Picture in your mind what automation in manufacturing looks like: Maybe you see a stark, gray scene, with pistons going up and down as a conveyor belt churns out the same product over and over. Or perhaps it’s a vision of the Rube Goldberg–esque machines of Willy Wonka’s colorful chocolate factory that produce an endless amount of sweets. Whatever image you conjure, the benefits of automation in manufacturing are universal: greater efficiency, higher productivity, and humans performing value-add tasks for better business outcomes.
Automation in manufacturing industries is the use of machines and software to automate production and business processes in a manufacturing operation. The purpose of automation is to hand over dirty, dangerous, or dull tasks to technology. Automation is not about eliminating people; instead, it eliminates repetitive tasks and shifts human brainpower over to more creative, high-value-added tasks that lead to business growth.
Although automation might happen in stages, the ultimate goal is to automate and improve the entire operation, creating a continuous journey of information that travels from engineering all the way to postproduction and maintenance—and eventually back to design. This connected data improves and optimizes the entire process of turning a virtual design into a physical product ready to ship to your customer.
Automation in manufacturing is not just robots on an assembly line handling mechanical processes, though that is part of it. Companies automate production and processes in order to:
Eliminate silos, empower the information flow, and connect everyone
Increase throughput and, with that, increase return of capital employed (ROCE)
Eliminate downtime
Reduce errors and rework
Free up humans for value-add and creative tasks
Create a closed data loop for full visibility and feedback
Be more agile
Enable mass customization
Support lean operations
Save money and reduce unit costs
Ever since humans have been on the scene, they’ve been inventing ways to hand over physical labor to machines for faster, more efficient work. Case in point: the water wheel. Some highlights of the automation-in-manufacturing timeline include:
The Industrial Revolution in the 18th and 19th centuries introduced new power sources, such as coal, oil, gas, and steam. Also, new high-tech (for then) equipment emerged, such as the steam engine, the spinning jenny, and the sewing machine.
In 1913, Henry Ford introduced the assembly line to the automotive industry (an idea he got from visiting the efficient workflow of a meat-processing plant) and a moving conveyor belt that brought cars to the workers at each station instead of the other way around. Suddenly, it took only 90 minutes to build a car instead of 12 hours.
Manufacturers were reluctant at first to switch from steam to electric motors to power their factories in the early 1900s, but once they did, they were more productive. By the 1920s, manufacturers had increased output by 30%.
In the 1950s and ’60s, automation replaced so many workers in the United States that the government slogan was, “You won’t get tomorrow’s jobs with yesterday’s skills.” Robots (PDF, p. 1) entered the factory and electronic data processing (EDP) emerged and began to automate business processes and information management.
In the past, automation was something you could predict. You set up a mechanical process that followed sequential steps and ended up with the same result. Then, a crazy thing happened: Customers started to have a say. They wanted to customize their cars and choose their own color. (Ford famously said customers can order the car in any color as long as it was black.)
Automation has come a long way, and humans are an integral part of the equation, despite long-held fears that they would be replaced completely. Cars are customized by order and companies can build different models on the same line thanks to flexible manufacturing and automation. (Even Ford now uses robots and artificial intelligence [AI].) Changes are happening fast, and companies need to be much more agile to be able to react quickly, enable mass customization, and adapt to shifting supply chains. But unlocked potential remains—moving forward, automation will be centered around data.
The more a manufacturing operation hands the reins to machines and software, the more it realizes the benefits of automation in manufacturing. Some of these benefits include:
Humans are the greatest asset of your operation. Automating mundane tasks allows you to pivot workers to more creative, high-value-add tasks. Unlocking that capacity means more innovation power as humans move to new roles, becoming data scientists, developers, and engineers.
When silos are eliminated, what’s left is a river of connected data streamlining the entire manufacturing process and supply chain. With centralized information, there’s knowledge continuity. People no longer need to search through paper records and folders to get the information they need, wasting time and money.
Machines and technology are programmed to perform repeatable tasks, and they do it much more consistently than their human counterparts. Machines don’t suffer from boredom or fatigue. They make fewer, if any, errors. This reliability and predictability support compliance and deliver a known product to customers.
Automation streamlines workflows for less rework and less wasted material, creating a leaner operation.
