Manufacturing technology, in the broad sense, refers to tools, machines or systems used in the production of goods. In today’s context, the term covers a range of technologies that guide manufacturers through a digital transformation — from AI-powered, cloud-connected devices to robot-filled, sensor-rich smart factories.
Manufacturing Technology Definition
Manufacturing technology refers to novel tools and processes used in contemporary industrial production practices, including artificial intelligence, IoT, cloud computing, 3D printing and data analytics.
What Is Manufacturing Technology?
Manufacturing technology refers to modern tools and processes that drive industrial production. Today, machines are slowly converging with emerging tech — namely advanced sensors, AI-led data analysis and automation systems — which is fundamentally changing how goods are made. This era of intelligent, interconnected machines and platforms is creating connected, data-driven ecosystems, and represents a renaissance in manufacturing, commonly referred to as Industry 4.0.
Sensor-laden machines and IoT devices continuously monitor production processes and report changes in real time. Across cloud platforms, this data can then be shared, stored and accessed from anywhere, at any time. Some smart factories are run entirely by robots, requiring zero human oversight.
Ultimately, this new level of automation is designed to improve efficiency, reduce costs, enhance quality and enable more informed, “data-baked decisions” across the manufacturing lifecycle, as Bill Rokos, chief technology officer at Parsec Automation, puts it.
“These technologies,” Rokos added, “will help develop smart solutions for supply chain resilience, a digital ecosystem for circular sustainability and the industrial metaverse, where augmented and virtual reality applications are used in manufacturing facilities to help manufacturers understand how to improve their operations through AI-guided insights.”
Examples of Manufacturing Technology
Sensors
Sensors make possible real-time monitoring and data collection from machinery, production lines and environmental conditions. By providing critical information down to the minute, sensors are useful in building everything from battery cells to vehicle assembly.
For example, General Electric extensively uses sensors in its aviation division to monitor the performance of jet engines down to the minute, said Prady Gupta, materials scientist and founder of Infinita Lab. “Earlier, it was all done manually or with few sensors,” Gupta said. “Now, all the guesswork is gone.”
AI and Machine Learning
AI and machine learning are used for predictive maintenance, automating quality control and enabling smarter decision-making across the manufacturing lifecycle by analyzing vast amounts of data. AI-powered visual inspection systems, for example, use computer vision and machine learning algorithms to automatically detect flaws or anomalies in products on the assembly line.
Smart Factories
A smart factory is a highly digitized and connected manufacturing facility that continuously shares and collects data, learning as it goes. An example of a smart factory that combines the use of IoT, AI, robotics and automation is Amazon’s smart warehouses.
“Everyone is touched in some way by the automation Amazon has brought in,” Gupta said. The company’s fulfillment centers use heavy-lifting, Roomba-like robots to deliver entire shelves of products to stationary human pickers for sorting, “drastically increasing efficiency and accuracy and reducing the time it takes to process and ship orders.” Warehouses can now hold 50 percent more stock while being able to retrieve orders three times faster, reducing the cost of fulfillment by 40 percent, according to Tech Vision.
Robotics and Automation
Robotics and automation refer to the use of programmable machines and control systems to autonomously perform repetitive, precise or complex tasks — such assembly, welding, packaging or material handling — without human intervention. In manufacturing, these technologies boost productivity and safety by automating hazardous tasks, reducing errors and increasing throughput. Take, for instance, lights-out factories — or fully automated facilities made up entirely of robots building other robots — that have been in operation since 2001.
Internet of Things
The internet of things, often shorthanded to IoT, refers to a network of interconnected physical devices that “talk” to one another through a continuous stream of transmitted data. Using sensors, they collect, exchange and analyze data in real time. This is leveraged in manufacturing to monitor performance of machinery and production processes while also enabling predictive maintenance of equipment.
Cloud Computing
Cloud computing delivers computing services — such as storage, processing power and software — over the internet. It is used to streamline manufacturing operations by enabling real-time data sharing across facilities, and may be “the most consequential change” for Industry 4.0 that has led to the true marriage of cyber and physical systems, according to Arjun Chandar, CEO and founder of smart factory platform IndustrialML. This feature benefits supply chains and stakeholder collaboration through a centralized platform, while ensuring compatibility with emerging IoT and AI technologies.
