Why AI is Key to Unlock the True Value of Industry 4.0
Two of the most powerful technologies of the 21st century, capable of bringing us to a place of immense prosperity and well-being, or precisely the opposite, are the Internet of Things and Artificial Intelligence. The boundary between IoT and AI, specifically in the context of Industry 4.0, is growing smaller by the day; it is essential to understand how these technologies augment each other to unlock their full potential for your business.
What is Industry 4.0 actually?
In a matter of few years, improvements in automation and the ability to track and analyze countless new variables related to productivity happened. It took the industrial world by storm in a movement referred to as Industry 4.0. And it wasn’t long before AI tagged along and the two were mentioned frequently in the same sentence.
But we’re often bombarded by headlines and marketing materials that present conflicting or confusing information on exactly what we are referring to when we speak of Industry 4.0 and AI (let’s hope this one doesn’t get added to the list). A simple way to visualize Industry 4.0 and the role of AI is in the interplay of three main elements: Data collection, Data analysis, and Action.
Figure 1. Industry 4.0 as visualized in three steps
Data collection has become increasingly available thanks to low-cost devices and sensors that measure things like vibration, temperature, humidity, and movement. For a manufacturer, data collection could involve equipping various parts in the production line with relevant sensors to gather data on operating conditions.
Once the data has been collected, it needs to be sorted, aggregated and visualized in a way that we can understand it. At scale, these insights could be hidden deep within millions or billions of data points (which is why we depend on edge computing to lighten the load). Hence, traditional methods of data analysis are no longer viable here.
This is where AI becomes the true value driver of Industry 4.0: its ability to analyze copious amounts of data, discern what’s relevant, and use that data to improve operations and deliver actionable insights that create novel value for your business over time.
If you’re planning to invest in an IoT platform, make sure it has AI capabilities built in to this analysis layer, so you don’t have to worry about any added complexity in your solution.
Last but not least, the Industry 4.0 circuit would not be complete without the physical Action required to produce a positive business outcome. Action acknowledges the insights derived from the data analysis and takes measures to make improvements that will boost efficiency or save costs.
Business outcomes of AI in Industry 4.0
Following the “Action” stage of our Industry 4.0 graphic are the benefits, or business outcomes, resulting from this iterative process. A few notable examples of this are:
Maintaining a high Overall Equipment Efficiency (OEE) rating is the result of manufacturing high-quality parts, as fast as possible, and with minimal stops. Many companies have relied on preventive methods of servicing their machines in the past like scheduled checks or historical data. One way AI is changing this is its ability to recognize a set of conditions that have produced a problem in the past and taking action to reduce or prevent it from occurring in real-time. In turn, this can reduce the number of unplanned stops due to equipment malfunction (which can cost up to $260,000 per hour) or improve aspects of inefficient workflows.
Accidents in the workplace can cost companies and employees dearly. This is another opportunity to deploy AI. Connected devices are and will continue to serve the industrial workforce as invaluable assets for performing their tasks. By analyzing location and other environmental data, patterns in accidents and injuries can be identified to mitigate harm in the future. Added panic button functionality on specific devices makes recognizing these events easier with instant alerts to be sent to management in a time of need.
New products / services / business models
The catch-all of high potential technologies like IoT and AI are the yet unexplored or undiscovered products, services and business models that will come of them. Already we are seeing the as-a-service model soar with innovative new ways of leasing and insuring equipment in the industrial space. A direct result of the AI / IoT combo. This is quickly branching to other sectors as product companies aim to monetize the lifecycle of a product rather than just the point of sale.
The evolution of “AIoT” in the future
As with any significant business decision, AI in particular requires companies to diligently plan ahead for how this technology may transform their business. Training employees to better understand AI and its relevant applications i.e. ways it will impact their job, and how they can prepare for the future, should be on management’s agenda.
In a previous post, we wrote about how to champion IoT and build a sustainable competitive advantage in various industries. Develop your digital strategy with AI and IoT in equal measure because they have become largely inseparable. You can even think of it as “AIoT” if you like (completely up to though).
To recap: The role of AI in Industry 4.0 is to:
- Analyze large amounts of data fast
- Recognize patterns and detect anomalies in the data
- Constantly improve in this identification by learning from data
- Deliver actionable insights that improve efficiency, reduce costs, and open doors to novel value creation
Be the first to know about the value Kontakt.io is bringing to Industry 4.0, including how we’re simplifying IoT (in a big way in the near future ;-)) by subscribing to our newsletter and following us on LinkedIn.
If you’re interested in how you can leverage IoT in achieving lean operations in a plant, check our whitepaper.