The current wave of digitalization is vigorously stirring up the core dimensions of business: i.e. value propositions, peoples & skills, processes, and technologies. This is especially true for the manufacturing industry: The Industrial IoT is in the starting blocks and will go far beyond current machine-to-machine applications. The first game-changing consequence of IoT is much closer communication between machines, machine parts and the business line within a company; the second consequence is data-driven interconnectivity between companies – not only along the traditional value chain from supplier to customer but also in many as yet unforeseeable directions. The automotive industry, for instance, is a prime example: cars exchanging data with other cars on the road, with the manufacturer, a garage, insurance companies, as well as weather data providers and traffic control systems, to name just a few of the socio-technical systems. There will be no limits for innovative IoT business cases.
The message for CEOs is crystal-clear: Digital reinvention must have absolute priority. Regrettably, according to McKinsey&Company, more than 70 % of all transformation projects fail. There are many reasons for failures, but the old craftsmen were right: If you want excellent results, you need excellent tools. And for success in IoT, CEOs have to focus on powerful software tools, development frameworks and evolving standards to achieve a fast, flexible and expandable IoT transformation.
From M2M to IoT
Even today, the shop floor creates lots of data. Sources include control units (SPS), Manufacturing execution systems (MES) and a variety of different databases, e.g. for process control or quality inspection. Computer-integrated manufacturing (CIM) and machine-to-machine communication (M2M) are based on particular standards and communication protocols. With OPC, MQTT and other interface protocols, message frameworks for vendor-independent communication are now at hand. However, more than 150 different technologies and systems cause a wide variety of incompatibilities – which is one reason why only a small part of the generated data is being analyzed so far.
IoT is sensor-driven Internet and goes much further than M2M. With IoT, basically all single production units can be connected while using Internet fabric and protocols.
In search for IoT knowledge
Embedding sensors and implementing IoT technology into industrial facilities requires sophisticated know-how. The first dimension of expertise refers to hardware: the varieties of sensors, microcontroller specifications or gateway boards pose many particular questions. The second dimension is software: New concepts, architectures, frameworks, and algorithms are pivotal in dealing with the sensor-driven flood of data. These data pose quantitative and qualitative challenges. As to quantity, it is often not possible or not reasonable to store, manipulate, merge or forward the huge amounts of data based on conventional IT paradigms (SQL databases, standard multi-layer architectures, etc.). As a result, most data generated by IoT sensors currently remain unused. The qualitative challenge is to exchange and integrate data across devices, users, and domains. Only semantic interoperability enables service-level integration of IoT end-to-end systems with components from different vendors and guarantees the aggregation of data from different domains. Meta-data are essential for IoT because they provide important context. Depending on the use-case, meta-data of interest might be serial numbers, and frequency of reporting, location, manufacturer, domain, access rights, restrictions, accuracy, calibration, and much more.
Modeling and managing these quantitative and qualitative software dimensions of IoT is a complex business. To fence, enrich and direct the incoming and outgoing data stream in Industrial IoT scenarios we need new and big data reservoirs and tools to identify valuable data or separate “muddy” (noisy) from “clean” data. In IT terms: companies have to evaluate enterprise service busses, real-time data ingestion, data integration, data preparation, etc. IoT without a thorough know-how for an intelligent data infrastructure is not an option. Or to put it another way: any serious IoT project needs a resilient data management strategy to identify and handle valuable data, deliver deep insight into the production line and establish an option to exchange data with the business world.
Connecting the digital and the physical world is an adventure into new territory. Companies have to start immediately, experiment and learn from mistakes. And clever C-level “craftsmen” mark the beginning with the best concepts and tools available. In the second part of this contribution, we outline German’s ambitious concepts for Industry 4.0 and an Industrial Data Space (IDS) and the consequences for a solid data management. While Industry 4.0 is a service-based architecture based on a layer-concept and a life-cycle model, IDS propagates an intelligent data space for a transparent and trustworthy exchange of data.
References:
Internet of Things: Connecting the Digital to the Physical World
Zeeshan Javeed: Sensors, Environment, and Internet of Things (IoT)
About the Author – Dr. Norbert Jesse
Jesse is co-founder and managing partner of QuinScape GmbH. QuinScape is a leading IT service provider for Talend, Jaspersoft/Spotfire, Kony and Intrexx. With today 120 employees QuinScape is partner of large corporations and internationally operating SMEs.
Jesse studied Social Sciences with emphasis on economics and statistics at Ruhr-Universität Bochum. He received his Ph.D. with a work on analytics for multi-dimensional spatial data.
Jesse has been organizer and co-organizer of numerous international conferences (Fuzzy Days, FIRA World Congresses, CIRAS, Enterprise 2.0 etc.). He is lecturer at TU Vienna and Visiting Professor at University of Business and Technology, Pristina. Furthermore, Jesse is author or co-author of more than 55 conference papers and co-editor of 6 books.
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