Machines can run around the clock, unlike their human counterparts who need breaks, food, and to go home at the end of the day. Automation also enables predictive maintenance, which reduces the risk of disruptions from equipment failures.
Manufacturing has long been a dangerous job for production workers. With automation, robots and machines handle more of the hazardous production tasks, reducing accident rates.
As manufacturing faces an ongoing labor shortage due to an aging and retiring workforce and lack of incoming workers, accelerating automation to handle those vacant production jobs can take the pressure off while companies recruit talent for the higher-skilled jobs needed.
Automation generates heaps of insights that enable humans to make data-driven decisions to optimize operations. Technology can also improve processes. Forty-two percent of organizations worldwide are putting money toward AI and machine learning, which allows computers to harness their own power to recognize patterns and constantly improve their own performance.
Automating business processes with industry-specific software can ensure compliance with manufacturing regulatory agencies.
Companies that automate their manufacturing processes are more responsive to customer and market demands, more flexible in the face of disruption, and more resilient.
The sum of all of these benefits? Greater productivity and efficiency, a more competitive standing in the marketplace, and a better bottom line.
Historically, manufacturing has been a linear process: ideate, build, sell. Information is siloed and disconnected and dead ends when the product is shipped. Any disruption along the way halts production, requires human intervention, and often leads to wasteful rework. Think of the classic scenario of the old newspaper printing press: If one component didn’t perform well, the whole run was ruined.
A first-tier goal of automation is to bend that horizontal workflow into a closed loop where data is connected and moves continuously from design to engineering to production to the customer and back to design. All of the data that machines and software generate is centralized instead of siloed and accessible across disciplines. But the real defining element of closed-loop manufacturing is feedback: At the end of production, information can travel upstream to inform and improve the next cycle and the quality of the finished product for a self-optimizing workflow.
The benefits of creating a closed-loop data flow include:
Deeper, actionable insights
Cross-functional collaboration
Constant learning and improvement
Mass customization
The ability to change production based on market requirements
Better competitiveness, responsiveness, and agility
Less waste
Better products
Shorter run times
Closed-loop feedback starts internally but can extend beyond the walls of the manufacturing facility, looping in suppliers so engineers can source materials and check pricing to make the best decisions. That connectivity can go all the way down the value chain from design to delivery. A closed-loop operation consolidates information so everybody has the same view and access to continually updated information when they need it.
Automation generates volumes of data from multiple sources, and that data is most powerful when it’s connected and open. When businesses connect, standardize, and automate their systems, they converge those multiple data sources. That leads to enterprise-wide alignment and creates consistency. Different people, different disciplines, and different systems are in sync, communicating and collaborating from a single source of information that is constantly updated for uninterrupted lifecycles throughout the operation.
Here are five lifecycles that benefit from a closed-loop system. They all need to come together to harness the power of automation and achieve operational excellence.
Automation in manufacturing is much more than programming robots to build things. It also includes streamlining the behind-the-scenes workflows of running a company. From purchase orders to HR functions, business process automation shifts manual functions to software, which frees up capacity, increases efficiency, and creates consistency. Think: HR automates payroll and scheduling to focus on recruiting and retention.
The benefits of business process automation include:
Connecting different systems for faster throughput of a particular process
Consolidating data so multiple people can work off of the same information
Automatically providing deliverables so the next-in-line process can continue working on the task.
Identifying duplicate work to reduce waste and eliminate cost overruns
Better inventory management that supports just-in-time manufacturing
Designing, building, and distributing a product was once a siloed, stop-start process with difficulty handing a project off from design to make. But with automation, product lifecycle management is a continuous journey with data seamlessly handed off from one discipline to the next. Centralized data enables enterprise-wide collaboration for everyone contributing to the different stages of the product lifecycle—from design and engineering to manufacturing and distribution—and accelerates time to market.
Product data management (PDM) automates the design and engineering process and keeps everyone working from a central source of organized data. Software, like Autodesk Vault, increases collaboration across engineering, manufacturing, and extended teams. It tracks designs and revisions and enables collaboration between internal and external teams for smoother, faster workflows.
For example, if an engineer develops a 3D model of a product, there is a tremendous amount of information in that data set. That typically gets lost during the transition from engineering to manufacturing into production and maintenance. Product data management facilitates the reuse of that information instead of re-creating it over and over, which is a big first step of automation.
