IIoT applications have been used across a broad selection of industries for a number of years. They achieve measurable competitive advantages, for example by ...
... automating production processes and minimising downtime
... measuring oil production in the oil and gas industry or supplying vital information for preventive equipment maintenance.
... automatically checking drinking and service water values in the water industry.
... providing information about fuel efficiency, idle times and locations in machinery and plant engineering to create maximum transparency for operators and allow automated optimisations.
... in smart turbines, providing information about wind farm output so that future planning and maintenance work can be scheduled.
... in the healthcare sector, exchanging patient data between different agencies, enabling precise monitoring, reporting any changes and facilitating prompt intervention.
... in facilities management, automatically controlling temperatures or monitoring entrance areas to enhance comfort and security.
... in future, in aircraft manufacturing, smart tablets will automatically transmit information to robot tools to significantly accelerate processes.
... in logistics, administering and monitoring inventories to enable automatic reordering.
... In retail, facilitating fast, intelligent decision-making in individual branches so that campaigns and portfolios can be tailored to individual customers.
IIoT architecture may have different weightings (technical, organisational etc.) depending on the function and structure. The approach outlined below gives a summarised account of key functional structures.
The structure of IIoT systems may be roughly divided into four levels.
The first level comprises the various sensors which record physical or chemical signals from devices or machines and transmit them to the second level via Bluetooth, wifi or satellite, for example.
This data is stored, compared, analysed, interpreted and linked to other data in the data infrastructure of the second level. Self-learning artificial intelligence or machine learning concepts are often used to upgrade collated data into so-called smart data.
At the third level (services level) this smart data may take the form of a purely visual display of certain values, specific recommendations, or fully automated actions. These applications, known as smart services, use the various data and evaluations and link the different players together. This allows processes to be optimised and services extended or created, inspiring new data-based business models and value creation opportunities at the fourth level.
The IIoT systems used (i.e. the interactions between machine, hardware, software and sensors) are tailored to the intended application and industry segment, rather like business models. One thing all systems have in common is that they demand a high level of scalability, real-time capabilities and, of course, maximum data protection throughout the entire process.