The evolution from M2M communication to IoT and AI-enabled systems has profoundly reshaped the industrial landscape. This transformation, which took its first steps with the interconnection of machines in the 1990s and evolved through scalable IoT platform solutions, has now reached an exciting juncture with the incorporation of cutting-edge data analytics and AI models. Together, these advancements form the bedrock for the transformation to a digital industry in the 21st century. Let’s take a look back at two decades of a technological revolution in motion.
Picture the 1990s – an era when the internet was in its infancy, and mobile phones were hefty contraptions that couldn’t fit into your pocket. Yet, it was during this decade that the seeds of something monumental were sown: Machine-to-Machine (M2M) communication. Since the early ’90s, M2M applications had been making their way into various sectors. The proliferation of machines and equipment equipped with maintenance interfaces and sensors, combined with rapidly advancing communication technology, paved the way. The growing availability of mobile networks with ever-increasing data speeds significantly accelerated the continuous evolution of M2M.
The first generation of M2M was focused on basic functions, such as alerting from a programmable logic controller (PLC) or remote configuration of parameters. This allowed for automated transmission of fault notifications to a service technician, often via GSM modems – a promising concept at the time.
The second generation of M2M solutions, which were server-based rather than cloud-based as they are today, could already process, analyze, and prepare data in real-time, sending it to downstream systems and processes. The technical one-way street of data flow from the machine to the application was opened up, establishing a bidirectional data transfer.
These developments enabled numerous new application scenarios, marking an important milestone in the history of M2M and the later Internet of Things (IoT). From this point onwards, M2M providers had to exhibit high flexibility regarding company-specific requirements. They needed not only technical development skills but also consultancy and process expertise. Consequently, M2M/IoT pioneers like Device Insight transitioned from being pure technology providers to solution providers for a digital industry.
A little over a decade ago, as it became clear that the global proliferation of machine connectivity was coupled with the internet, the Internet of Things (IoT) emerged as the new hype. The focus remained on the intelligent connection of machines and equipment, as well as the interplay between machine control, intelligent sensors, mobile data connectivity, and an IoT platform, like Device Insight’s successful CENTERSIGHT.
In one of Device Insight’s pioneering projects, logistics vehicles were interconnected in production and warehouse facilities via radio modules with M2M gateways. Through mobile networks, these gateways transmitted vehicle operational data to an IoT platform, from which the data was sent to a fleet management system. Operating hours, usage data, as well as alarm messages were collected and analyzed – across locations and independent of manufacturers.
Given the vast amounts of data in the industrial sector, Device Insight has always adhered to the principle of scalability to meet the scalability requirements most effectively. This approach eventually led to a strong partnership with Microsoft and paved the way for native integration into Microsoft Azure. Today, Device Insight’s software architects design IoT applications based on various cloud providers, especially Azure and AWS.
The concrete benefits that companies expected and still expect from the advent of digitization and, by extension, M2M & IoT are cost savings, efficiency, and productivity gains, and, more recently, the leverage of AI-driven automation and proactive process optimization. To achieve this, manufacturers must implement standard applications, such as those included in the first software modules of CENTERSIGHT and continuously developed since, including Condition Monitoring, Predictive Maintenance, Remote Service, and Data Analytics. In theory, implementation may sound easier than it is due to the complex dependencies within corporate structures. However, this complexity is manageable, as we know from 20 years of market experience – provided that the solution provider brings genuine use-case experience and the necessary domain knowledge.
After several product generations, Device Insight changed its solution philosophy and introduced CENTERSIGHT scale as an adaptable and scalable framework, considering itself an “enabler.” It is no secret that the requirements for digitalization solutions change dynamically. Functionalities must be realigned with users and market needs repeatedly. We experience this firsthand with the discontinuation of first-generation IoT platforms, further fueled by the discontinuation of tech giants like SAP and IBM. The cloud and data services of hyperscalers are on the rise – and that’s precisely what CENTERSIGHT scale is designed for. Moreover, Device Insight’s “Consult Instead of Code Down” mentality ensures the necessary technological foresight. This way, we ensure that our customers benefit optimally and sustainably from the solutions provided.
KUKA iiQoT robot platform: CTO Thomas Stammeier of Device Insight and Dr Richard Zunke-Hatfield, Senior VP R&D Platform Applications & Tools at KUKA, present the jointly developed IIoT platform in the podcast.
From the outset, Device Insight’s solution concept was designed for a wide range of industries: from machinery and plant engineering to building technology, retail, industrial automation, energy management, and medical technology. This continuity is also evident until today in our thematically diverse case studies.
What was new, however, was the step towards combining the two top technologies, AI and IoT, in 2021. Together with the Swedish AI specialist Sentian, Device Insight designed integrated AIoT solutions, making it one of the first providers to do so. Soon, it became evident that the intuition for the emerging trend of Analytics & AI was spot on. To not only use the “Insights” from data and “Devices” for added value but also to do so faster, more efficiently, and holistically, companies today have no choice but to embrace AI-driven solutions. It is the next logical step in the evolution of M2M, IoT and AI. In 2023, Device Insight established a strategic partnership with Databricks.
To transform data into “actionable insights” using Analytics & AI, a modern, future-proof data strategy is required – an IoT data strategy, if you will. This is where we, together with our partners, assist companies. We believe that Lakehouse architectures, which break down the traditional boundaries of Data Warehouses and Data Lakes, offer significant added value for the data engineering of the future. Our goal is to design and implement solutions that allow companies to capture, cleanse, process, store, share, analyze, model, and, above all, monetize data. Ideally, all of this is achieved in one go. What may sound ambitious becomes a practical reality through the coherent symbiosis of IoT, Analytics & AI.
Today, we stand at a juncture where machines not only communicate but also learn from data, make predictions, and directly apply them. In an era where efficiency, sustainability, and productivity reign supreme, these capabilities, more than ever, expedite the journey toward a connected, intelligent industrial future.
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