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IoT solutions as drivers of efficiency: Estonian advanced manufacturing companies are able to provide highly complex solutions

Estonia is one of a few countries best equipped to provide qualified employees, technology, know-how and solutions for IoT projects from a single source.

Estonian companies are already development partners for companies across Europe, ranging from automotive supply, energy, engineering to logistics, pharmaceutical and chemical industries. They are forerunners in the digitalization of processes in automation. The industry 4.0 solutions provided by its companies cover predictive analytics, preventive maintenance, smart factory, smart logistics, and process automation. Since the mid-90s, Estonia has set the course for digitalization with “Tiigrihüppe”, the Tiger Leap, expanded from the administrative and educational system into numerous industries and is now exporting the digital transformation to 120 countries worldwide.

“Estonian advanced manufacturing companies are able to provide highly complex solutions, including contract manufacturing, due to their extensive vertical integration,” explains Triin Ploompuu, member of the board of the Federation of Estonian Engineering Industry. This relieves customers of procurement, supply chain management, development and production. Estonian companies have an export rate of 80 percent and manufacture at competitive costs.

Predictive Analytics: IoT saving energy and creating efficiency

Advances in technologies such as big data, IoT and machine learning have also given predictive analytics a boost. From a large amount of production data, it is necessary to filter out those relevant ones that lead to information and knowledge, i.e. to draw conclusions from video, audio and sensor data or log files of machines that help production management to make predictions or to avoid upcoming critical situations, e.g. through early maintenance and repair work.

The German shipbuilding company Rheinhold & Mahla has developed an application together with the Estonian company Proekspert that uses predictive analytics to save energy on ships. This control solution uses model predictive control to improve the air conditioning on board, reducing the power consumption of the HVAC system by 10%. Data from the air conditioning system is collected remotely, from which various control decisions are simulated onshore, such as the room temperature setpoint. The autonomous, optimal decision is then transmitted to the local control of the air conditioning system, i.e. human intervention is not required. This model of air conditioning technology can also be transferred to office or production buildings.

Smart factory: highly automated manufacturing and real-time tracking

Another use of industry 4.0 is a smart factory, an intelligent, fully automated factory, for example for the production of wood pellets. Using data from various points in the pellet production process, such as drying, filtering or pressing, the entire plant can be monitored to optimize the production or identify problems.

The Tallinn-based company SBA Service combines this data on temperatures, pressures or work flows in a fully equipped, walk-in control container. From there, operating parameters can be automatically adjusted or individually overwritten. This enables factory operators to find the ideal output/cost ratio, which is particularly beneficial in countries with high energy costs. Remote maintenance via the internet is used to install new programs, update the software or reprogram the system from Estonia.

Smart logistics: internal and external logistics

Bosch Thermotechnology also relies on a solution from Estonia for its competence center for water heaters in Aveiro, Portugal. The Tallinn-based company Eliko has installed a tracking network consisting of UWB (ultra-wideband) technology to locate and control logistics vehicles within the plant.

Due to real-time tracking, Bosch’s internal logistics team constantly has the overview on the exact number of vehicles, the components and materials they carry, and to which place. This digitalization enables faster planning, better route definition and higher efficiency, which leads to greater cost-effectiveness, shorter delivery times and at-the-end improved customer service.

Kuehne + Nagel, a leading global provider of fully integrated supply chain solutions, is using smart logistics in a different way. The Estonian software and consulting company Helmes developed several tools for K + N to simplify the cooperation with different customer segments. These include an efficiency tool for road transport operators and procurement managers that supports the validation of cost calculations for road transport by subcontractors. Another online solution allows users worldwide to request LCL (read-than-container-load) quotes with just a few clicks. For customers who have to ship temperature-sensitive goods in various modes of transport, such as pharmaceutical deliveries, Kuehne + Nagel uses a tool for digital, seamless monitoring.

Preventative maintenance not only for offshore applications

IoT applications are particularly interesting when dealing with costly work, such as offshore operations like oil rigs or wind farms; these repairs at sea involve a great deal of effort. With its joint venture partner Sensorise from Bremen, Proekspert developed IoT applications that are used in flanges, wind and water turbines. These smart applications collect data and information for monitoring and preventive maintenance in order to increase the operational reliability and service life of the equipment and reduce costs.

Sensors monitor critical screws in wind turbines. They help the operators of machines and plants to collect operating data, while machine producers gain deeper insights into product quality and can draw conclusions about material properties.

Interested in investment opportunities that Estonia’s digital industry has to offer? Read more here or send us a request for e-Consulting.

*Original Article published in Industrielle Automation Magazine

See the original article here.

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