In this article, learn about:
The technologies driving modern manufacturing automation
How automation improves safety, efficiency, and production visibility
Real-world examples of automation in today’s manufacturing facilities
Automation in manufacturing is not a new process. In fact, manufacturing itself is automation at work. However, the role of automation in manufacturing has evolved throughout the decades. The most significant advancement in recent years has been the introduction of AI, driving more autonomous processes from planning to production.
In this article, we’ll dive into the technologies shaping modern manufacturing automation, explore how they are being used across industries to improve efficiency, safety, and decision-making through automation.
A Brief History of Automation in Manufacturing
Before we dive into the current role of automation in manufacturing, it’s helpful to understand how automation in manufacturing and the supply chain has evolved over time.
Key automation developments in the past 300+ years:
The 18th Century: The Industrial Revolution
The Industrial Revolution introduced machine production in textiles, coal, and iron industries.
Steam- and water-powered engines allowed significant productivity increases in formerly labor-intensive tasks.
Factories began to replace small-scale workshops and businesses.
The 19th Century: Industrial Expansion and Transportation Innovation
Mechanized looms, presses, and machine tools became more widespread, increasing production speed.
New transportation methods, like railroads and steamships, allowed for faster and longer transport of materials and finished goods.
The 20th Century: Robotics and Increased Automation
Ford’s assembly line revolutionized the speed of car production and created ripples through manufacturing.
Industrial robots made their way into manufacturing, replacing humans for repetitive or dangerous tasks like welding, lifting, painting, etc.
The computational revolution introduced programmable logic controllers (PLCs) that automated process control.
Semiconductor chips made electronics smaller, faster, and more reliable in manufacturing.
The 21st Century: An Explosion of Automation and AI
AI, machine learning, and advanced analytics allow manufacturers to optimize workflows and improve quality control.
The widespread implementation of IoT sensors and RFID technology provides real-time visibility into production, inventory, and supply chains.
Autonomous machines, collaborative robots (cobots), and additive manufacturing increase flexibility and reduce manual labor.
Related Reading: How manufacturers can eliminate the stress of frequent change
The Current State of Manufacturing Automation
Today, technologies such as Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), and advanced robotics are transforming manufacturing into a connected, data-driven environment. These advancements not only enhance efficiency, but also have the ability to quickly pinpoint bottlenecks, increase worker safety, reduce waste, and allow manufacturers to make faster, data-driven decisions.
Here’s a snapshot of automations at play in modern manufacturing automations:
Artificial Intelligence
AI has numerous applications in manufacturing, as you will see in the following examples. AI, in simple terms, is a form of computer science that builds machines meant to emulate human cognitive functions — like learning, making decisions, recognizing patterns, learning from data, etc.
In manufacturing, this capability allows for a variety of applications, like AI systems that monitor equipment performance in real time, detect defects during quality inspections, optimize production schedules, forecast demand, or identify potential equipment failures before they occur.
Industrial Internet of Things (IIoT)
While advanced robots and machines are an important part of manufacturing, ensuring that these systems, devices, and equipment can communicate with each other is where the Industrial Internet of Things (IIoT) comes into play.
So, what is IIoT? According to the Computer Science Resource Center, IIoT is defined as:
“The sensors, instruments, machines, and other devices that are networked together and use Internet connectivity to enhance industrial and manufacturing business processes and applications.”
In manufacturing, IIoT is an important driver of visibility, smart decision making, and increased efficiency. For example, one application is using IIoT to increase safety measures in a manufacturing facility. IIoT allows for real-time data collection across production lines, equipment, and facility systems. This allows for higher level of facility monitoring, helping manufacturers more quickly identify and mitigate safety risks, like broken machinery, misuse of equipment, gas leaks, or other environmental threats.
Computer Vision
Computer vision is a subset of AI technology that gives machines the ability to process and understand visual inputs, like videos or images. IBM outlines several functions that computer vision is capable of, including:
Image recognition, classification, segmentation, and generation
Object detection and tracking
Scene understanding
Facial recognition
Pose estimation
Optical character recognition
Visual inspection
For the manufacturing process, this allows for automation in areas like quality control, equipment monitoring, and workplace safety. For example, Ford implemented computer vision systems in its manufacturing line called AiTriz and MAIVS for inspection and quality control. AiTriz can identify millimeter-scale misalignments through videos, while MAIVS can process images to ensure vehicles are properly assembled.
Autonomous Robotics
Autonomous robots, are used throughout manufacturing to perform tasks, often without human intervention. These robots can vary significantly across function, size, mobility, intelligence, etc., depending on the needs of the manufacturer. Some of the benefits from investing in autonomous robotics include improved speed and efficiency, increased precision, and reduced risk of humans operating machinery.
A common use of autonomous robots is in the transportation of materials. Robots are capable of moving a variety of materials between production lines, warehouses, and storage areas, reducing the time employees must spend on manual material handling and repetitive tasks.
For example, Amazon uses more than one million robots throughout its operations — enabling products and packages to be sorted, lifted, and carried without human intervention. These robots navigate using sensors and mapping technology to safely move products/packages.
Collaborative Robots (Cobots)
Collaborative robots (also called cobots) are robots specifically designed to work alongside humans. Unlike traditional industrial robots that typically operate behind safety barriers, cobots are equipped with sensors and safety features that allow them to detect human presence and adjust their movements accordingly. This makes them well suited for tasks that require a combination of human judgment and robotic precision.
The BMW Group uses collaborative robots on its vehicle assembly lines to assist workers with installing components inside of the doors of BMW X3s. These cobots work side-by-side with employees to improve efficiency and reduce the demand of a highly precise, labor-intensive task.
Predictive Maintenance Systems
Predictive maintenance has become a major player in modern manufacturing operations. Rather than scheduling maintenance or reacting to equipment failures after the fact, predictive maintenance systems use sensors, data analytics, and machine learning to monitor equipment. This allows for more thorough performance monitoring and the early detection of issues.
In manufacturing environments, this technology is particularly useful for:
Preventing costly delays from equipment downtime
Increasing efficiency by optimizing the time and resources that need to be spent on maintenance
Creating a safer workplace by identifying equipment issues before they lead to accidents, leaks, or hazardous operating conditions
For example, Shell implemented an AI-powered predictive maintenance system to monitor more than 10,000 pieces of critical equipment across its global operations, including pumps, compressors, and control valves. In an interview of Shell’s Vice President of Computational Science & Digital Innovation in 2022, the executive shared that their investment into predictive maintenance not only paid for itself in cost savings, but also offered significant environmental and workplace safety benefits.
Digital Twins
A digital twin is a virtual representation of a physical object, system, or process that uses real-time data to simulate performance and behavior. This technology allows manufacturers to test changes, predict outcomes, and optimize operations without disrupting actual production.
Digital twin technology has a wide range of applications in manufacturing, but a few examples include:
Production process optimization
Equipment monitoring and predictive maintenance
Product design and testing
Supply chain and facility planning
Related Reading: A Supplier's Guide to Manufacturing
SPS for Manufacturers
The manufacturing industry is ever-changing, with new technologies, supply chain disruptions, and evolving customer expectations shaping how products are produced and delivered. SPS can help you simplify your supply chain operations by automating workflows, improving communication, and providing real-time visibility into orders, shipments, and inventory.
