Here’s how to move beyond net zero promises, to a scalable, verifiable and data-driven approach to reporting on products’ environmental impact.

When it comes to environmental sustainability, businesses run a real danger of engaging in more talk than action. In research conducted by Economist Impact and supported by Cognizant, in fact, the vast majority (about 90%) of organizations cited sustainability as a key aspect of being a future-ready business. However, far fewer (one-third or less) said they were taking a data-driven approach to such tasks as monitoring water use, impact on biodiversity and carbon footprint across the value chain.

A meaningful action that businesses can—and soon will have to—take is providing comparable and independently verified information on the environmental impacts of their products, components and materials. This information is increasingly required not only by regulators but also by customers, markets and internal departments, like R&D and procurement.

To do that, though, they will need to invest in a more automated approach to gathering, managing and reporting on data to enable product environmental footprint assessments across the product portfolio.

Injecting rigor into green claims

So far, environmental impact data reporting has been unregulated, which has often led to poor assessments and unsubstantiated claims. Soon, however, companies can expect a wave of regulations across the globe to reduce green claims and improve product sustainability information.

For example, the European Commission’s recent proposal on an Ecodesign for Sustainable Products Regulations suggests that any environmental claims will have to be based on the Product Environmental Footprint (PEF) method.

The PEF method builds on Life Cycle Assessment (LCA) and its requirements exceed existing ISO standards. The method includes primary data from a company’s own operations and processes, strict data governance to ensure quality, and independent verification with possible on-site visits.

Having a robust and standardized reporting method like PEF in place represents a great opportunity for businesses—as well as a burden. Businesses will benefit from the increased transparency that allows them to compete based on their environmental performance. At the same time, they will need to deal with the overhead and cost involved in meeting the strict requirements of the PEF method.

Challenges posed by existing infrastructures

Today, most companies face several challenges when assessing the environmental impact of their products and services:

• Diverse systems require manual data collection
  
  
• Inconsistent data entries and formats make it difficult to identify, clean and filter data for easy processing
  
  
• Redundant and unoptimized workflows in PEF-based Life Cycle Assessment (LCA) include overlapping and common processes across products
  
  
• Reports need to be manually written
  
  
• There’s a general shortage of LCA experts, and external expertise is costly for performing environmental impact assessments. As a result, companies can currently only conduct assessments on selected products with low data quality.

To comply with PEF requirements and scale up PEF-based Life Cycle Assessment (LCA) across the entire product portfolio, companies need to invest in acquiring verifiable data from their own operations, processes and supply chain, using digital and Internet of Things (IoT) solutions. They also need automated data management and reporting to enable PEF assessments at scale and across the product portfolio.

Such automation can largely reduce reporting overhead, while also opening the door to transparency and auditability in providing verified and high-quality environmental impact data at scale.

Three steps to scaling impact assessment

Here are three steps companies can take to enable product environmental footprints at scale.

1. Analyze the current assessment process and identify the business case for process automation.
   
   An important first step is to look at the current processes in place for lifecycle and product environmental footprint assessment and answer the following questions: How long does it currently take to conduct an assessment on a single product? What are the process steps, and who is involved? What share of the data from operations and suppliers is secondary vs. primary data? Where are the pain points, redundancies and error sources in the data flows and process steps?
   
   The answers to these questions can result in an end-to-end process map, revealing low-hanging fruit for intelligent process automation.
   
   
2. Understand data quality requirements and develop a uniform data structure.
   
   A further step is to develop a uniform data structure that allows for interlinked and common data across Life Cycle Assessment (LCA) and product environment footprints, based on existing assessments and product-specific requirements. In most enterprise architectures, product lifecycle management (PLM) is a useful starting point for this. (Cognizant is a recognized leader in PLM implementation.)
   
   Product lifecycle management is used to identify and report on materials, based on a product’s bill of materials data. Classically, this has enabled simple and cost-effective reporting to demonstrate compliance with EU directives like RoHS, WEEE or ELV. 
   
   The problem with PLM systems is that often, data entries are not consistent and standardized (e.g., "kg” and "kilograms” are used interchangeably). This creates high overhead in cleaning up the data and making it useful. A uniform data structure solves these problems and creates a single source of truth for the LCA. 
   
   
3. Enable automated data retrieval and mapping from various enterprise systems. 
   
   Another crucial step is to make it easy to identify, link, filter and clean data sources from enterprise resource planning, energy management and other enterprise systems, and automatically integrate and map this data in a dedicated Life Cycle Assessment tool.
   
   Cleaning may require intelligent solutions such as fuzzy logic mapping (e.g., so that “potato” and “potatoes” are recognized as the same thing) and better data governance. The lifecycle inventory processes can then be automatically mapped to the different lifecycle phases. Additionally, processes that overlap across products can be automatically mapped, minimizing redundant workflows.

Taking meaningful environmental action

This is not an exhaustive list but is a starting point to automate PEF-based Life Cycle Assessment (LCA) and meet increasingly stricter standards on the data quality and verifiability of product sustainability information.

By investing in more automation to analyze the environmental footprint of products, companies can open the door to more sustainable innovation and differentiated competition. They can set themselves ahead of the pack based on their environmental performance and save untold hours spent on data collection, modeling and verification.

Best of all, by moving beyond sustainability lip service, they’ll stand out from the crowd when it comes to taking meaningful environmental action.