Konecranes, specialising in the manufacture and service of cranes and lifting equipment, has implemented Siemens’ digital innovation platform to accelerate its product development process and connect product and performance data together.

The company is using MindSphere, the open, cloud-based Internet of Things (IoT) operating system, and the Teamcenter portfolio, the world’s most widely used digital lifecycle management software, to leverage the digital twin and reduce the number of physical prototypes, which helps to increase efficiency and decrease product validation time.

The Konecranes proof of value is one of the first implementations of IoT to develop a framework that connects and synchronizes the virtual (engineering design, analysis and simulation) and physical (testing and operational reliability) worlds.

The product design process is currently based more on an engineer’s experience and generally shared assumptions than measured facts from existing products. These assumptions often lead to non-optimised designs that are over engineered.

With an integrated digital twin platform, there is major potential in speeding up the product development process, reducing prototypes, increasing traceability and thus improving quality and reduce development cost.

Today design, simulation and prototype testing organisations operate in their own silos, often using out of date processes for their work.

At Konecranes, a digital twin was utilised as the communication approach between all three organisations to review data and provide feedback around engineering, simulation and testing intent.

Using the Siemens platform for digital innovation, Konecranes has been able to connect the data from all of these organisations to create one 360 degree view of how prototypes are running and performing, and correlating requirements to, real world performance data.

A closed-loop digital twin framework using IoT and product lifecycle management (PLM) technologies can lead to faster design issue resolution and shorter prototyping phases by leveraging virtual sensor data in product simulations to provide accurate results.

It can also improve overall quality and support downstream product lifecycle processes.

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