A Sustainable footprint
footprint data management
A Product Environmental Footprint includes many types and categories of data, usually scattered in different systems, with many attributes, and formats, in need of compilation, centralisation, quality assurance, and syndication.
Footprint Data Management means managing the product’s life cycle data and the different Category Impacts included in the EU’s PEF methodology such as water, carbon, and/or land use on a capable multi domain Master Data Management.
Footprint Data Management is not just a data-driven approach to manage and control your footprint and sustainability data, it is also a strategic and sustainable solution through its stability, flexibility, and scalability. Being in control of your footprint means being prepared for any future demands or needs as well as starting to act on lowering your impact.
More than carbon calculations
There are more than 200 environmental labels active in the EU, and more than 450 active worldwide. Many believe that sustainability will be just as regulated as financial reporting in a near future, and most companies and nations have already set up precise targets for carbon emissions for the upcoming years. Are you prepared?
In the proposal from the EU Commission for 2023 the Regulation on Ecodesign for Sustainable Products addresses product design and sets new requirements. “Product-specific information requirements will ensure consumers know the environmental impacts of their purchases. All regulated products will have Digital Product Passports.”
“The information to be included in the product passport may include information such as the environmental footprint of a product, information useful for recycling purposes, the recycled content of a certain material, information about the supply chain, and others.”
Compliant or leader
The market demand for environmentally responsible companies and more sustainable commerce and products is intensifying. Working strategically with sustainability data on an MDM platform like other business critical data creates the transparency necessary for your organisation to access and share the data needed to make better analysis and make better decisions. This increase in business agility will also make it easier to comply to new regulations as well as report progress to owners, investors and consumers and an opportunity to develop more circular and sustainable business models, solutions and products.
A data-driven approach
Footprint Data management is a data driven approach to sustainability work. By automating the processes to compile as well as syndicate actual footprint data from external sources and by integrating necessary internal systems, you’ll free up operative time to harness the real strategic value of the data.
MDM platforms have built-in functionalities for data quality management and ensure that updates and changes are performed everywhere simultaneously. You need both access as well as actual data to analyse where and how you can possibly decrease the total impact.
Better data means better business which can ultimately create a better world.
customer case Oatly
Oatly wanted a scalable and capable platform to:
• calculate, measure and manage their products’ footprints
• utilise the same platform for PLM processes
• sustainability reporting
• show their products’ actual PCF numbers on the consumer packages
Oatly not only wanted a sustainable solution that could scale, but early on they adopted a “know your numbers to show your numbers” data management strategy.
Listen to Oatly’s testimonial by clicking the image to the left.
From strategy to action
Together with Sustainability and Future Strategist Consultants at Kairos Future we have developed a model for helping companies go from vision to continuous process development and to deliver on their sustainability goals. Kairos Future can help you to identify your focus areas, ambition level and what to measure. When you are ready, we will quantify the data you need to collect and calculate, what your data streams and workflows look like, and identify all data sources and parties.
We do this together in a Think phase, a process analysis, to really understand what the current situation looks like and as a starting point for an implementation analysis which focuses on building the optimal data model for you as well as discovering what integrations will be needed.