{"id":509311,"date":"2023-02-14T17:50:51","date_gmt":"2023-02-14T16:50:51","guid":{"rendered":"https:\/\/www.capgemini.com\/?p=861392"},"modified":"2025-04-03T04:54:45","modified_gmt":"2025-04-03T04:54:45","slug":"getting-ready-for-predictive-lifecycle-assessment-models","status":"publish","type":"post","link":"https:\/\/www.capgemini.com\/au-en\/insights\/expert-perspectives\/getting-ready-for-predictive-lifecycle-assessment-models\/","title":{"rendered":"Getting ready for predictive lifecycle assessment models"},"content":{"rendered":"\n<header class=\"wp-block-cg-blocks-hero-blogs header-hero-blogs\"><div class=\"container\"><div class=\"hero-blogs\"><div class=\"hero-blogs-content-wrapper\"><div class=\"row\"><div class=\"col-12\"><div class=\"header-title\"><h1>Getting ready for predictive lifecycle assessment models<\/h1><\/div><\/div><\/div><\/div><div class=\"hero-blogs-bottom\"><div class=\"header-author\"><div class=\"author-img\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/au-en\/wp-content\/uploads\/sites\/10\/2022\/11\/Doro1.jpg?w=520?w=200&amp;quality=10\" alt=\"\" loading=\"lazy\"\/><\/div><div class=\"author-name-date\"><h5 class=\"author-name\">Dr. Dorothea Pohlmann<\/h5><h5 class=\"blog-date\">16 Feb 2023<\/h5><\/div><\/div><div class=\"brand-image\"><img decoding=\"async\" loading=\"lazy\" src=\"\/wp-content\/themes\/capgemini2020\/assets\/images\/capgemini-engineering-white.svg\" alt=\"capgemini-engineering\"\/><\/div><\/div><\/div><\/div><\/header>\n\n\n\n<section class=\"wp-block-cg-blocks-group undefined section section--article-content\"><div class=\"article-main-content\"><div class=\"container\"><div class=\"row\"><div class=\"col-12 col-md-1\"><nav class=\"article-social\"><ul class=\"social-nav\"><li class=\"ip-order-fb\"><a href=\"https:\/\/www.facebook.com\/sharer\/sharer.php?u=https:\/\/www.capgemini.com\/?p=859914\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-fb\"><\/i><span class=\"sr-only\">Facebook<\/span><\/a><\/li><li class=\"ip-order-tw\"><a href=\"https:\/\/twitter.com\/intent\/tweet?url=https:\/\/www.capgemini.com\/?p=859914&amp;text=\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-tw\"><\/i><span class=\"sr-only\">Twitter<\/span><\/a><\/li><li class=\"ip-order-li\"><a href=\"https:\/\/www.linkedin.com\/shareArticle?url=https:\/\/www.capgemini.com\/?p=859914&amp;text=\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"opens in a new window\"><i aria-hidden=\"true\" class=\"icon-li\"><\/i><span class=\"sr-only\">Linkedin<\/span><\/a><\/li><\/ul><\/nav><\/div><div class=\"col-12 col-md-11 col-lg-10\"><div class=\"article-text article-quote-text\">\n<h2 class=\"wp-block-heading\" id=\"h-we-will-one-day-be-able-to-predict-sustainability-impact-of-design-changes-at-the-push-of-a-button-we-need-to-start-preparing-our-data-and-it-now\">We will one day be able to predict sustainability impact of design changes at the push of a button. We need to start preparing our data and IT now.<\/h2>\n\n\n\n<p>In previous articles, we discussed the importance of measuring the environmental impact of complex products \u2013 such as planes, trains and cars. The next big thing in sustainable engineering, we believe, is to move beyond manual data collection, and automate it via a model-of-models. This would allow design engineers to experiment in silico, making \u2018virtual\u2019 changes to design, materials, or supply chains, and immediately understanding how combinations of decisions change the overall lifecycle environmental impact.<\/p>\n\n\n\n<p>The idea is to create live models of every aspect of your product\u2019s lifetime environmental impact \u2013 from raw materials and manufacturing footprint, to in-use emissions, to end-of-life disposal or better yet re-use in a 2<sup>nd<\/sup> life. Then, to build an overarching model that combines all of these models into one. This would dramatically improve their ability to make sustainable design choices.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-moving-from-life-cycle-analysis-to-life-cycle-modelling\"><strong>Moving from life cycle analysis to life cycle modelling<\/strong><\/h2>\n\n\n\n<p>This challenge is often underestimated. Many think data management software and access to SAP is all that will be needed. But in reality, significant is work needed on data and IT architecture, as well as supplier and customer engagements, before these sub-models \u2013 let alone the overarching model \u2013 can be reliably built, connected, and trusted.<\/p>\n\n\n\n<p>Life Cycle Assessment (LCA) tools for reporting and even planning are advancing (<a href=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2022\/09\/GHG-Impact-Methodology-Thought-Leadership-Report.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">we have a methodology for calculating the carbon impact of projects<\/a>, for example). However, <a href=\"https:\/\/www.capgemini.com\/au-en\/insights\/research-library\/data-for-net-zero\/\">Capgemin<\/a><a href=\"https:\/\/www.capgemini.com\/insights\/research-library\/data-for-net-zero\/\" target=\"_blank\" rel=\"noreferrer noopener\">i Research Institute (CRI) research<\/a> found that 45% of organizations are not using their emissions data for decision-making in any way, beyond mandatory reporting. No one we are aware of is successfully using autonomous tools that take such data and use it to support decisions by modelling their impact on complex systems.