3D Printing
3D printing, or additive manufacturing, is a process that creates three-dimensional objects layer by layer from a digital design. It’s used in processes like rapid prototyping or to iterate complex or customized parts that are otherwise impossible to generate using conventional manufacturing methods. Metal parts, for example, are no longer limited to pouring molten metal into a sand cast, but can be reproduced in powdered form through powder bed fusion or extruded from filament mixtures. 3D printing methods often reduce material waste while offering design flexibility and cost-efficiency.
Computer Numerical Control (CNC) Machining
Computer numerical control machining is a manufacturing process that uses pre-programmed computer software to direct the movement of machinery and tools to shape and cut materials with high precision. Industries like aerospace, automotive and electronics use this method to quickly produce complex parts with extreme accuracy.
Digital Twin
A digital twin is a dynamic virtual model that mirrors a real-world physical asset, system or process. For accuracy, it uses real-time data to simulate and predict behavior. By continuously taking in and analyzing data lifted from its physical counterparts, digital twins provide insight into production performance and can even spot potential issues before they occur.
Augmented Reality
Augmented reality overlays digital information, such as images or data, onto real-world environments through a device. Using AR-enabled smartphones, tablets and smart glasses, frontline workers can follow real-time visual guidance during assembly or remote troubleshooting, with 3D models or step-by-step instructions projected directly into their user's field of view. High-tech companies like Airbus and BMW use AR-wearable headsets on their assembly lines to assist with complex.
Advantages of Technology in Manufacturing
New-age technology of Industry 4.0 introduces several key advantages to the manufacturing sector.
Increased Efficiency
Automating manufacturing and production processes with smart technology speeds up operations and reduces manual labor. To avoid operational disruptions, AI-powered systems keep track of routine checkups to keep equipment at peak performance. Additionally, computer-aided machinery and sophisticated robotic systems deliver precision and consistency on a new level, minimizing errors and waste while maximizing throughput.
Improved Product Quality
Precisely calibrated machinery and automated systems ensure consistent production at high accuracy. High-resolution sensors and real-time monitoring systems are able to detect and correct defects before a product makes it through production, reducing variability and errors. Thanks to advanced imaging and data analytics, a higher standard for rigorous quality control and testing can be set.
“Big data processing and software systems have not changed the core practices of companies in terms of what information is important to production, but they have greatly improved what can be tracked and how frequently it can be monitored,” Chandar explained.
Take for instance how computer vision, which uses algorithms to analyze parts in real time, makes it possible to get 100 percent quality inspection — versus manual inspection practices that are limited to a small sample size, inspecting maybe “one to two percent” of parts, Chandar estimated.
Predictive Maintenance
With the help of AI-based algorithms, predictive maintenance can identify equipment failures before they occur. This prevents unexpected downtime and costly repairs. To manufacturers, it’s a superpower that optimizes maintenance schedules, ensuring that interventions are performed only when necessary. Not only does predictive maintenance reduce operational costs, it also boosts reliability and extends the lifespan of machinery.
Cost Reduction
Manufacturing technology lowers operational costs, reduces labor expenses and minimizes downtime. Companies that adopt automation at scale may cut costs up to 30 percent within the first five years of implementation, according to McKinsey.
Safer Workplace
By delegating repetitive or hazardous tasks to machines, working conditions for humans greatly improve in terms of safety — preventing everything from back twisting strains to death. And companies benefit, too, reporting decreased costs related to accidents, such as medical expenses and lost productivity. Based on data sourced from OSHA, there’s evidence that facilities with integrated robots (counting 1.34 robots per 1,000 workers) reduced work-related injury rates by approximately 1.2 injuries per 100 full-time workers.
