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Modelling the life cycle impact of engineering design decisions
Better models could predict the entire lifecycle impact of sustainable design choices

Dr. Dorothea Pohlmann
22 Nov 2022

In any given product, up to 80% of its lifetime emissions will be decided in design[i]. Design choices affect the materials used, in-use emissions, and end-of-life processing. With growing pressure from customers, shareholders, and regulators to be more sustainable – engineering businesses understandably want to make more sustainable design decisions.

One can point to thousands of potentially sustainable design decisions. Airbus is exploring replacing composite materials with bio-based composites[ii]. BMW is considering what materials it can reuse from its cars at their end of life[iii]. Automotive and aerospace companies are looking at sustainable fuels, from biofuels, to syngas, to hydrogen.

These are all exciting possibilities. But the question is not ‘should we make our products greener?’. We should. The question is, ‘in a world of millions of exciting possibilities, and limited time and resources, what is the best combination of decisions to cost-effectively meet sustainability goals whilst maintaining technical performance?’.

Making emissions reductions decisions in a messy world

Most sustainability decisions involve trade-offs. New materials and fuels have knock-on effects for other elements of design, supply chains, and end-of-life disposal. Switching inputs from virgin materials to waste streams is good, all else being equal, but not if shipping them to your factory creates more emissions than mining them locally.

And all this needs to be considered in the context of practicalities such as cost and ensuring sustainable replacements perform well enough. There is no point making a sustainable product that cannot be manufactured, reduces safety or efficiency, or makes your product so expensive that you lose customers to an unsustainable alternative.

Sometimes a seemingly good decision is not as good when you’ve worked through the lifecycle, whilst those that didn’t seem immediately obvious can have an outsized effect. Sometimes leaders’ personal passions jump to the front of the queue when better ideas are out there. And sometimes, your instinct is spot on.

It is impossible to know what the most sustainable design decisions are without good data and models.

Modelling the future

Sustainable design models are still quite immature, but they are coming. Most companies do Lifecycle Assessments which involve gathering data on their products, to calculate emissions. Most have digital design tools that allow them to model different design decisions and PLM systems that let them track products across their lifecycle.

But connecting these worlds is challenging. These systems lack real-time product and supply chain emissions data. The models within them are usually physics-based or hypothetical projections based on industry standards, rather than on what is actually happening in the real world. They can give approximations, eg the carbon savings of swapping one material for another based on industry standard calculations. But they cannot show the real impact right across your product’s lifecycle.

Ultimately what design engineers want – and what leading corporate innovators are now moving towards – is real-time systems-level models. These would use sensors, tracking, and reporting mechanisms to collect real-world data – eg on materials tracking, supplier energy use, product in-use emissions and so on, which all feed directly into the model. So when a hypothetical change is made, designers can see how that ripples through the system across all lifecycle stages

The idea is something akin to self-service financial reports – where financial teams can play with projections to understand the business impact of different future scenarios in just a few clicks.

Such a ‘model-of-models’ might have a dashboard where users click on a part of the product, then use drop-down menus to switch inputs between, say, primary steel, secondary steel, new biomaterials or innovative manufacturing technologies. The user interface would then show them what impact that has on everything from total emissions across the supply chain and product life, to technical elements (weight, aerodynamics, etc), and manufacturing requirements (eg what new machines would be needed).

That would let users experiment with new design ideas in silico and make decisions about which emissions reduction initiatives are most worth pursuing from a cost-benefit perspective.

No one is doing this at this level yet. But many are heading in that direction.

A model of models

Such real-time data is increasingly possible to capture through connected sensors. Setting up such data capture systems remains a mammoth task, but companies are heading in this direction as they already understand the benefit resulting in gathering and using this information.

Once the data is captured, the big challenge is turning it into meaningful insights.

Solving this means not just deploying technologies, but linking up data and models of multiple complex systems.

So, for a plane, we may have a model – or a digital twin – of the aircraft itself, with a list of every material and process needed to manufacture the plane, its components and sub-components. When you change a part in the user interface, the overarching model pulls data from various sub-system models.

It would look at the part’s supply chain model of costs and emissions, which is being updated in real-time by the suppliers – and runs the change to see what effect it has. It does the same for other models along the value chain – customers, service engineers, recycling companies.

The updated emissions values would then feed into your own company emissions model (e.g. how would the new material directly impact your energy use?); and to a model of the plane’s in-use emissions (how would the lightweight material change fuel consumption?).

So when you switch one input in the frontend, you get an updated total score for environmental impact and cost.

The journey to joined-up product modelling

Many companies are already on this journey. As we discuss in a previous article, work on gathering data for life cycle assessment (LCA) will provide the foundation of these models. Many are investing in data collection and Green PLM tools across their business.

As these mature and data collection improves, they can be adapted and combined to provide detailed models which inform strategic decisions about both product design and reinventing production processes for the circular economy.

But to do this, some complex challenges need to be overcome across the organisation, in terms of data collection, management, and curation, as well as IT architecture. These challenges and their solutions are explored in our next article.

[i] (citing



About author

Dr. Dorothea Pohlmann

CTO Sustainability, Capgemini Engineering
With 15 years at Capgemini Engineering, Dorothea has applied her technical skills in business transformation and technology projects in automotive, manufacturing, e-mobility, energy and utilities sectors. More recently she has focused on sustainability-driven business with a specific expertise in Product Lifecycle Assessment (LCA) in the context of complex systems, wind energy and hydrogen. She is an active speaker at conferences and events on sustainability, and is passionate about the need for more sustainable-driven business impact. She holds a doctorate in laser physics.