Car production has long been a staple of the economy and a marker of what manufacturing is capable of. First we had Fordism, then came Toyotism — but as electric vehicles (EVs) become increasingly dominant, it seems that Teslaism is the future. Meanwhile, the fourth industrial revolution (sometimes called industry 4.0) is coming, and it’s set to use technologies like the Internet of Things (IoT), artificial intelligence (AI), cloud computing and advanced sensors to power smart factories.
As EVs hit the mainstream, they will require efficient production systems and greater reliability to become a more feasible alternative to gas-powered vehicles. If this will be the role of smart manufacturing, what can we expect from the smart factories of the future, and can they really make EVs more secure and reliable?
What to expect from smart factories
Before we go into the specifics of what to expect from manufacturing facilities for EVs, let’s take a broader look at what smart manufacturing is all about.
One component of smart factories is advanced sensors. These are able to indicate whether an object is present or absent and what condition it is in, which is the basis of just about everything else in a smart factory. Industrial IoT (IIoT) — IoT adapted to industrial needs — takes real-time data from these sensors and brings it into a centralized system, usually on the cloud.
This way, managers and engineers can access information about the supply chain and monitor it from anywhere, even from the other side of the world using smartphones.
And this is just the beginning. AI and machine learning can analyze the data collected from sensors, which improves decision-making. Manufacturers can access insights related to quality control, production output, waste, and more to refine and optimize processes over time. Plus, machine learning can detect errors immediately and alert someone to amend the problem.
Data from production operations can even be combined with data from other stages in the supply chain, enterprise resource planning (ERP), customer service, or other areas to provide general business insights.
Finally, robotics can perform certain tasks in a smart factory and remove the need for certain processes to be done manually, saving money and time. Quality control is a perfect example.
All this makes it possible to carry out crucial tasks and maintenance based on predictions from data insights, minimize down-time in the factory, increase efficiency, and provide more visibility to customers.
Lens on EVs
Now that we’ve had a look at smart factories as a whole, it’s time to put the spotlight onto EVs and some of the more unique features found in their production processes.
In EV factories, assembly plans often make use of software simulation suites to test virtual models. These use machine learning to test out possible problems with different models and how components will fit together before resources are wasted in production.
Smart factories make it easier to customize vehicles. Using advanced simulation software along with 3D printing, it’s possible to make smaller batches while remaining cost-effective.
3D printing can also be used to solve the challenge of limited inventory, as components can be printed as they’re needed instead of a manufacturer needing to store everything in advance.
Leading EV manufacturer Tesla has successfully used data to make its business model more profitable. It is constantly generating and using data from cars on roads, in plants, and global supply chains, and using the insights to refine its approach over time. For instance, data from vehicles on the road could point to a small manufacturing error that can be solved. Then, simulation suites could model how to solve the problem.
Over time, we’re likely to see more overlap between technology companies and carmakers as the two industries become more intertwined and cars start to look more like software.
Does smart manufacturing improve reliability?
As innovative as smart factories might be, efficiency can only get you so far if it doesn’t also come with reliability.
As touched on already, the use of sensors and robotics makes it easier to spot manufacturing errors early. If something goes wrong, everyone will know immediately. Plus, big data analytics improve process safety and product quality. As more data is collected and analyzed, it’s possible to reduce the probability of problems happening.
The use of simulations makes it possible for automakers to visualize, validate, and connect parts before building a prototype to ensure a model will be safe and work well.
However, skilled engineers are required to successfully implement the machine learning and cybersecurity needed for these systems. Without them, advanced software may even cause vulnerabilities — if someone were to hack the systems, it could cause major issues in the supply chain.
Does this make EVs safe and reliable?
Most of these general benefits and concerns translate into the world of EVs, too.
EVs may need fewer parts than gas-powered vehicles, but they need 3-to-1 more electric parts. As cars become more digitized, some have called them “computers on wheels” — and this can lead to problems. If there’s a software issue, a car could suddenly stop in the middle of the road and cause a crash. Or, somebody could hack into a car’s software.
The hardware used could also cause issues. Smart manufacturing favors a modular approach, where individual components are replaceable. However, due to the complexity of how these modules can be altered and replaced, only the original manufacturers are capable of the job and not car dealers (unlike gas-powered vehicles). Considering the shortage of highly-skilled engineers, this could cause reliability issues. A report by Hexagon suggests this lack of talent is the biggest issue electrical vehicles face.
It’s therefore clear that manufacturers need to work on their processes to ensure quality and reliability, especially when it comes to the software used in cars and the cybersecurity measures in place. However, as long as these problems can be ironed out, smart manufacturing offers the potential for greater security.
Simulation suites allow engineers to put a prototype through infinite scenarios and possibilities before it ever even touches the road, which maximizes reliability. Then, as cars on the road share data and AI analyzes it, possible faults can be spotted ahead of time and amended before they cause bigger problems.
Toward a more secure future
Smart manufacturing increasingly is able to use a combination of real-time data analytics, cloud computing and machine learning to capture data about what’s happening on a production line or in a vehicle and promote better decision-making. This should bring exponential safety and reliability increase over time, especially as more data becomes available.
However, there are still challenges that remain, especially when it comes to the inherent vulnerability of software to glitches or hacking, and the reliance on a small pool of skilled engineers who can understand the software.
About the author
Peter Els is a South Africa-based former automotive engineer. This includes time with Nissan South Africa’s Product Development Division, Daimler Chrysler, Toyota, Fiat/Alfa Romeo, Beijing Automotive Works (BAW), as well as tier-one suppliers Robert Bosch and Pi Shurlok. After consulting to the local industry for 15 years, Els has ventured into technical writing and journalism about the latest trends, technologies, opportunities and threats facing the new world of mobility.