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Industrial innovation, how an OEM evolves thanks to AI and IIOT

In the face of increasingly complex challenges for their business models, today’s Original Equipment Manufacturers (OEMs) need to leverage digitization, Big Data, Industrial IoT (IIoT) and Artificial Intelligence (AI), which are the main drivers behind industrial innovation, to become and remain competitive in the market and to produce value.


Applying the Internet of Things to the industrial world, by interconnecting smart machines capable of data acquisition, processing and communication, smart infrastructures and advanced IoT analytics platforms, generates unprecedented operational efficiency and is crucial to ensuring the competitiveness of OEMs. Not only does it optimize processes and increase the production value, it also paves the way to completely new business models.


But while there has been a growing realization among OEMs and industry in general that investing in innovation is not only beneficial, but above all necessary[1], not all companies have embarked on successful digitization strategies and, for many, machine interconnection and data analytics is still far from being an industry standard. The same can also be said about the adoption of new business models and servitization.


Yet, IIoT is a strategy that can lead to significant results in terms of efficiency, productivity, and safety in manufacturing. And industrial innovation is the only one that can help OEMs achieve these goals as it is closely linked with greater insight into manufacturing processes and higher efficiency in the use of resources.




Industrial innovation and IIoT


Experts estimate that by 2025 the market for the Industrial Internet of Things (IIoT) will be worth around 4.6 billion dollars, guaranteeing new and numerous opportunities to increase the value of the global economy thanks to the interaction between machines, humans and software.


In the era of Industry 4.0, therefore, industrial OEMs must be able to connect their machines and physical infrastructure to the digital world easily, quickly and cost-effectively. Indeed, communication between IT/OT, when properly managed, leads to improved production processes, reduced costs and increased competitiveness of factories.


For OEMs, digitization and industrial innovation must coincide with the industry’s need to generate value from infrastructure, including obsolete infrastructure, whilst preparing it for connectivity and data analytics. But the real future challenge for OEMs in fully automated production is to shift to a user-centric perspective, i.e. one in which the machine becomes a means of improving customers’ production processes and gives them a competitive advantage over their business rivals. All of this is possible thanks to IIoT technologies that, by leveraging machine-generated data, through advanced analytics in the field and on the cloud, can more successfully manage system productivity, especially in areas of high production flexibility.


Savvy OEMs are already aware that data, both structured and unstructured, once analyzed can provide valuable insights and give constant feedback on products. A data-driven infrastructure enables them to become more agile and flexible, increasing their ability to adapt to consumer demand, and to accelerate time-to-market for new digital products and mobility services. In this sense, IIoT-based industrial innovation also leads to the delivery of additional services, such as, for example, monitoring of machine performance and wear and tear, and servitization functions through which customer loyalty can be built and the user base expanded.




The role of AI in industrial innovation


Industrial innovation is intrinsically linked to advances in AI, which has now reached such a degree of maturity that it is recognized as a strategic area for implementing the digital transformation of businesses.


Thanks to machine learning, i.e. the ability of machines to learn autonomously through algorithms, and Artificial Intelligence, it is possible to optimize a machine’s performance and foster predictive maintenance policies and remote intervention, thus reducing service costs. AI models constantly monitor the operating status of machines and act before faults occur and service delivery is interrupted, thus avoiding downtime and all the relevant consequences. It is, therefore, possible to predict where and when the fault will occur and take preventive action to solve the issue.


In addition, by leveraging distributed intelligence architectures and systems, i.e. by bringing Artificial Intelligence and Machine Learning onto the Edge, directly to the device that collects the data, additional benefits can be achieved in terms of reduced latency, cost and security risks, thus improving enterprise efficiency. Decentralizing the cloud through local IT resources which are physically closer to the generated data, in fact, reduces the downtime associated with running AI processes executed on the Cloud and rapidly deploys solutions, since deep learning algorithms can operate directly on the device that collects, processes and analyzes the data. Edge AI also results in cloud decongestion, reduced energy consumption, and cost contraction.


This is exactly what SECO’s CLEA Smart HMI solution does: it rapidly collects the data produced by industrial machinery - facilitating interfacing through the ability to use connectors for major industrial PLCs - and then, analyzes it both locally and on the cloud, applying sector-based AI models reinforced by training. All the information collected is also displayed on the HMI installed on board the machinery, where the display of notifications and alert messages help to optimize processes and improve the user experience.




[1] According to data from the Industry 4.0 Transition Observatory of the School of Management of the Politecnico di Milano, the Industry 4.0 market also grew in the pandemic year (2020/2021), reaching a value of 4.1 billion (+8% over 2019).