Industrial enterprises are now rapidly developing their digital services. The aim is to find new business opportunities and increase the value created for customers. Digital lifecycle thinking helps industrial enterprises deliver significant added value to customers and gain a competitive advantage for their business.
Currently, services mainly support only individual steps in the digital lifecycle of a product, such as after-sales or maintenance. These are areas where companies are already utilising data to good effect, but we see room for improvement, for example in how data can speed up and improve product development.
First of all, digital twins must be integrated into all digital service solutions. Secondly, companies must invest in the digitalisation of the entire product life cycle.
Digital lifecycle management is a new way of understanding how to build a new business model for value-added digital services on top of physical machines or devices. This is a prerequisite for creating new digital-based business and growth. Converting physical product thinking into service-based revenue is an absolute necessity for any business.
Digital twins play a key role in lifecycle thinking. They help companies create better equipment faster, more efficiently and in a more environmentally friendly way. At the same time, digital twins change and improve product development, production, maintenance and ultimately the entire lifecycle.
Three perspectives on the benefits of lifecycle thinking in digital services
- Data
Today’s companies know how to use operational data of machines and devices in preventive maintenance solutions, for example. Collecting and analysing data with the help of artificial intelligence provides companies with information on the basis of which they can make comparatively far-reaching forecasts. This makes it possible to monitor the condition of a unit and determine, for example, the failure rate of a particular model series or a specific component, avoid unexpected repairs or anticipate necessary maintenance based on the actual situation.
However, we have noticed that this data is not yet sufficiently utilised in product development. This is not good, as the data can show, for example, how much a particular feature is being used, helping you to decide where to put your focus in product development efforts. It also improves the quality of the product being developed by providing feature-specific product information.
We believe that pioneering companies stand out from the crowd by analysing the data collected throughout the product lifecycle. This creates valuable information to support decision-making.
Digital twins are useful especially in situations where traditional methods of analysis and optimisation are not sufficient. The flow of information between a true digital twin and a physical machine is essential for creating value in the management of the equipment in the field.
Digital twins also help companies track, analyse and reduce their carbon footprint. This allows them to comply with environmental standards, monitor the environmental impact of production, minimise emissions and waste, and simulate the effects of new regulations, for example.
At the end of the digital lifecycle, data can be utilised to support the recycling and reuse of products, which in turn promotes sustainable business models.
2. Training
Staff training and skills development are vital for today’s industrial enterprises. By utilising digital twins in the implementation and simulation of training, companies can create a smoother, safer and more measurable way to learn about manufacturing, maintenance and new technologies.
Digital twins can be used to monitor and analyse the operation of machines and equipment. If service staff detect a problem, the digital twin will help them go through the necessary steps. This will reduce the number of service visits and make it more likely that the first repair will be successful.
In training organisations, artificial intelligence and digital training can be used to narrow productivity gaps. Digital training enables learning regardless of time and place, which makes training more efficient and improves learning outcomes.
Another significant advantage of this approach is that employees in different roles within the organisation can develop the product simultaneously. The changes are visible in real time in a model that is available to every designer and trainee.
3. Service Sales
In building service sales, usage data helps identify customer needs, tailor the service offering and better target customer contacts. In this way, product development investments can be targeted at the features that are most used and create the most value.
For example, Caterpillar has created digital twins of its new generation of excavators, improving their performance. Virtual simulations visualise the air flow and temperature in the machine’s radiators while fan speeds are varied to simulate performance in different operating applications. The digital twin optimises machine cooling and monitors performance throughout the machine’s lifecycle, while optimising energy consumption.
Usage data can be used to personalise services. This will create new business opportunities that better meet customer needs. In addition, the data can also be valuable to third parties. For example, component manufacturers can benefit from data collected by a machine manufacturer, or leased machines can be discounted when the data shows they are handled with the best possible care.
Industrial enterprises now have a unique opportunity to take the next step towards a more sustainable and customer-centric digital future.
