The Earlier, the Better

Digital twins can help track and reduce the carbon footprint of products along their life cycle. The data shows: The biggest leverage can be achieved at the very start.

© iStock / gorodenkoff
With the help of data from the entire life cycle, developers can test products in terms of their CO2 balance sheet.

When all you have is a hammer, everything looks like a nail. In other words, when assessing and solving problems, it always depends on one's own perspective – and when working in science, that is something that must be taken into account. So when researchers in production science consider how to influence the sustainability of a product, they have to look beyond the natural scope of the mere production. This is exactly what scientists at Fraunhofer IPK do by observing sustainability indicators such as the carbon footprint over the entire life cycle of a product.

Considering the entire life cycle

From raw material procurement to disposal, reuse, remanufacturing or recycling – data about emissions are recorded and evaluated everywhere. These data can be used to determine the points in the product life cycle where it is possible to influence a product's carbon footprint. It shows that particularly important decisions are made long before production. The climate friendliness and also the circular economy capability of a product – and everything that belongs to its ecosystem – are determined by, for example:


1. the optimal design of the product itself. This includes

  •  product design that aims for a low carbon footprint in the use phase (for example, through efficient engine types),
  •  the choice of materials that ensure a low carbon footprint in material procurement and required transport routes, production processes, recycling and disposal, as well as a long service life, and
  •  optimizing the geometry and joining methods of the product to facilitate repairs, disassembly and reuse, thus enabling circular economy strategies.

2. planning the manufacture of the product in terms of resource-efficient production strategies, intralogistics technologies and manufacturing processes, as well as the machinery and equipment used – but also planning energy-efficient processes accompanying production, for example for lighting, heat, ventilation or exhaust air. In production itself, potential for improvement can be leveraged by regulating machines and systems to run at their optimum in an energy-efficient manner. Product developers use the information gathered during production and in the further life cycle to adapt subsequent product generations in the long term and optimize them in terms of their carbon footprint (feedback to design). 

So how does one get from data about a product and its production to its carbon footprint? In this context, the method of life cycle assessment provides a standardized procedure for evaluating the sustainability of products on the basis of various impact categories, in accordance with ISO standard 14044. All material and energy flows in the course of the product life cycle are recorded and standardized in indicators, for example in CO2 equivalents. This creates an extensive body of data for each individual product that must be stored and evaluated in a meaningful way – and this is where the digital twin comes into play.

Digital twins for sustainability

Digital twins provide product-specific insights, for example in the form of a carbon footprint, which can be used to understand how sustainable the respective product is. They support product developers in making decisions during product design and production planners in designing production processes. In the after-sales area or during system maintenance, they help with monitoring and point out possible optimization measures. At the end of the product life cycle, they provide product-specific information on its disassembly and reuse potential, thus contributing to reuse, remanufacturing or recycling. Digital twins can even make decisions themselves with the help of suitable data analytics – as autonomous digital twins, so to speak. Researchers at Fraunhofer IPK are harnessing the ecological potential of digital twins on the behalf of the manufacturing industry. To this end, they have developed a concrete concept, including system models, the necessary infrastructure, information logistics and development methodology: The »Digital Twin for Sustainability«. For this purpose, the central steps of data collection and evaluation of a life cycle assessment are performed several times over a product's lifetime. A distinction is made between the three central steps: the early LCA during the planning phase, the so-called »plan LCA« (A), the regular update based on operational data, also known as »live LCA« (B), as well as the comparison of these two as the core function of the digital twin (C). 

In a study published in 2022 by Fraunhofer IPK on the topic of digital twins, 63% of the companies surveyed saw a very high potential for digital twins to be used for sustainability assessment in the future.

These are shown in our illustration in the context of the components of the digital twin.

A. As early as in the development phase, an initial life cycle assessment based on assumptions and plan data (»plan LCA«) is performed for different variants of product design and corresponding process alternatives. These LCA results are stored in a so-called digital master (1), which can be the same for several product instances.

B. As soon as the manufacturing phase begins, the product generates an individual carbon footprint, which is now recorded over all phases of the product life as a digital shadow (2). Data is also collected from suppliers for all procured parts to determine the most accurate carbon footprint possible. The carbon footprint is constantly recalculated during production based on energy and resource consumption data as well as emissions. For this reason, this digital shadow can also be referred to as a status quo life cycle assessment or, in the case of very real-time data, as a »live LCA«. A unique identification number of the digital twin is used to link these data together and make them traceable over the life cycle. Depending on the use case, the carbon footprint is updated at certain fixed points in time or continuously and calculated and stored for each individual product instance – or even for an entire product group or fleet.

C. In order to not only calculate a realistic carbon footprint for verification purposes, but also to be able to effectively reduce said footprint, the digital master and shadow are intelligently linked in the core of the digital twin (3). Deviations to the plan are automatically detected and evaluations are performed to identify the most critical polluters and derive optimization measures. Control commands can also be derived from evaluations within the digital twin via a direct feedback loop to the physical system (4), for example to optimize product behavior with regard to carbon emissions. 

The BMBF-funded »BioFusion 4.0« research project shows the implications of this method in concrete terms. A digital twin with integrated LCA is being implemented using the example of an automotive component – the valve housing – which is manufactured at the Mercedes-Benz plant in Berlin. The component’s carbon footprint is recorded from the procurement to the production phase and stored in a digital twin. The entire process is being implemented by Fraunhofer IPK, together with its technology partners CONTACT Software and Green Delta. If the component in question is then installed in a car, its carbon footprint can be considered a component of the vehicle's overall footprint. BMW, ZF Friedrichshafen, BASF, Henkel and LRP – Autorecycling Leipzig, among others, are investigating how digital twins can be designed at the vehicle level as part of the major »Catena-X« research project. Fraunhofer IPK researchers are involved in designing digital twins in order to implement circular economy principles such as reuse, recover, remanufacturing or recycling: The architecture, services and data models required for this are being researched and implemented in the automotive value chain. However, the focus here is explicitly on a – Gaia-X compliant – cross-company data exchange.