The automotive industry—like many other manufacturing sectors—is undergoing a major digital transformation right now.
The automotive industry — like many other manufacturing sectors — is undergoing a major digital transformation right now.
The rise of Industry 4.0 tech — which includes industrial IoT (IIoT) devices, as well as AI and big data analytics — has disrupted manufacturing as a whole. This has pushed factory owners to adopt new technology and solutions that allow them to streamline workflows or optimize factory management.
The rollout of 5G has made IIoT tech more practical and beneficial than ever, including for automotive manufacturers. These are the main benefits they can secure from implementing IIoT solutions at their factories and adopting IIoT tech in the most effective way possible.
HOW IIOT FLEETS WORK IN AUTOMOTIVE FACTORIES
An IIoT fleet of suitable size can collect massive amounts of data on how a factory works — everything from machine and task timing to inventory movement.
With enough sensors collecting the right information, it’s possible to create a bird’s-eye view of what factory processes really look like on a day-to-day basis. This can make it much easier for supervisors to spot potential inefficiencies.
For example, one manufacturer may use IIoT devices for traceability — tracking parts and components as they move through the production process. With the right data, this company could find that incorrectly manufactured pieces originated from the same factory or certain machines.
Another may find that something as simple as a floor layout change, like the widening of warehouse aisles, could save time and create a safer working environment.
An IIoT system is one way factory owners can integrate operational and information technology — two tech investments that can sometimes be siloed off from each other. Auto manufacturers typically either buy complete IIoT platforms — which include individual sensors, IoT machinery and analytics platforms — or assemble them themselves.
In the second case, they’ll typically begin by identifying a problem they want to solve in their factory or a KPI they would like to improve. They’ll then source the sensors they need to track data related to that problem or KPI, along with an open IIoT analytics platform that will coordinate those IIoT devices and organize the data they collect.
ASSISTED DECISION-MAKING WITH IIOT DATA
Collected data can do more than help supervisors coordinate staff and adjust workflows. It can also let them make better decisions and provide feedback on how to influence workflows or product quality down the line.
For example, many auto manufacturers must decide if they want to use passivation or electropolishing to create corrosion-resistant parts. Both methods have unique advantages and disadvantages. It isn’t always clear what impact one process may have on efficiency or part performance. When deciding which one to use, supervisors may also consider the part being manufactured, available machinery and existing factory workflows.
The number of variables they need to keep track of may make it hard to decide which method will be best. Historical component performance data, operational information and more can help supervisors make the decision.
Similar tech may help automakers decide among particular finishes, stainless steel alloys or component shapes for a given vehicle model.
ONE USE CASE FOR AUTOMOTIVE IIOT: PREVENTIVE MAINTENANCE BECOMES PREDICTIVE MAINTENANCE
One of the most popular applications of IoT in manufacturing is predictive maintenance. A factory owner uses IIoT devices to collect operational information on a given machine. This data may include its temperature over time or its pattern of movement or vibration. In some cases, sensors may listen for supersonic noises and sounds indicative of gas leaks.
When fed into an AI algorithm or big data analytics method, this method can estimate machine health in real time. Often, with the right information, these systems can accurately predict soon-to-come machine failure.
If given control over machine systems, predictive maintenance systems may even be able to force the shutdown of machines on the verge of failure, potentially avoiding damage or injury to nearby workers.
Preventive maintenance is the industry-standard approach for most manufacturers. Regularly scheduled checkups ensure good working conditions and repairs that can extend a machine’s lifespan and reduce the risk of sudden, unexpected failure.
The approach is not perfect, however. Opening a machine for maintenance can put sensitive electronics and components at risk. The process may expose machine internals to site dust, moisture and other contaminants.
Workers and machine operators may have no heads-up if a failure occurs between checks. They’ll still be at risk of injury and downtime that could have been avoided with the right repairs.
Predictive maintenance can help resolve some of these problems and give supervisors and workers advanced notice of machine failure.
In cost terms, predictive maintenance can provide savings of 8-12% over preventive care, according to a summary of past studies by the U.S. Department of Energy. Savings go up to 30-40% for businesses using reactive maintenance or repairing machines when something seems to be broken or malfunctioning.
Predictive maintenance can also spare businesses downtime and injuries that may happen as a result of sudden machine failure.
ADOPTING PREDICTIVE MAINTENANCE
Manufacturers that want to integrate this kind of approach have two options. They can either purchase a complete predictive maintenance solution or combine tech from various vendors to collect and process maintenance data.
Manufacturers that want to build their own solution with technology from multiple vendors should start with the data they’ll need to collect. For example, vibration sensors are often used to track the performance of machines with rotating components.
They’ll then need to find an existing predictive maintenance or big data analytics platform that’s compatible with their chosen IoT sensors.
Manufacturers should also plan for a phase-in period, during which baseline data will be gathered. This period allows the analytic algorithm to build up statistics on “normal” machine performance.
KEY BENEFITS AND BEST PRACTICES FOR AUTOMOTIVE MANUFACTURING IIOT
IIoT devices can offer major benefits for automotive manufacturers. They offer a wide range of uses, like the ability to collect massive amounts of data that can help improve the understanding of a factory and its manufacturing process.
Manufacturers that wish to adopt the tech should clearly understand what they want to improve or what particular IoT use case they would like to implement.
This article was originally published on EE Times.
Emily Newton is a technology and industrial journalist who enjoys discovering how the IoT is impacting different industries. Emily is editor in chief of Revolutionized – an online magazine exploring trends in science, technology and industry. Subscribe to her newsletter to keep up with the latest.