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Using AI for cradle-to-grave product lifecycle analysis (LCA)

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In an age where sustainability is no longer optional but crucial for business longevity and global well-being, product lifecycle analysis (LCA) stands as an invaluable tool for measuring and reducing the environmental impact of products. However, the complexities involved in traditional LCAs, as well as the dependence on specific expertise, often lead to time and resource-intensive processes, which can be barriers to widespread adoption, particularly for smaller businesses. 

Enter artificial intelligence (AI), with its capabilities to automate, analyze, and scale. The integration of AI into LCA processes offers a new horizon for manufacturers and sustainable innovators to conduct more thorough and frequent analyses, leading to more informed decision-making and, ultimately, greener products. This blog explores the role of AI in revolutionizing product LCAs, the benefits it offers, and the challenges it confronts, as well as real-world examples of AI-driven LCA in action. 

For a comprehensive look at navigating AI’s potential and pitfalls with LCA, and ensuring trustworthy results, we delve deeper into these topics in our latest whitepaper – The AI tightrope: Balancing automation, accuracy and trust in LCA/EPD

Understanding product lifecycle analysis 

Product Lifecycle Analysis (LCA) can be categorized mainly into two types: “cradle-to-gate” and “cradle-to-grave.”  

Cradle-to-gate LCA focuses on assessing the environmental impact of a product from the extraction of raw materials (the cradle) up to the point where the product leaves the factory gate, ready for distribution. It doesn’t consider the use and disposal phases of the product’s life cycle.  

In contrast, cradle-to-grave LCA encompasses a more comprehensive assessment, extending from raw material extraction through to the product’s end-of-life disposal, including its use, recycling, and landfill stages. 

The principal advantage of cradle-to-grave LCA lies in its holistic approach. By considering the entire lifespan of a product, this method provides a more accurate picture of its environmental impact.  

This thorough analysis enables manufacturers and businesses to identify potential areas for reducing environmental damage not just in production, but in product use and disposal as well, leading to more sustainable products and practices. Consequently, cradle-to-grave LCA is often regarded as superior for those aiming to make genuinely eco-friendly decisions. 

Challenges in conducting cradle-to-grave LCA 

Undertaking a cradle-to-grave life cycle assessment poses distinctive challenges for sustainability professionals. One major obstacle lies in the difficulty of acquiring precise and comprehensive data concerning the environmental impact of raw material extraction and processing. This data is crucial for conducting a thorough LCA but can prove elusive due to proprietary processes or the dispersed nature of supply chains. 

Another hurdle is the intricate nature of contemporary supply chains themselves. Products often traverse multiple countries and manufacturing stages before reaching the final disposal stage, complicating the tracking of their precise environmental impact. Moreover, standardizing this data for comparison purposes can be laborious, given the diverse production techniques and materials utilized across various industries. 

These challenges demand advanced expertise, significant resources, and frequently, innovative data collection and analysis methods, underscoring the intricacy and significance of conducting precise cradle-to-grave LCAs. 

Overcoming challenges in LCA with AI 

AI plays a pivotal role in revolutionizing cradle-to-grave life cycle assessment (LCA) by offering unparalleled advantages in data collection, processing, and mapping across diverse systems. Firstly, AI streamlines the collection process by automatically gathering data from a myriad of sources, such as online databases and enterprise systems. This automation not only saves time and resources but also guarantees the inclusion of up-to-date data in the analysis. 

Secondly, AI’s capability to handle vast datasets enables sophisticated mapping and processing, significantly bolstering LCA efforts by intelligently inferring and filling gaps in datasets, thereby providing a more complete and accurate picture of a product’s environmental impact. 

Manufacturers can proactively identify and address potential environmental risks through AI-driven simulations of various scenarios like material changes or production process adjustments, thus bolstering sustainability efforts. 

Moreover, AI facilitates real-time monitoring and optimization by providing continuous feedback loops. For instance, product data models built with AI can help engineers quickly identify alternative material or supplier choices, based on multiple criteria such as cost or environmental impact. This real-time insight empowers organizations to make informed decisions promptly, ensuring efficient resource utilization and environmental lifecycle thinking. 

Benefits for manufacturers and sustainable innovators 

AI brings a multitude of benefits to those invested in sustainable practices, ranging from efficiency and innovation to market competitiveness. 

Improved decision-making processes 

By enhancing the speed and accuracy of LCA, AI empowers decision-makers to develop and implement sustainability strategies more proactively. With AI insights, product teams can prioritize areas for improvement and make smarter choices that align with business and environmental goals. 

