IoT apps are here to stay. And manufacturing companies are beginning to reap the benefits.
What are IoT apps?
IoT apps are lightweight bundles of software and services that usually involve a packaged machine learning algorithm that is ready to roll out and run on various edge devices. The data received from various devices at the IoT edge is integrated and analysed to enable fast decision-making.
Unlike comprehensive IoT solutions, AI-driven IoT apps are usually limited to one capability or track just one metric. The examples in industrial manufacturing are numerous:
- a GPS reader app that reports on the geolocation of assets
- a temperature & humidity reading app that gives you an overview of these parameters over time and sends out alerts if a limit is exceeded
- an object detection app where the algorithm is trained to only detect certain types of objects
- a people counter app that monitors human movement in crowded spaces and sends out alerts when a certain critical number of people is reached
- a motion detection app that can, for example, send out alerts if any movement is detected in hazardous areas
These are only a few examples. Visit our App Store to explore more IoT apps.
How Do IoT Apps Work?
In the IoT app universe, you build your use cases by selecting and installing different apps on your edge devices to get to the full picture you’re aiming for. For example, if you want to do remote condition monitoring for certain groups of assets on the shop floor, you may want to trace the machine metrics for temperature, humidity, pressure, oil levels, vibration, motor circuit, ultrasonic profile, electrical and electromagnetic monitoring, etc.
Instead of building a custom IoT solution from scratch, you simply install separate ready-made apps or customisable app groups that are built to trace these parameters. You will have simple, basic functionality apps to bulk-track metrics such as temperature, humidity, or oil levels. And then, you will supplement these with some more sophisticated apps that will do the electromagnetic monitoring. This way, you will additionally measure magnetic field distortions to detect leakages, cracks, dents, corrosion, thinning, or other structural weaknesses.
To get an overview of all these metrics and the data coming from different apps, you will need a customisable dashboarding solution. This is how you remain on top of any contingency and are well-equipped to anticipate any machine failure weeks before it occurs.
At the end of the day, this approach allows you to remain flexible as you can upgrade your existing use case by adding more apps or removing any applications that are no longer delivering metrics that interest you. This is also a cost-effective way to keep track of machine health as you downgrade or upgrade depending on current budgets.
By combining several lightweight apps and installing them on edge devices, manufacturers can now build their own use cases in no time, with unparalleled flexibility.
With a fully managed solution, then, the full app lifecycle is already covered. You can troubleshoot, debug, install OTA updates, and monitor app performance in near real-time.
Transforming Industrial Manufacturing with Lightweight AI
Thanks to the high availability of AI, digital transformation is becoming part of the status quo. This does not simply include automation and smart monitoring but a plethora of digital services that improve customer experience and employee engagement or tap into new revenue streams.
But even though manufacturing companies have embraced IoT and have connected their assets on the shop floor, true transformation has remained out of reach for many. Thousands of manufacturers across the globe are still holding onto legacy systems and are only using a fraction of the collected device data. Instead of working toward greater efficiencies, most of the data is utilised in a shortsighted manner, used to trace just one or two machine metrics without significant insight generation.
Against this backdrop, IoT apps are where shop floor assets at the IoT edge and software services meet. Machine learning algorithms and AI are brought to the IoT edge as lightweight products that bring together near real-time data streaming and live dashboarding solutions. This way, companies benefit from a ready-made OT+IT bundle that empowers them to build their own IoT product and assume full ownership of the use cases.
Manufacturers are slowly becoming aware that the anticipated digital transformation is not as much about connected shop floors as it is about creating sustainable solution cycles.
Overcoming the challenges of unstructured data, closed systems, and intransparent processes, IoT apps can become the ultimate equaliser in industrial manufacturing. With apps, manufacturers can forego the chaos of implementing a patchwork of complicated technologies and the risks of delivering an incomplete data picture. Thanks to container technology, apps can be installed on any device without a change in the existing hardware landscape. This translates into getting data from all machines and achieving full transparency on the shop floor.
Scaling existing use cases is also no longer an issue as companies can solve this by bulk app installations on given device groups. All classic considerations such as security and interoperability are already solved at the app level so companies will not need to make adjustments locally or build from scratch.
IoT apps serve as the reusable IoT building blocks for fast-track use case generation. Ultimately, for an organisation that means reaping the benefits of real-time intelligence and sophisticated machine learning solutions in a fully managed setting.
Curious? See our IoT App Store in action.
About Record Evolution
We are a data science and IoT team based in Frankfurt, Germany, that helps companies of all sizes innovate at scale. That’s why we’ve developed an easy-to-use industrial IoT platform that enables fast development cycles and allows everyone to benefit from the possibilities of IoT and AI.