Many companies already have extensive sustainability data at their disposal. However, the real challenge lies in connecting this data and making it usable for well-founded decision-making. Modern data platforms and artificial intelligence (AI) can support ESG reporting.
Many companies are now required to disclose their impacts on the environment and society, as well as their standards of corporate governance (ESG). Reliable information on greenhouse gas emissions, climate targets, energy consumption, supply chain risks and sustainability initiatives is required. Within the European Union, these requirements are governed by the Corporate Sustainability Reporting Directive (CSRD).
For many companies, however, the greatest challenge is not collecting this data in the first place. Instead, sustainability information is often distributed across different systems, departments and external sources. Only when this information is brought together and placed within a common business context can reliable carbon balances and well-founded decision-making bases be created.
“The challenge for many companies is often not a lack of data, but rather its quality and connectivity. Data platforms such as SAP Business Data Cloud (BDC) enable sustainability data from a wide range of sources to be consolidated, allowing companies to derive reliable CO₂ analyses and well-founded recommendations for action,” says Ruth-Maria Katemann, Head of the Competence Center Analytics at retailsolutions AG.
ESG reporting across the value chain
To create a reliable carbon footprint, information from procurement, production, logistics and finance must be combined with external emissions data. Modern data platforms provide a common data foundation for this purpose. They consolidate operational information from ERP, production and logistics systems and supplement it with external data such as emission factors, supplier information, industry benchmarks and regulatory requirements.
This creates end-to-end transparency across the entire value chain – from raw material sourcing through to the finished product.
“SAP Business Data Cloud, for example, creates an enterprise-wide single source of truth. Different data sources are harmonised, enriched and connected through a common sustainability data model. While SAP Datasphere enables data from different SAP and non-SAP systems to be harmonised and brought into a common business context, SAP Analytics Cloud enables the analysis, visualisation and simulation of sustainability scenarios. As an Intelligent Application within BDC, Sustainability Control Tower also provides comprehensive ESG reporting and management capabilities,” explains Katemann.
Once sustainability data has been consolidated, artificial intelligence opens up additional opportunities. It automates complex analyses, improves data quality and supports companies in managing sustainability initiatives in a targeted way.
AI integration for real-time carbon transparency
The ability to accurately calculate emissions across the supply chain is particularly relevant. To achieve this, operational business data can be combined with emission factors and actual Product Carbon Footprints (PCFs) from suppliers.
Through SAP Sustainability Data Exchange (SDX), companies can exchange primary data directly with business partners, replacing estimates and industry averages with reliable emissions information.
SAP Sustainability Footprint Management calculates PCFs and company-wide emission values and creates the Corporate Carbon Footprint (CCF). The solution links material and energy flows with emission factors, enabling a detailed analysis of CO₂ emissions at product, process and company level.
“The integration of Generative AI and machine learning plays a particularly important role. These technologies automate the mapping of emission factors and support processes such as data cleansing and anomaly detection. In addition, AI-based models can forecast emission trends, identify hotspots in supply chains and highlight optimisation opportunities,” reports Katemann.
AI also simplifies the creation of sustainability reports: Generative AI can prepare ESG reports, automatically process data and generate text drafts.
“In a recent project, we implemented the Sustainability Control Tower and the associated reporting capabilities. One of the key challenges is assigning the appropriate emission factors to procurement data. The use of AI can significantly simplify the mapping of emission factors,” adds Katemann.
ESG reporting as the foundation for environmental and economic decisions
The insights gained have a direct impact on key areas of the business. In procurement, suppliers can increasingly be evaluated not only based on price and quality, but also on their carbon footprint.
In supply chain management, analyses enable companies to identify lower-emission transport routes and more efficient supply networks. Production managers gain transparency into particularly energy- and emission-intensive processes and can identify targeted optimisation opportunities.
As a result, sustainability is evolving from a pure reporting obligation into a manageable business performance indicator. When sustainability and financial data are analysed together, the environmental and economic impacts of decisions can be assessed holistically for the first time.
ESG reporting is therefore becoming a foundation for both operational and strategic business decisions.
Ruth-Maria Katemann is Head of the Competence Center Analytics at retailsolutions AG.
Please note: This article is an English translation of the original German version. In case of any discrepancies, the German version shall prevail.
