Tag: Product Design

The Core Problem: Complexity Has Outpaced the Tools

The complexity of modern product portfolios and multi-tier supply chains has outpaced what traditional EHS and sustainability tools can handle. Companies are now being asked product-specific, substance-level questions by customers, regulators, and investors. Most lack the integrated data infrastructure to answer them.

This is not a niche problem. Enterprise manufacturers managing millions of products and tens of thousands of chemical substances cannot generate reliable life cycle data, Scope 3 emissions figures, or audit-ready compliance records using spreadsheets, disconnected ERP systems, or manual research. The volume and precision required make human-scale processes structurally unworkable.

AI is entering this space not because it is fashionable, but because there is no other path to scale.

What Is Actually Working vs. What Is Still Hype

The panel was direct on this. AI in EHS and sustainability is generating real value today in specific, well-scoped use cases, and falling short where the underlying data foundation is missing.

What’s working today:

Automated LCA and PCF generation at product and configuration level, where AI processes full material declarations, maps substance-level data to background databases, and generates traceable life cycle inventories without manual modeling effort. AI-assisted chemical data modeling for substances where no emission factors or LCI datasets exist, using synthesis and pathway data to fill gaps rather than defaulting to averages or proxies. Continuous compliance monitoring against expanding regulatory frameworks, where AI matches BOM-level data to substance watchlists in real time. Scope 3 supply chain mapping across multi-tier supplier networks, surfacing hotspots and prioritizing data collection where it matters most.

Still more promise than reality:

Fully autonomous sustainability decision-making without expert validation. AI cannot produce ISO-compliant outputs without human oversight of methodology and data quality. Generic large language model deployments without deep sustainability domain training, the specificity of EHS methodology, LCA system boundaries, and substance-level compliance cannot be approximated by general-purpose models. And AI layered on top of structurally broken data processes will only create fragmented, siloed, unvalidated inputs produce unreliable outputs regardless of the model.

The consistent finding: AI works when it is applied to specific, high-impact use cases on a structured data foundation. It does not work as a substitute for that foundation.

The Real Bottleneck Is Data Readiness, Not Model Capability

One of the most technically substantive discussions in the keynote focused on where enterprise organizations actually get stuck. Not in AI capability, but in data readiness.

Consider what it takes to generate a product carbon footprint for a manufacturer with a complex chemical portfolio. Measured LCI datasets and emission factors exist for only a fraction of the substances involved. The remainder must be modeled from synthesis pathways, process data, or representative chemical categories. For a single product, dozens of custom LCA datasets may need to be generated from hundreds of candidate substances. Across a portfolio of millions of products, this is a multi-year data engineering challenge.

The approach that works is incremental and methodologically rigorous.

• Map existing coverage first. Identify what background database coverage already exists and where manufacturer-provided LCA data can be matched exactly. This scopes the true gap before any modeling begins.
• Prioritize by impact. Focus custom dataset generation on substances with the greatest frequency and material contribution across the portfolio. Starting with the most-used materials delivers meaningful coverage without attempting to solve everything at once.
• Model the long tail by category. Remaining substances can be grouped into chemical categories, solvent classes, inorganic groups, and represented by datasets with defined variance, min/max ranges, and documented error margins. This is scalable and auditable.
• Handle marginal contributors appropriately. Substances that contribute negligible quantities to the final product can be represented using high-level grouped data, such as average organic or inorganic chemical classifications, without materially affecting output accuracy.
• Align on methodology before scaling. ISO compliance requirements, error margin conventions, and how averages are applied in reporting must be agreed between technical and sustainability teams before outputs are used for external disclosure.

This is not a “plug in AI and get answers” workflow. It is a structured, expert-guided process in which AI dramatically accelerates each step. The methodological rigor is still required. Makersite is built to support exactly this kind of layered, scalable approach, from data ingestion and substance-level mapping through to audit-ready LCA and PCF outputs.

How the Sustainability Leader Role Changes in 3 to 5 Years

The panel’s view here was grounded rather than speculative.

