The Portfolio-Wide Approach to Measuring Product Carbon Footprints

Editor’s Note: This guest column reflects the views of AspenTech and does not necessarily reflect the opinions of Chemical Processing or constitute an endorsement of any specific technology or its capabilities.

Chemical manufacturers are facing increasing global pressure to provide transparency across their supply chain as customers expect more visibility into key environmental impact categories. Customers often consider environmental performance metrics as part of their decision-making process for purchasing materials. In addition, understanding “hot spots” across the supply chain with significant negative environmental impact allow chemical companies to consider where to intervene.

Calculating product carbon footprints (PCFs), best described as a product’s greenhouse gas emissions up to the point at which it leaves the factory (cradle-to-gate), is one of the most common ways for a chemical process company to provide that much-needed visibility.

Typically, PCFs are generated by modeling individual products in a spreadsheet or lifecycle assessment (LCA) software that requires significant upfront costs. This product-by-product approach is both time- and resource-intensive, and it often requires engineers to manually source operational data from each industrial site. This process can take three to nine months per finished product, and once a PCF is done, it is typically valid for no more than three years. This becomes untenable at scale considering most chemical companies produce hundreds of finished products.

Providing externally verified PCF values to customers has become table stakes, but PCFs are tedious to generate, and the best path to portfolio-wide PCFs is unclear.

A Platform Approach

One way to tackle this problem is to adopt a platform approach to generate portfolio-wide PCFs. Ideally, a platform approach integrates existing business processes and software, such as enterprise resource planning (ERP) systems. Specialized LCA software is an option, but it carries high upfront setup costs and is inherently isolated from other business tools and processes, thus requiring duplicative efforts.

An innovative approach to scale PCF calculations involves expanding the capabilities of the organization’s existing supply chain planning optimization infrastructure that is used to evaluate strategic capital-investment decisions. Existing integrations between the supply chain economics optimization platform and ERP system can be readily leveraged, and strong data governance processes already exist for data inputs. 

With a platform strategy such as this, a company can model its entire business with all its production sites and complexities, consider materials and energy flows, incorporate inputs across the entire supply chain and allocate greenhouse gas (GHG) emissions to products. Calculating PCFs soon evolve from a customer request to an integral part of decision making. 

Lastly, using an existing platform not only reduces total cost of ownership across the technology stack, but it also increases the return on investment from the supply chain economics optimization software. 

To be successful in relying on a supply chain platform to generate PCFs across a product portfolio, companies must take a methodical approach to building a scalable model that can be optimized with the right data from across the value chain. 

A Strong Data Foundation 

A foundation of quality data is critical to almost any cross-functional initiative. For most chemical processing companies, a planning optimization cost-to-serve capability in the company’s supply chain software may already exist to support key business metrics, such as capital deployment decisions. To scale PCF calculations and provide insight into both economic and sustainability dependencies, the model must be enhanced with a GHG-to-serve capability.  

The GHG-to-serve capability requires four key components: 

• Alignment with globally recognized emissions reporting standards 
• Critical sustainability-relevant data such as emissions from reactions, electricity, steam and waste streams often missing from ERP systems
• Carbon intensity of input streams and services
• Methods to handle multi-functionality

This enhancement is best led by a collaboration between engineering, LCA and supply chain talent who not only deeply understand the organization’s sites and data validations, but are already managing the supply chain planning economic optimization model. The integrated model serves as a single source of truth on materials and energy flowing in and across the company, and it must be consistently maintained. 

Optimizing Business Planning Systems

Usually, systems like a company’s ERP are originally deployed for very different reasons than to support manufacturing or strategic economic studies. An ERP system’s main purpose is to provide quarterly accounting updates, so a culture of data stewardship, data integrity and ingenuity is critical upon enabling a supply chain integration and feeding the model with manufacturing process data. 

A deep understanding of the organization’s business systems and material flows lends itself well to fresh perspectives on how data ingested into the model can be applied in different ways. For instance, a material that was once viewed strictly through a cost-per-unit lens also could be evaluated based on how much energy is needed to move it, the steam needed to treat it and the associated GHG emissions. With this data, decision makers have visibility into scenarios to optimize both sustainability and economics, enabling robust decisions to prioritize projects for the business and the planet. 

Planning for the Future

Once a reliable data model is established, there are many possibilities for scale. For instance, it’s possible to use the supply chain economic optimization model to calculate PCFs years into the future by inputting variables on future scenarios, such as predicted electricity needs, renewable energy or regulatory shifts. With the right model, it becomes much easier to execute data-driven, long-term planning and production optimization.

Having the ability to calculate PCFs across an entire site, supply chain or product portfolio will strengthen purpose-driven companies’ ability to create a more resilient world. As calls for supply chain transparency continue, advanced use of data, modeling and digital technology will introduce a new level of rigor, efficiency and insight into major CAPEX decisions that support both decarbonization and economic goals.