Faster Time-to-Market
Automation, advanced analytics and real-time data tracking are designed to reduce delays and inefficiencies throughout the production process. 3D printing and digital twin models enable faster product launches by quickly iterating new-and-improved designs through rapid prototyping. All of these tools allow companies to clue into market demands as they develop, gaining a competitive edge to capture market opportunities sooner.
Disadvantages of Technology in Manufacturing
As emerging technologies roll out, technical glitches are inevitable.
High Initial Costs
Purchasing advanced machinery and high-tech solutions can require significant capital expenditure upfront, which is often a barrier for small-to-medium-sized businesses. And that’s not including costs related to installation or training employees on how to actually use the tech. This is largely the cause for Industry 4.0’s slow rollout rate, delaying its widespread adoption, and potentially widens the gap between firms that can more easily afford to invest in digital transformation.
Complexity and Maintenance
Advanced systems can be complex to operate and maintain, requiring specialized skills and knowledge that’s in short supply. This factor can lead to higher maintenance costs and downtime, and will likely require ongoing training for staff in both the proper use and troubleshooting of the technology, offsetting its overall efficiency.
Job Loss and Displacement
Automation and robotics are replacing a number of manual labor jobs previously performed by humans, leading to unemployment, displacement and job loss; however, despite the hysteria, experts say this is more of a temporary growing pain as the nature of work transforms and humans are upskilled and reskilled into more fulfilling, lateral roles. In fact, the World Economic Forum estimates that Industry 4.0 technology will create at least 12 million more jobs than it destroys by 2025.
Dependence on Technology
Over-reliance on technology can create vulnerabilities, such as system failures or cybersecurity threats, that can lead to detrimental operational disruptions or even halt production entirely. It can also reduce a company's flexibility, making it harder to adapt to unexpected changes or interventions that require manual, non-automated solutions. Whether it’s paying out vendors for technical support or financial loss caused by downtime of a critical system failure, the cost may outweigh the benefit for some companies.
The Future of Manufacturing Technology
Manufacturing practices are transforming as we speak, albeit piece by piece and at a relatively slow pace. So while experts expect to see up to 80 percent of global manufacturers implement Industry 4.0 technologies over the next few years, companies notoriously overestimate their preparedness and rarely make it past the pilot phase. Instead, trends suggest that convergence will roll out in layers — one tool at a time — rather than a holistic approach with fully integrated systems.
“Within the industry, we’re still seeing a significant range of technology adoption, fluency and maturity between companies,” Rokos said. Even though a handful of industry leaders, like Tesla and Amazon, have already achieved a level of digital transformation, the majority of manufacturers are still deciding what’s best for their needs. “Many still rely on non-digital data collection, and are not yet ready to ingrain technology throughout their workflows.”
Aside from equipment-related innovations, the future of manufacturing technology must also consider its role in the cultural shift of workplace culture. As much as automation contributes to job loss and displacement, it’s also transforming how we work entirely while improving worker safety and satisfaction. To combat high turnover rates and a severe labor shortage, companies are developing novel ways to retain talent.
“Because manufacturing workforces are … [experiencing] turnover every three to five years instead of 10 to 20 years,” Chandar said, “the biggest industry-wide technology changes need to account for this.”
There’s a greater emphasis on knowledge sharing, training practices that shorten onboarding time and upskilling programs that encourage continuous, on-the-job learning. Skill gaps can be supplemented with digital, quick-reference tutorials on how to make parts or use equipment or even real-time communication delivered through AR-enhanced smart glasses.
In other words, the same technology that’s replacing jobs is also being used to create new ones while improving conditions for workers.
“Right now, AI, machine learning and IoT integrations may feel like a ‘nice to have,’” Rokos said. “Soon, they’ll be the standard.”
Frequently Asked Questions
What is an example of manufacturing technology?
Robotic automation, which deploys robots to assembly lines, is an example of manufacturing technology. Today, robots can efficiently perform tasks such as assembly, welding, inspection and packaging with high precision and at a fraction of the cost.
What new technology is being used in manufacturing?
Several tools are guiding manufacturers through their digital transformations, such as artificial intelligence and machine learning, internet of things, big data analytics, cloud computing, augmented reality and 3D printing to name a few.
