While medical equipment is enabling doctors to more fully understand the patient, the data in many industrial settings, such as a factory, is not always readily available.
For example, there can be thousands of legacy machines in an automotive manufacturing plant that might have been installed decades ago, creating a black box of unavailable insights. While IoT has begun to solve this conundrum by connecting the machine and collecting valuable data insights, there is still a deeper layer of the system’s characteristics unknown.
This inaccessible data source is simply too valuable to ignore for industrial companies evaluating digital transformation initiatives to create differentiated products or improve operational effectiveness. We’ve seen an increase in forward-thinking manufacturers adapting a ‘reverse-engineering mindset’ and seeking out the OEMs of machines and systems operating in their facilities for their technical specifications and ultimately create a digital twin. This also is occurring in safety-intensive industries such as aviation and government, where performance is mission-critical, and this level of machine and product detail is required.
This push by industrial companies and manufacturers to seek out the digital definition or ‘DNA’ of both machines they use internally and products they sell are developing powerful digital twin use cases.
Obtaining this ‘digital X-ray’ to see beneath the skin of a deployed system requires both real-time characteristics and historical systems of records. Underpinning these increasingly digital inputs are the machine or product’s computer-aided design (CAD) model and additional context through product lifecycle management (PLM) integrations. Industrial companies equipped with a machine’s granular specifications are then capable of running complex real-time computer-aided engineering (CAE) simulation applications like thermal and structural analysis.
Howden is a real-world case of this through implementing sophisticated purpose-built digital twin analytical models for its customers based on baseline product definition data with real-world IoT performance data.
This digital definition data and analysis will unlock pivotal analysis, but adoption will come with coinciding challenges. A digital twin of a human will naturally bump up against sensitive patient data compliance frameworks and generate questions of who owns his/her data: themselves, the physician, the pharmaceutical company or the genetics test provider?
The same data ownership question will pop up in industrial digital twin use cases and whether the product/machine core technical specifications belong to the parts supplier, OEM, CAD software provider or end user? However, cross-sharing of machine data will likely be less-sensitive and easier to implement among stakeholders then a patient’s data.