The shift is not from human judgment to automated decision-making. It is from reactive reporting to real-time insight generation. Sustainability leaders who today spend significant time on data collection, supplier follow-up, and manual LCA modeling will increasingly function as analysts and strategists, interpreting AI-generated outputs, setting data quality standards, and embedding sustainability criteria directly into product design and procurement decisions.

The implication for organizations is clear: the value of the sustainability function is increasingly determined by the quality of its data infrastructure, not its headcount. Teams that build structured, auditable data pipelines now will have a structural advantage in regulatory readiness and decision speed within the 3 to 5 year window.

Where Scope 3 Is Hardest

Scope 3 emissions, particularly Category 1 purchased goods and services, remain the most difficult area, and the panel was specific about why.

The problem is not the emissions calculation. It is the absence of primary data at the supplier level. Most Scope 3 analyses rely on spend-based or industry-average approaches because supplier-specific, product-level emissions data does not exist in structured, accessible form. AI can model gaps with defined uncertainty, but it cannot compensate for missing primary data.

Organizations making the most progress on Scope 3 share three characteristics. They have built structured supplier data collection processes, full material declarations, BOM-level inputs, that feed directly into LCA and PCF workflows. They have invested in component-level modeling that can be reused across product families rather than rebuilt product by product. And they have established methodology alignment across sustainability, engineering, and commercial teams so that AI-generated outputs are trusted and acted upon.

The approach that consistently does not work: attempting to resolve Scope 3 at the portfolio level with aggregate methods while continuing to operate disconnected, product-level data systems.

What an AI-Enabled Sustainability Decision Looks Like at the Design Level

The most forward-looking discussion centered on product design, where the greatest leverage exists.

In the next three to five years, engineers making component selection decisions will have real-time access to sustainability impact data at the substance and configuration level. Selecting a different supplier or substituting a material will immediately surface its LCA, compliance, and Scope 3 implications before the decision is finalized, not weeks later during a sustainability review.

This is technically achievable today for organizations that have built the necessary data infrastructure. The constraint is not AI capability. It is the availability of structured, substance-level product and supplier data in a form that AI can use.

The leadership implication is significant: Sustainability decisions in the next decade will increasingly be made by product engineers, not sustainability teams in isolation. The function of the sustainability team shifts to building and maintaining the data systems, methodological standards, and AI tooling that make those decisions possible at scale.

One non-negotiable: AI-generated sustainability outputs require full audit trails. ISO-aligned PCFs and LCAs need traceable, validated data lineages. Explainability is a technical requirement, not an optional feature.

Watch-Outs as AI Gets Embedded in EHS Workflows

The panel closed with a frank assessment of where AI adoption fails in EHS and sustainability.

Treating AI as a substitute for data quality is the most common mistake. AI can model gaps and generate datasets for missing substances, but it cannot produce defensible outputs from structurally flawed inputs. Organizations that skip data foundation work before deploying AI will generate results that fail audit, regulatory, or customer scrutiny.

Neglecting methodology alignment is the second failure pattern. Different LCA system boundary definitions and allocation approaches can produce materially different results from the same underlying data. If sustainability, engineering, and commercial teams are not aligned on methodology before AI outputs are generated, those outputs will be contested internally before they reach any external stakeholder.

Underestimating the supplier engagement requirement is the third. Scaling sustainability data across complex supply chains is not purely a technology problem. Thousands of suppliers must participate in structured data collection for AI-generated outputs to reflect primary data rather than estimates. That requires change management and supplier enablement, not just software.

And finally: confusing speed with accuracy. AI generates outputs faster. Faster outputs with unquantified uncertainty are not more useful than slower outputs with defined error margins. Speed and methodological precision must be calibrated together.

The One Thing Leaders Should Understand Right Now

Organizations that will use AI effectively for sustainability in 3 to 5 years are the ones building structured data foundations today.

The technology is ready. The bottleneck is data, specifically, the absence of product-level, substance-level, supplier-validated data organized in a way that AI can work with. Progress comes from practical, incremental steps: mapping what data exists, identifying the highest-priority gaps, and systematically closing them through supplier engagement, AI-assisted modeling, and expert validation.