Just like data has the power to optimize internal operations, it can do the same for the customer journey. Leveraging data at each stage of the customer lifecycle—from reach to retention—enables a customized, deliberate interaction at every touchpoint. Having a digital communication channel that collects data helps you see where the customer is in the buying process, own the customer interface, and create customer intimacy.
Digital channels of communication (a chat feature or self-service portal, for example) enable you to handle 100 different inquiries from 100 different customers and create intimacy with each one. This automation lets you personalize the customer lifecycle, create a frictionless experience, build loyalty, and stimulate growth. And after the pandemic, customer experience has become a priority for the manufacturing industry.
As you automate your manufacturing facility and processes, it’s critical to create a roadmap for the lifecycle of the hardware and software you’re investing in. In other words: technology lifecycle management, which means from asset acquisition to asset disposal.
For asset acquisition, it’s important to look at what you make, who you’re making it for, and the materials you use to determine the best technology for your operation. For example, if you’re building sports cars, you might work with carbon fiber because it’s strong yet lightweight. But if you’re building consumer cars, you’ll most likely be working with steel and aluminum. Different technologies are appropriate for different customers and different designs. For an informed decision, ask questions like:
What technology will put you in the best position to be a competitive player in your market?
What equipment do you need to handle the materials you’re working with? If regulations change and you need to swap out materials for sustainability reasons, can your technology handle a switch of design, production methods, or suppliers?
What are your plans for upgrading and disposing of older technology as new technology becomes available?
In an automated, closed-loop operation, you have greater visibility into the functionality of your technology infrastructure and insight into how to best manage its lifecycle.
While there are many benefits of automation in manufacturing, there are a few challenges and trade-offs companies should anticipate.
Automation in manufacturing is a case-by-case decision. Companies need to weigh the pros and cons to assess if it’s economically feasible based on their specific needs, even if it technically makes sense to automate. For example, a manufacturer in a country with lower labor costs might not jump at the chance to lay out a big investment in new technology and machines. But if labor costs increase and you’re forced to outsource, at a certain point, you need to automate to keep work in your country.
Companies also need to take easily disrupted supply chains into account. It doesn’t help if you have cheap labor if your ship is stuck in a harbor in another part of the world. Manufacturers need to think about how to balance on-shoring, near-shoring, and cost of labor to determine if and when automation is needed.
The nuts and bolts of automating an operation can create logistical challenges that require proper planning to avoid. If the effort is too big, companies will shy away from it. First, there will be a disruption as you automate each process, because you can’t change the wheels while you’re flying. You have to figure out a few things like:
How do I connect the different data flows?
How do I connect the different projects and products?
How do I standardize the data exchange between the different systems and disciplines?
Automation also generates dependency, which is part of the beauty of it; the open flow of information between connected machines and programs creates a high-functioning manufacturing ecosystem. But when tasks depend on each other, one small break in the process can lead to a system-wide failure.
While automation will have your production line humming along faster and more efficiently than ever, it also means humans will be displaced, and you’ll need to restructure your workforce into more cognitive areas. You’ll need to hire skilled workers or invest in upskilling your current workforce.
One of the upsides of automation is that workplace technology holds an appeal for many job seekers looking for more digitally progressive employers. But finding top talent can be difficult in today’s labor market—in fact, there could be as many as 2.1 million unfilled manufacturing positions by 2030. Reorganizing and upskilling your current workforce will be necessary investments for successful automation.
The principles of automation are steadfast, but the application of those principles varies by industry and by company, depending on specific needs. Here are some examples of automation in manufacturing.
Founded in 1881, GEA is one of the world’s leading producers of equipment, systems, and processing solutions for manufacturers, particularly in the food and beverage industry. Although the company offers a line of standard products, it also custom designs equipment. Each order might be unique to a customer’s needs, but the primary components—like the compressors, pumps, and valves—are the same on each unit. To create and customize configurations using standard components, GEA uses Autodesk Inventor to automate the design of custom machines and Vault to automate engineering tasks and deliverables. The company has reduced engineering time by up to 80% and can create new designs in hours instead of weeks.
ANDRITZ is an Austrian manufacturer of paper mill equipment. But the company was buried under mountains of paper until it digitalized its planning and design process. With automated business processes, ANDRITZ has streamlined workflows and made information, data, and designs equally accessible across the value chain by using Autodesk Forge, BIM 360, and Vault. Automating data management has reduced errors that are common with manual data recording and connected the company directly with its suppliers.