<\/p>\n\n\n\n<p>However, progress is being made and best practice is starting to emerge from early experimentation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-gather-the-right-data-in-the-first-place\"><strong>Gather the right data in the first place<\/strong><\/h2>\n\n\n\n<p>The first challenge is gathering all the data you need for the sub-models.<\/p>\n\n\n\n<p>Your own emissions (Scope 1 &amp; 2) can be captured by deploying electricity and gas meters, weighing fuel, tracking vehicles, and energy invoices. That, combined with data on fuel values on the local energy mix, can be used to build models that calculate emissions. This is not technically challenging, but deployment can be a sizeable project in a large organisation.<\/p>\n\n\n\n<p>Emissions beyond your organisation \u2013 supply chains, product emissions, end-of-life (Scope 3) are trickier.<\/p>\n\n\n\n<p>For in-use emissions, products such as cars and planes now have sensors which collect detailed usage data. That data can feed physics-based models to derive energy use and emission, which can be updated in near real-time. Products without such sensors, however, will need to rely on models which approximate their impact.<\/p>\n\n\n\n<p>Suppliers, manufacturers, and disposal are harder to collect data on. Whilst some suppliers do their own LCAs, many do not collect even basic energy data, and there is little international standardisation.<\/p>\n\n\n\n<p>Some may respond to encouragement, especially if you are one of their big customers. Workshops and guidance on what you need may help, as may paying to install sensors at their site, share product and material related information, or access to shared reporting software that feeds your own supplier models. Sticks may support carrots, such as audits and threats to switch to suppliers with better environmental data. Making PCF (Product carbon footprint) data reporting a condition for any new customer will help in future.<\/p>\n\n\n\n<p>If all else fails, there are industry benchmarks to fall back on to calculate emissions of materials and parts, based on the materials and local energy mix where they were mined and processed.<\/p>\n\n\n\n<p>A particular challenge is integrating new concepts. Creating values for different steel types is not too tricky, since there is lots of historical data. Understanding the impact of an untested biomaterial is harder. Evaluating the impact of a whole new technology, such as hydrogen, is a real challenge. There\u2019s some chicken and egg; we need data to make the projection, but we want to project before we make the investment. The best middle ground is to make sensible projections based on scientific and engineering expertise, then gather data as the product evolves, which feeds directly back into the model to improve its predictive power.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-cleaning-and-clarifying-your-data\"><strong>Cleaning and clarifying your data<\/strong><\/h2>\n\n\n\n<p>All the data coming into your models needs to be well-defined, consistent, and in machine-readable formats.<\/p>\n\n\n\n<p>This starts with setting consistent policies for data collection and formats across your own organisation, and where possible communicating these to your value chain. An industry-led project in automotive, <a href=\"https:\/\/catena-x.net\/en\/\" target=\"_blank\" rel=\"noreferrer noopener\">Catena-X<\/a>, provides a good model for how data may be shared across the supply chain in future, and designing data capture and modelling that will integrate with this ecosystem is advisable.<\/p>\n\n\n\n<p>That may set a path for the future. But a lot of legacy data \u2013 internal and external \u2013 has evolved in silos over the years, from engineering data, to excel spreadsheets, to PDFs of technical drawings. That will necessitate an exercise to find, clean, convert and tag data.<\/p>\n\n\n\n<p>AI tools can \u2013 crawl IT systems, screen for the right data, and pull it from PDFs, Excel and so on, checking it, filling in gaps with industry standard figures, and pumping it out in a format that is <strong>Creating the right IT infrastructure for a model-or-models<\/strong>readable by your model.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-creating-the-right-it-infrastructure-for-a-model-or-models\"><strong>Creating the right IT infrastructure for a model-or-models<\/strong><\/h2>\n\n\n\n<p>All of this data is spread out in different parts of the organisation, suppliers, and customers. But we want a single source of truth so that all data will be \u2018Findable, Accessible, Interoperable, and Reusable\u2019 or FAIR.<\/p>\n\n\n\n<p>That means setting up a sustainability data hub \u2013 a master database in the cloud \u2013 where all relevant data is fed and validated.<\/p>\n\n\n\n<p>It will also need software customisation to ensure all sources of data \u2013 whether sensor management platforms or CRM databases \u2013 are collecting data in the right format and updating the master database in real-time.