Enhanced product innovation and market competitiveness 

AI’s contributions to LCA enable businesses to innovate sustainably. Through a deeper understanding of their products’ lifecycles, companies can develop eco-friendly products that resonate with consumers’ growing environmental consciousness, thereby gaining a competitive edge in the market. 

Challenges and considerations 

While the prospects of AI in LCA are promising, there are challenges that need to be addressed. 

Data accuracy and reliability 

The effectiveness of AI-driven LCAs depends on the quality of the input data. Ensuring the accuracy and reliability of data sources, especially those feeding predictive models, is critical to generate meaningful and actionable insights. 

Integration with existing systems and workflows 

Adopting AI solutions for LCA needs careful integration with existing systems and workflows. For successful implementation of AI in LCA, it’s important to integrate product data from Product Lifecycle Management (PLM) systems and map this information to transaction data held in Enterprise Resource Planning (ERP) or purchasing systems, ensuring a seamless flow of information and heightened efficiency in sustainability analysis. 

Examples of AI-enabled LCA 

Several industries have begun to leverage AI for LCA: 

  • Amazon and Flamingo: With the assistance of Flamingo, an AI-powered algorithm, Amazon is now able to swiftly and precisely measure the carbon footprint of its products. In a specific trial, the algorithm decreased the time required by scientists to map 15,000 Amazon products from a month to just a few hours.
  • Microsoft’s LCA 2.0 powered Makersite: Microsoft is committed to reducing the environmental impacts of its products through structured Ecodesign approaches and LCA. Microsoft’s innovative approach involves leveraging AI and data analysis provided by Makersite to automate and scale the product modeling process, focusing on supply chain-specific environmental impact accounting. The transition to Version 2.0 has improved quality, increased accuracy, and better identification of environmental hotspots in their supply chain. The methodology shift aims to enhance transparency, collaboration, and consistency in LCA results, and product emissions, across Microsoft’s entire product portfolio 

These examples demonstrate the potential of AI to transform LCA into a more agile and strategic product carbon footprint environmental management tool. 

Conclusion 

AI will be a game-changer in many industries. Its role in accelerating and enhancing product design processes makes it a powerful solution for managing complex products and their supply chain. With its ability to clean, connect and enrich cross-departmental data with third-party sources, it removes the dependency on sustainability, cost and risk experts.

With AI, product engineers and designers are able automatically detect and connect product components and manufacturing processes to the right supply chain data from a harmonized and hyper-connected database, instantly solving one of the most time-consuming problems: mapping data to multiple sources at a granular level. The result is a detailed, extremely specific view into deep-tier supply chains, giving users a better understanding of environmental footprints, should-costing, and compliance risks at an unprecedented speed.

As manufacturers and innovators realize the benefits of AI-driven LCAs—better decision-making, deep-tier supply chain visibility, reduced environmental impact, and enhanced competitiveness, to name a few—it’s not a question of whether AI should be integrated, but instead of how quickly and effectively it can be done. 

The AI tightrope: Balancing automation, accuracy and trust in LCA/EPD

The end of the entrepreneur: Why ‘take, make, waste’ culture must end

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2 hours, 11 minutes, 53 seconds. That was how long it took Ethiopia’s Tigist Assefa to complete the Berlin Marathon in September 2023. She smashed the women’s marathon world record. Beat it by more than 2 minutes. But it wasn’t Assefa who made the headlines afterwards. It was her shoes. 

On her feet were the Adidas Adios Pro Evo 1. They weight just 138 grams. They have a 39-millimetre heel. They cost $500. And they’re only meant to last for one race. A feat of design and engineering? Absolutely. A revolution in running technology? Of course. But at what cost? 

Despite their public proclamations to the contrary – their stated commitments to ‘people, product and planet’ – Adidas, Nike (who also have a foot in the single-use shoe game) and their contemporaries seem to value column inches over reducing GHG emissions, instant (but fleeting) acclaim over a sustainable and more efficient future.  

Yes, they’ll tell you they’re only producing these shoes in very limited numbers and that it’s the lowest carbon emissions performance running shoe they have ever created. But that’s not the point. In its promotion of such a high-profile single-use product, Adidas are creating a new normal.  A continuing acceptance that increased consumption and rapid wastage is fine. Our global climate crisis is driven by over-consumption, an overreliance on oil-derived materials, huge energy usage in production and shipping and a general disregard for our environment. The Adidas Adios Pro Evo 1 represents everything that is bad in microcosm. 