As Manuel noted ahead of NAEM OPEX/TECH26, “Progress tends to come from practical steps that build confidence, not from trying to solve everything at once. It’s an evolution, not a revolution.”

That remains the right starting point.

In Practice: How Lenovo ThinkPad Solved This at Scale

The challenge described throughout this keynote is not theoretical. Lenovo’s ThinkPad team worked through it directly with Makersite.

ThinkPad faced increasing pressure in enterprise procurement bids requiring configuration-specific, ISO-aligned Product Carbon Footprints. A single model-level PCF cannot represent variation across customer configurations. Without configuration-level visibility, ThinkPad could not demonstrate how component choices influenced the final footprint. This was a measurable gap in competitive tenders.

The approach Makersite and Lenovo took maps precisely to the methodology described in this here in this article. Supplier Full Material Declarations were ingested and automatically converted into substance-level LCA models. More than 2.5 million FMDs processed through Makersite. Rather than modeling every product variant independently, ThinkPad shifted to a shared-component approach: SSDs, displays, memory, and chassis were validated once and reused across product families. Makersite then generated the highly granular substance-level LCAs for the parts and assemblies behind each shared component. Work that would have taken years manually.

The outcome: configuration-level, ISO-aligned PCFs generated at scale, certified, and usable in enterprise sales conversations. ThinkPad sellers can now demonstrate how specific component choices move the carbon footprint up or down, using traceable data rather than estimates.

Internally, sustainability, engineering, and commercial teams now work from the same data. That alignment between people, methodology, and system is what makes the outputs usable, not just accurate.

Next Steps

If your organization is navigating product-level sustainability data challenges, whether for LCA, Scope 3, PCF, or compliance, the starting point is understanding where your data stands and where the highest-impact gaps exist. Makersite works with enterprise manufacturers to build and scale that foundation.

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Masterclass Key Takeaways

Manufacturers today are navigating an increasingly challenging compliance landscape. Global regulations are evolving faster than ever, supply chains are more complex, and regulatory expectations demand far more than just ticking boxes. Modern product compliance now requires robust data management, seamless supplier collaboration, and continuous process optimization to keep pace.

Recognizing these challenges, Makersite’s material & substance compliance experts take a deep dive in our most recent online masterclass to walk through proven strategies to help North American manufacturers not only stay compliant, but scale their compliance operations efficiently, strengthen supplier engagement, and protect product availability.

Here’s what you need to know to build a scalable, resilient product compliance approach, and turn regulatory complexity into a competitive advantage.

The Evolving Compliance Landscape

Regulatory requirements are accelerating at an unprecedented pace, creating new challenges and complexities for manufacturers across every industry. Staying compliant is no longer just about keeping up, it’s about staying ahead.

Here’s a look at the biggest hurdles North American companies are facing right now.

Key Challenges for Manufacturers

• Complex and Expanding Regulations: Regulations like REACH, TSCA’s PFAS reporting rules, and RoHS exemptions are adding thousands of new substances to watch, often at an accelerating pace.
  
• Disjointed and Isolated Data Systems: Traditional tools like spreadsheets, ERP, and PLM platforms often operate in silos, making it challenging for organizations to establish seamless communication between systems. This lack of cohesion leads to disjointed, unstructured data that is difficult to integrate, analyze, and leverage effectively for decision-making. As a result, teams may experience inefficiencies, errors, and missed opportunities for growth and innovation.
• Fragmented Supplier Communication: Relying on emails and forms, without a centralized platform for managing supplier responses, approvals, and escalations, leads to confusion, delays, and errors. On top of that, suppliers are overwhelmed with requests from hundreds of different customer portals, making engagement and data collection even harder to scale.
• Compliance Addressed Too Late: Reactive compliance approaches don’t just risk shipment delays, costly redesigns, and regulatory fines. They also limit strategic options. Staying ahead of evolving legislation, like monitoring the SVHC Candidate List, enables companies to substitute risky materials early. New regulations like PFAS reporting in the US require companies to trace product data backwards, in some cases as far as January 2011.