<\/p>\n\n\n\n<p>For sharing data across supply chains, or between manufacturers and customers, privacy and cross-border data-sharing rules also need to be considered. Blockchain-based databases offer good solutions to tracking parts and products securely as they move along the value chain. We already see companies like <a href=\"https:\/\/www.press.bmwgroup.com\/global\/article\/detail\/T0307164EN\/bmw-group-uses-blockchain-to-drive-supply-chain-transparency?language=en\" target=\"_blank\" rel=\"noreferrer noopener\">BMW<\/a> using blockchain to track supply chains, and <a href=\"https:\/\/www.siemens.com\/global\/en\/company\/topic-areas\/product-carbon-footprint.html\" target=\"_blank\" rel=\"noreferrer noopener\">Siemens<\/a> has a blockchain-based tool that lets suppliers share verified emissions data with customers, whilst keeping any underlying sensitive data confidential.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion-aim-high-create-value-along-the-way\"><strong>Conclusion: Aim high, create value along the way<\/strong><\/h2>\n\n\n\n<p>All of this will be very bespoke to each organisation. It will mean working with data and software experts, as well as domain experts familiar with the materials and processes that the data represents.<\/p>\n\n\n\n<p>This will not happen overnight, but the journey will also provide value. The best strategy is to have an eye on long-term value, whilst delivering more immediate returns. Improving data, building sub-models, and connecting up data streams will help life cycle assessments and small-scale modelling projects, as you gradually build to a systems-level model-of-models.<\/p>\n\n\n\n<p>Ultimately \u2013 as companies test ideas in silico and see how they ripple through the supply chain and product life \u2013 they will become better able to make smarter, and often disruptive, decisions in sustainable design. No one is there yet, but this is the direction of travel.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/section>\n\n\n\n<section class=\"wp-block-cg-blocks-wrapper-people-slider section section--expert-slider wrapper-people-slider undefined\"><div class=\"container\"><div class=\"content-title\"><h2 data-maxlength=\"34\" class=\"people-heading-title\">Author<\/h2><\/div><\/div><div class=\"slider slider-boxed\"><div class=\"container\"><div class=\"slider-window\"><div class=\"slider-list\">\t\t<div class=\"slide\">\n\t\t\t<div class=\"box\">\n\t\t\t\t<div class=\"row\">\n\t\t\t\t\t<div class=\"col-md-6 col-lg-4 box-img-wrapper\">\n\t\t\t\t\t\t<img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/www.capgemini.com\/au-en\/wp-content\/uploads\/sites\/10\/2022\/11\/Doro1.jpg\" alt=\"Dr. Dorothea Pohlmann\"\/>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div class=\"col-md-6 col-lg-8 box-inner\">\n\t\t\t\t\t\t<div class=\"row title-social-media-header\">\n\t\t\t\t\t\t\t<div class=\"col-md-12 col-lg-6 mbl-social-icon\">\n\t\t\t\t\t\t\t\t<ul class=\"social-nav\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"col-md-12 col-lg-6 box-container\">\n\t\t\t\t\t\t\t\t<div class=\"box-title\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"people-profile-title\">Dr. Dorothea Pohlmann<\/h3>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span>Senior Expert and Lead COE Sustainability, Capgemini Engineering<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"col-md-12 col-lg-6 social-box-container dkt-social-icon\">\n\t\t\t\t\t\t\t\t<ul class=\"social-nav\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"people-info\">Dr. Dorothea Pohlmann, holds a doctorate in laser physics, started her career 15 years ago at Capgemini Engineering. Her path began in a subsidiary at the time in search of new challenges that combined her consultant expertise with her technical know-how. Today, she is a Senior Expert in the Technology &#038; Innovation Office and Co-Lead of the \u201cCenter of Excellence Sustainability\u201d.<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div><\/div><\/div><div class=\"slider-nav\"><button class=\"slider-prev inactive\" aria-label=\"Slider-previous\" tabindex=\"-1\"><\/button><ul class=\"slider-paginator\"><\/ul><button class=\"slider-next\" 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This site is optimized with the Yoast SEO Premium plugin v22.8 (Yoast SEO v22.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Getting ready for predictive lifecycle assessment models - Capgemini Australia<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.capgemini.com\/au-en\/insights\/expert-perspectives\/getting-ready-for-predictive-lifecycle-assessment-models\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Getting ready for predictive lifecycle assessment models\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.capgemini.com\/au-en\/insights\/expert-perspectives\/getting-ready-for-predictive-lifecycle-assessment-models\/\" \/>\n<meta property=\"og:site_name\" content=\"Capgemini Australia\" \/>\n<meta property=\"article:published_time\" 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