But this isn’t an article written to call out Adidas. Their approach is simply emblematic of a bigger problem that we’re facing. A problem that, in our approach to solving it, will define us. We live in a world weighed down by commercialism and individualism. We venerate waste and consumption. We exist in a place and a time where ‘take, make and waste’ has become the norm. 

It didn’t have to be this way. Today, most products are made with a singular goal in mind: to sell as much as possible. If our leading companies were not blinded by greed and an unerring focus on the bottom line, they might be able to see that there is another way forward. A future where single-use products aren’t seen as little more than a tool to increase brand power and drive visibility, where sustainability and consideration of the environment aren’t sacrificed at the altar of the dollar. We are a long way from where we need to be. 

Our focus on wealth and immediacy is damaging us. A culture and an economy underpinned by the ‘get rich quick’ mantra is no good for anyone. The people at the helm of our biggest organizations are leaving us with a legacy of poor-quality products that add little to no value. Commercialism, consumption and immediate availability come at a price – and are all concepts defined by low costs, oversupply and a lax attitude towards sustainability and the health of our planet. 

Our culture of consumption has been orchestrated by a very specific type of business person. A person who started out with good intentions but either found themselves at the head of a hydra they could no longer control, or who simply lost their purpose – their duty to people and planet – as soon as money became the primary goal.  

They were no longer the makers and innovators that set out to change entrenched systems. Great ideas, without enough support to hold off commercial imperatives, meant that these people simply became a part of the system themselves. They fell in love with the ‘celebrity’ of the entrepreneur and the financial rewards that come with it. They take actions first and ask questions later. We are left with a scenario where the masses are in awe of the product but don’t consider what goes into creating it. And by the time the curtain falls and the negative cost and supply chain impacts of such rapid consumerism become clearer, it’s already too late. The damage has been done. 

These disruptors of old have become something else. Ideals corrupted by wealth and greed. A symptom of our problems rather than a cure for them. Douglas Rushkoff recently wrote about the “unbearable hubris” of Musk, Bezos and the rest, about their “increasingly outlandish and imperial” behaviour towards the world around them. He’s not wrong. These are men – and they’re almost always men – who cast contemptuous glances at anyone without a similar vision, who view rules and regulations as little more than minor impediments on their quest for growth. 

Unlike the titans of the past – Rockefeller, Carnegie, Vanderbilt, Morgan – it is harder to track the impact of today’s billionaires. Unlike their forebears, they are not capped by the limits of the material world. But that does not mean their operations do not have an impact. As Rushkoff notes, we can still see the consequences of their undertakings in the form of “externalised harm.” 

“Digital businesses,’ he writes, “depend on mineral slavery in Africa, dump toxic waste in China, facilitate the undermining of democracy across the globe and spread destabilising disinformation for profit – all from the sociopathic remove afforded by remote administration.” This represents a new frontier. The imperiousness of this new billionaire class is unprecedented, their “disregard for people and places” without comparison. 

Today’s entrepreneurial leaders are essentially unlimited in the broadness of their reach – holders of what Rushkoff terms “horizontal power.” They donate from their own organisations, often in the form of their own stock, and make their own decisions about how the money is spent. They exist in an impenetrable bubble whilst the world – remade in their own interests – collapses around them. 

But there is still hope. Still time to make a change. Damage has been done, but it is not yet irreversible. We don’t require a complete realignment. It is time, says Rushkoff, to “get on with reclaiming the world from this new generation of robber barons rather than continuing to fund their fantasies.” But how, and when? 

I think now is the moment for a new thought process. A future defined by collaboration, not individualism. Working together for the greater good. Not ‘make it faster’ but ‘make it better’. But in order to create the better world that so many of us want, we have to give our innovators the right platform to succeed. We need to create an environment where success isn’t judged on how many extra zeroes there are on the balance sheet, but on how we build for the future we want and how we protect our planet in the process. 

I’m done with radical promises. I’m finished with sceptics and non-believers. I’m putting my faith in product engineers being able to lead us to a new, better future where they drive strategic transformation underpinned by a shared, compelling vision, financial support based on more than just commercial imperatives and a dynamic ecosystem that is agile, efficient and geared toward ethical, criteria-driven innovation. 

And how do we get there? That’s something we’ll talk about next time.

 

An edited version of this article also appeared on Forbes.com.