Machines are now equipped to make decisions and provide technical assistance, which has led to more transparent communication. The influx of AI technology can result in increased work efficiency but could also come with drawbacks, like data security breaches and fewer human jobs.
However, on the positive side, job safety and effectiveness can increase exponentially. Where health and safety are concerned, machines can calculate and determine risk factors, improving the work environment. For business leaders, this can mean an increase in return due to maximized efficiency.
The Industrial Internet of Things (IIoT) plays a crucial role here, using sensors to render actions into signals. The signals are transmitted digitally where they can determine things like the functionality of production equipment. This process, called predictive maintenance, is fundamentally changing the manufacturing industry.
The IIoT and Predictive Maintenance
In the manufacturing space, IoT technology is a crucial enabler for predictive maintenance. Through the use of IoT sensors, smart factories are coming to life, with connected machines that can communicate with each other and with humans, who can take action when necessary.
This technology can catch changes and faults that are unseen by the human eye. Instead of solving a problem after it happens, predictive maintenance will alert the system ahead of time, so humans (or machines) can take the necessary action to ensure no problem occurs at all.
The benefits of this technology are clear.
Poor maintenance can reduce a plant’s productivity by 5 - 20 percent. Furthermore, unplanned downtime costs U.S. manufacturers an estimated $50 billion USD each year. Predictive maintenance can reduce or even eliminate unplanned downtime by predicting when a machine needs checkups or when it may become faulty. Optimizing maintenance tasks in this way ultimately maximizes the lifespan of equipment and avoids costly disruptions.
Predictive Maintenance and Industry 4.0
Writing in Forbes, Bernard Marr, author of ‘Big Data in Practice’, states that for a factory or system to be considered Industry 4.0, it must satisfy all of following criteria:
- Interoperability: Machines, devices, sensors and people that connect and communicate with one another.
- Information Transparency: The systems create a virtual copy of the physical world through sensor data in order to contextualize the information they receive.
- Technical Assistance: The systems support humans abstractly—in decision making and problem solving—as well as practically, in tasks that are too difficult or unsafe for humans.
- Decentralized Decision-making: Cyber-physical systems need to make simple decisions on their own and become as autonomous as possible.
The two key criteria in the context of predictive maintenance are technical assistance and decentralized decision-making.
Regarding the first, predictive maintenance drastically improves technical support by catching errors that no humans can see. Not only does this eliminate machine downtime, but it increases the safety of all people who use the device. Regarding the second, a machine doesn’t lie or have a gut feeling, instead predictive maintenance decisions are solely based on data, eliminating a centralized decision-maker—though it should be noted that bias can still be a problem for AI.
Nevertheless, the combination of machine-to-machine (M2M) communication and AI will soon enable entire factories to make data-driven decisions with minimal human intervention.
Predictive maintenance is playing a key role in Industry 4.0. Manufacturers will not only have full lifespan use of parts and no unplanned downtime but smart factories that enable M2M learning can improve productivity and safety.Decisions will be made with data, reducing the chances of human error. It’s also worth noting that the technology needed for smart factories is becoming more affordable. Storage, network, and bandwidth fees—which were once exorbitant—are coming down, bringing Industry 4.0 closer not just to OEMS, but SMEs as well.
As the transition to Industry 4.0 continues, predictive maintenance will play a fundamental role in that transition, ultimately ushering in a new way to work.
Jennifer Roubaud is the VP of UK and Ireland for Dataiku, the maker of the all-in-one data science software platform Dataiku Data Science Studio (DSS), a unique advanced analytics software solution designed to enable companies to build and deliver their own data products more efficiently.