to count drug cartridges — a seemingly simple but crucial requirement that previously required significant manual intervention. In its new workflow, a robotic arm places a box of cartridges on a platform, a camera takes images of the box, an IoT edge device equipped with AI/ML analyzes it, and results are displayed on a dashboard.

• Electronics and Semiconductors

  An intelligent manufacturing process run by a major semiconductor manufacturer aims to reduce waste and improve product quality with streamlined processes, such as in-line inspection, to catch defects and other issues much earlier than possible with off-line tests. High-resolution cameras take multiple images per second during production and can see defects that are undetectable to the human eye. The images are analyzed by an ML model on IoT edge controllers. If defects are found, the system can sound an alarm right there and then — or even stop production.

NetSuite: A Single, Unified Solution to Drive Manufacturing Excellence

NetSuite is increasingly incorporating AI/ML technologies throughout its flagship ERP system and range of specialized software suites, all designed to help manufacturers control, coordinate and manage all their operations in one place. NetSuite for Manufacturing can help manufacturers evolve to intelligent manufacturing, while also ensuring that production processes are connected back to financial reports, inventory management and outstanding orders in real time. The cloud-based solution also takes advantage of the recently announced NetSuite Text Enhance, which embeds generative AI capabilities to optimize the management of manufacturing operations.

Intelligent manufacturing represents the next leap in digital industrial transformation, elevating production from automated to autonomous. Building on the foundation of “smart manufacturing” with real-time data and automation, intelligent manufacturing leverages advanced AI/ML to further optimize processes and enable them to run and adapt to changing circumstances without human intervention. AI/ML is driving this evolution, transforming manufacturing from low tech to smart and, ultimately, intelligent.

Intelligent Manufacturing FAQs

What is the difference between smart and intelligent manufacturing?

“Smart manufacturing” is blurring into “intelligent manufacturing” as the industry evolves, so the two terms are sometimes used interchangeably. But key differences are becoming clear. Smart manufacturing implements technologies, such as artificial intelligence (AI) and machine learning (ML), using real-time data from Internet of Things sensors to improve their operations and run some repetitive processes automatically. Intelligent manufacturing uses more advanced AI/ML techniques and other technologies to further enhance manufacturing processes and make many of them run autonomously — that is, able to adapt to changing circumstances without human intervention.

What is an intelligent production system?

An intelligent production system uses advanced artificial intelligence techniques, such as deep learning, natural language processing and computer vision, along with core machine learning capabilities and in conjunction with other technologies, such as Internet of Things sensors. The result is an enhanced manufacturing process that may even run autonomously.

What are the elements of an intelligent manufacturing system?

Core components of intelligent manufacturing systems include sensors and other Internet of Things devices for data collection; enterprise resource planning systems and big data analytics software to analyze the data; and robotics to perform functions on the production line. Increasingly, artificial intelligence and machine learning are built into these elements and used in managing them overall.

What is meant by smart manufacturing?

Smart manufacturing leverages and integrates advanced technologies to automate, improve and optimize production processes. Enabling tech includes the industrial Internet of Things, sensors, cloud computing, artificial intelligence and robotics.