The consequences of non-compliance are becoming more severe, and increasingly business critical. Without robust processes in place, manufacturers risk facing shipment holds, financial penalties, loss of customer trust, and even market bans. In some cases, a single missing declaration or outdated material can block product access to entire regions, leading to lost revenue, disrupted supply chains, and strained customer relationships.

The Exploding Regulatory Horizon

The challenge isn’t static; it’s expanding. Manufacturers must keep pace with key regulatory deadlines such as:

• California & New York PFAS Bans: Taking effect in 2025. These bans have significant implications for industries like Automotive, where PFAS are commonly used in coatings, upholstery, and other vehicle parts. Additionally, New Mexico’s HB 212, signed into law on April 8, 2025, makes it the third U.S. state, following Maine and Minnesota, to enact a broad PFAS ban.
• REACH Updates: Universal PFAS restrictions are currently under review, but what makes this regulation unique is that it doesn’t target specific substances, but an entire group of chemicals. This presents a particular challenge for industries like medical devices, where certain products can’t currently be manufactured without PFAS.
• Current discussions at ECHA indicate two possible directions: Industry may continue to use fluoropolymers only where no alternatives exist. Meaning if a competitor can produce a similar product without PFAS, you may be required to do the same. Secondly, consumer uses of fluoropolymers are still being considered for a complete ban.
• RoHS Lead Exemption Phaseouts: Changes expected in the next 12–18 months. The EU’s Restriction of Hazardous Substances (RoHS) directive has historically allowed certain exemptions for the use of lead in specific applications, particularly in complex electronics and medical devices where no viable alternatives existed. However, many of these exemptions are now under review and expected to be phased out in the coming 12–18 months. This presents a significant challenge for manufacturers, especially in sectors like electronics, automotive, and industrial equipment, where lead has been critical for soldering and high-reliability components. Companies relying on these exemptions need to act now to identify alternative materials, redesign components, or prepare for requalification processes, all of which can be costly and time-consuming if left too late.

The overlaps in these regulations—such as varying thresholds and contradictory rules between federal and state mandates (e.g., TSCA vs. California PFAS disclosures)—add further complexity.

Pro Tip

To remain competitive and compliant, manufacturers need scalable systems that enable centralized compliance tracking, cross-functional regulatory reviews, and ongoing horizon scans.

Supplier Engagement & Data Collection

Effective compliance starts with obtaining the right input data from suppliers. Without this, meeting regulatory requirements becomes an uphill battle. Leading organizations are overcoming this challenge by leveraging a centralized supplier portal, a single source of truth that not only streamlines data collection but also provides built-in escalation paths and approval workflows.

By equipping suppliers with a central portal that offers escalation and approval functionalities, companies can ensure faster response times, better data accuracy, and improved collaboration. This approach reduces confusion, minimizes back-and-forth emails, and provides full traceability across supplier communications, a critical advantage when managing complex global supply chains.

Minimum Data Requirements

Ensure seamless and comprehensive compliance by securing access to:

• Bills of Materials (BOMs): A detailed breakdown of all materials and components used in your products, essential for accurate regulatory reporting.
• Supplier-Provided Files: Full Material Declarations (FMDs) and Certificates of Compliance (CoCs) to ensure traceability and adherence to standards.
• SCIP and Regulatory IDs: Streamline automated submissions and maintain efficiency in meeting regulatory demands.

FMDs vs. CoCs: Understanding the Difference

• FMDs provide complete transparency, offering a robust framework for long-term compliance that evolves with regulatory advancements.
• CoCs, while suitable for immediate needs, require frequent updates to align with changing regulations—making them less sustainable for future-proof compliance strategies.

Pro Tip

Revolutionize your compliance approach with a focus on innovation, efficiency, and sustainability. By leveraging advanced data strategies, your business can stay ahead of regulatory demands while building a foundation for long-term success.

