The new generation of digital twins
Recent research shows that the future of business can be shaped differently with customer-focused KPIs and this may be indeed the approach for OEMs. KPIs such Customer Value and Customer Segmentation can drive the future of the business strategy, but should they drive the direction of their digital transformation path? Could KPIs internal to the organisation support the value analysis and what would they be?
The simplest way to identify customer value is by working back from the customers’ pain points. In air and gas handling applications, end users of rotating equipment are typically focused on:
A digital twin model designed to solve physical issues faster by detecting them sooner, predict outcomes to a much higher degree of accuracy. Its ability to evaluate performance of the equipment in real-time, may help companies realise value and benefits iteratively and faster than ever before.
The value internal to a manufacturing organisation starts with having a complete digital footprint of their products from design and development through to the end of the product life cycle. This, in turn, may enable them to understand not only the product as designed but also the system that built the product and how the product is used in the field. With the creation of the digital twin, manufacturing companies may realise significant value in the areas of speed to market with a new product, improved operations, reduced defects, and emerging new business models to drive revenue.
One feature that can be changed in the traditional digital twin approach to satisfy such KPIs is the source of the historical data. Whilst the concept of superimposing digital profiles on real-life application data is the true value enabler, revisiting the source of the data for the digital half may result in a game changing approach. Traditionally, we think of historical data as operational data recorded and used to average a past behaviour.
A digital profile populated by such data will depict a “theoretical” performance map of the equipment as per its design intent. Superimposed on the real-life operational data from the sensors, such information will enable right from the get-go the mapping of the current operation in respect to the equipment best efficiency point, without processing any amount of historical data. Isolating and analysing the difference between the two data sets will enable the performance optimisation of the equipment to match the operational requirements.