Feature article - Why the ‘AI’ in ‘sustainability’ matters more than ever for chemical companies

Dr Marko Lange, co-lead of the Chemical Industry business unit, and Sergey Nozhenko (above), chemical industry product specialist, at SAP detail the critical role intelligent capabilities can play in helping companies meet ESG targets and mandates

For chemical companies, there is no bigger issue than sustainability. In a 2024 survey of chemical industry executives orchestrated with the help of the American Chemistry Council, almost two-thirds of respondents said improving sustainability is their top priority over the next two years. Almost half, 47%, identified it as the industry’s single biggest challenge.

Whether the priority is to cut product carbon footprint, incorporate more renewable energy into processes, adopt more sustainable procurement practices and/or pursue other pathways to becoming a more sustainable business, chemical companies are going to need to rely heavily on fresh, high-quality data from internal and external sources—and on intelligent tools to manage and analyse that data—to meet that challenge.

Here is where artificial intelligence (AI) can play an important role. For chemical companies, meeting sustainability goals and fulfilling mounting regulatory compliance responsibilities will take a combination of solid data and powerful data analytics tools, along with a holistic approach to sustainability that encompasses not just their own operations but their supply chains too.

Companies must have the means to establish sustainability-related parameters and targets across the business and the supply chain to measure and report on progress toward meeting those targets, and to identify areas where they need to course-correct to stay within the parameters they have set. The concept here is to employ a ‘control tower’ or ‘eye in the sky’ that gives a manufacturer a centralised, 360° view of all things sustainability related.

We see four areas where AI can give chemical companies the greater visibility and insight they need to succeed with their sustainability initiatives. These are discussed in turn below

1. Process optimisation

Simulations using intelligent modelling tools can be applied from the control tower to real-world data to show companies how to optimise processes so they are more resource-efficient and less emission-intensive. They can provide insight into the carbon footprint associated with each product, plant, process and profit centre to identify the specific production steps and raw materials causing emission ‘hot-spots’.

For example, AI-powered systems can continuously forecast conditions and suggest optimal processing parameters, which boosts production output and/or reduces material cost, while also bolstering efficiency for better stewardship of resources. AI can also help identify areas where renewable energy can be substituted for fossil fuels to facilitate carbon footprint and energy transition planning.

2. Product design & development

How can products be designed for recyclability, so more secondary material can be extracted from them? How can they be designed to eliminate hazardous substances, and incorporate more recycled material? What products are the most viable candidates to use greener feedstocks from renewable sources, and specifically which ones make the most sense?

AI can help companies find answers to these kinds of questions. For example, European consumer products company Henkel is using AI-driven tools to design and make products and packaging with less plastic and a lower carbon footprint.

3. Procurement

AI can be built into processes to enforce compliance with sustainability-related parameters throughout a procurement organisation. By analysing the sustainability performance of companies across the procurement network—and of companies that are not already part of the network but perhaps should be—it can offer guided sourcing recommendations based on sustainability-related parameters and intelligent résumé analysis.

From the control tower, AI also can analyse supplier data to develop a clear picture of what is happening at each stage of the supply chain journey, with the ability to track and trace the origin of raw materials so that company decision-makers clearly understand the exposures related to how and where materials are sourced, transported, etc. From there, AI can provide suggestions for how to reduce those exposures.

4. Collecting & curating data for reporting & compliance

The chemical industry’s presence in so many industrial value chains and downstream industries, along with the global proliferation of carbon-reduction and similar regulations, mean that individual chemical companies must shoulder a growing responsibility to provide sustainability-related information to their customers. That, in turn, is ushering in a new era of collaborative data-sharing and standards creation across chemical supply and value chains.

An initiative called Together for Sustainability (TfS) is one example. Henkel and BASF are among the participants in TfS to separately create supplier information-sharing programmes for reporting emissions, including Scope 3, along with energy, water consumption and waste volumes. BASF is mobilising chemical and process industry stakeholders around a Strategic CO₂ Transparency Tool (SCOTT) methodology for calculating product carbon footprint to align with a TfS standards.

Meanwhile, some of the world’s largest chemical producers, suppliers, customers and technology partners are participating in a still-formative effort called Chem-X to create an open data space to enable companies to share information about the origin, content, properties, carbon footprint, custody journey and compliance requirements of specific materials, compounds and products, without compromising the sovereignty or security of their data.

Outlook

All these efforts are predicated on collecting, standardising and sharing data. And that is where AI can help. Generative AI, for example, can help companies identify collect, standardise and report Scope 3 emissions data from various sources across the value chain.

AI can extract and standardise data within safety and technical data sheets, certificates of analysis, and other formats and sources for dissemination across the value chain, down to the end consumer. It can also determine which laws and compliance regulations apply to specific products in specific markets for compliance purposes, then identify exactly the kinds of data a company must collect for regulatory reporting.

These kinds of capabilities lay the groundwork for digital passports that enable customers, regulators and other stakeholders to access a range of information about specific products, including physical attributes, content (renewable vs. non-renewable), material source, carbon footprint, certifications, recyclability and more. These passports will be required for products manufactured or sold into the EU from 2027 as part of the EU Green Deal. It probably will not be long before similar reporting requirements take hold in the US, as regulators, consumers and companies themselves push for a more sustainable chemical industry.

Contact:

Sergey Nozhenko

Solution Expert Chemicals

SAP

+351 933 011 953

[email protected]