VisiConsult manufactures industrial x-ray machines primarily for the aviation industry, but its customer base is ever-expanding, and the company often gets orders for custom x-ray equipment for a variety of industries. To create a seamless design-build process, VisiConsult leverages automation with Fusion 360 Manage and Fusion Lifecycle to engage with customers and automates product lifecycle management, testing and simulating designs virtually before any raw material is used.
The reality of the future is that the global population is steadily growing and expected to reach 10 billion by 2050, and demand for goods is increasing along with it. That means manufacturers must meet this demand by producing things better and faster. But at the same time, supply chains are weak, resources are scarce, and the labor pool is shrinking.
While automation isn’t a fix-all as it is now, the future of automation in manufacturing will help rectify these imbalances. But despite all of the bells and whistles that technology will bring, the core purpose of automation will remain the same: to handle repetitive tasks, harness the power of data, and unlock human potential to focus on value-add tasks like product innovation.
Here’s a glimpse into the future of automation.
As systems become more connected and Industry 4.0 becomes the norm, automation is going to kick manufacturing into high gear. In the current configuration, machines are good at handling repetitive and known tasks. In the future, greater machine-learning capabilities will enable a more autonomous operation capable of anticipating unforeseen events and correcting course preemptively.
With this, the industry will move toward intelligent automation. That will lead to what’s been coined as hyperautomation, where every business process that can be automated is automated, thanks to advanced software and technologies. This will usher in a more autonomous factory that can observe, analyze, and improve using the following elements:
AI will continue to expand its reach and take automation to the next level. With real-time data monitoring, AI can autocorrect and optimize an operation. In manufacturing, AI-powered generative design will handle more of the product development. A computer will take basic configurations and explore different design possibilities, beyond what the human brain could conceive of, to reach the best possible solution.
Computers are programmed to perform actions. In the future, they’ll be able to analyze their own data and figure out what they need to do to maximize performance. There are hints of this now, like with predictive maintenance. Equipment failures are one of the biggest disruptions to the manufacturing process and can cost a manufacturer $532,000 an hour. Predictive maintenance tries to curb those failures and downtime, but it hasn’t had the automated setting to reach its full potential. As factories become smarter, machine learning will grow stronger as sensors everywhere collect data to optimize the factory.
Robotic process automation (RPA) is software that will be able to monitor and track human actions and mimic and automate those same tasks.
There’s no question that the role of humans is changing as automation accelerates. More and more, workers are transitioning to jobs away from the production floor and into more cognitive realms. The workers in the future will handle the creative orchestration of the automated facility, with all its complexity and flexibility. But despite being further from the action, people will still be a key part of intelligent automation.
Some think the future of automation in manufacturing is a lights-out facility, where machines and software operate the entire process without the presence of humans. And there are indeed instances where autonomous vehicles and robots seem to be in charge. But in reality, it will be a combination of highly intelligent automation: so-called cobots, robots that work together with human workers in a highly flexible production environment. Instead of handing tasks over, this cohort works with technical systems in new ways that will take manufacturing to a new level of production and efficiency—and, at the risk of sounding dramatic, will change the world.
So now you have this fully automated, intelligent factory that’s buzzing with data. But where does all this data live? The next big step is going to be around platforms. Cloud-based platforms will be connected ecosystems hosting all of the data that automation generates. It will connect systems, people, and processes to make information flow better between different disciplines and tools. Platforms will level up automation to the next technical capabilities.
Automation has come a long way from a conveyor belt moving cars around a factory. Automation in manufacturing has become more granular, focusing on the power of what data can do. It’s creating greater visibility and delivering deeper insights than ever before. As automation accelerates, companies will be able to make data-driven decisions that propel them to a more competitive standing and a more innovative, resilient future.
Detlev Reicheneder is senior director, business strategy design and manufacturing at Autodesk. Based in Germany, Reicheneder has been with Autodesk for 19 years and has 28 years of industry experience. He’s responsible for the worldwide design and manufacturing industry strategy, market development, and industry thought leadership. Reicheneder graduated from Technical University Dresden with a degree in micromechanics engineering and electronics technology.
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