Simplify Supplier Collaboration

Simplifying supplier collaboration isn’t just about sending standardized forms. It requires the right technology to scale effectively. Equip your suppliers with intuitive, standardized formats like IPC 1752 to prevent fatigue and reduce friction. But to truly streamline the process, companies need a software solution that enables automated workflows for collecting, validating, and managing supplier data at scale.

Automation not only saves time for everyone involved but also reduces error rates and ensures data consistency, something manual processes simply can’t deliver when dealing with complex supply chains and evolving regulatory demands.

Automating Internal & External Compliance Reporting

Compliance demands transparency at every level. Here’s how automation transforms reporting processes.

• Drill into the details: Analyze BOMs at a granular level to pinpoint components and assess compliance risks with precision.
• Big-picture monitoring: Gain complete visibility across your portfolio with real-time dashboards tracking product status, supplier responsiveness, and key compliance metrics.

External Stakeholder Reporting

Streamline compliance management with automation that eliminates manual processes, delivering:

• Ready-to-submit regulatory documents (e.g., SCIP or ECHA submissions).
• Customizable dossiers tailored to meet customer and market-specific requirements.

Manufacturing enterprises need a centralized platform seamlessly integrates with ERP and PLM systems, ensuring stakeholders always have access to accurate, up-to-date compliance data.

Scaling Compliance Efforts-Why it Matters

With growing product lines and expanding global markets, manual compliance efforts no longer cut it. They fail to keep up with evolving regulations, hamper market readiness, and increase operational costs.

Next-Generation Solutions for Scalable Compliance

• Leverage Automation: Automate workflows and data flows to reduce manual errors and accelerate compliance efforts.
• Adopt Standardization: Use globally accepted data formats (e.g., IPC), enabling smoother communication across teams.
• Adapt to Change: Implement systems that not only flex with new regulatory requirements but also enable companies to proactively identify and substitute substances or materials, even before new regulations come into force. This future-proofing approach helps avoid costly redesigns, reduce risk, and accelerate market entry.

By investing in digital tools, companies can significantly reduce time-to-market while managing the growing complexity of product compliance. You can accelerate data processing, automate regulatory checks, and helps identify potential product compliance risks early, even across large, fragmented supply chains. This not only speeds up supplier data validation but also enables smarter decision-making when it comes to material substitutions, regulatory reporting, and risk mitigation.

Looking Beyond Compliance

Compliance isn’t just a legal mandate; it’s a strategic advantage and an untapped opportunity to drive sustainability and innovation.

Product Compliance Managers sit on a gold mine of product and material data, often without realizing its full potential. The detailed supplier, material, and substance information collected for compliance purposes forms the perfect foundation for conducting Product Carbon Footprints (PCFs) and Life Cycle Assessments (LCAs) at scale.

This creates a unique opportunity to break down organizational silos between product compliance and product sustainability teams. By leveraging compliance data more strategically, companies can accelerate sustainability initiatives, reduce Scope 3 emissions, and design greener products — all without starting data collection from scratch.

Driving Sustainability Through Innovation

Enhancing BOM data with material insights empowers manufacturers to:

• Conduct precise Life Cycle Assessments (LCA) and calculate accurate Product Carbon Footprints (PCF).
• Monitor and report Scope 3 emissions for comprehensive corporate sustainability strategies.
• Implement Eco-design Scenarios to replace non-compliant materials with greener, cost-efficient alternatives.

Strategic Recommendations

Adopt a proactive, scalable compliance strategy designed to drive efficiency and ensure sustainability.

1. Leverage Supplier Data: Analyze existing data to map compliance gaps and address deficiencies with targeted outreach.
2. Minimize Supplier Fatigue: Implement long-term data solutions like FMDs to reduce repetitive requests and build stronger, collaborative supplier relationships.
3. Bring Compliance In-House: Enhance transparency, reduce reliance on external consultants, and stay agile in adapting to regulatory changes.
4. Automate Reporting Processes: Deliver precise, real-time reports that integrate seamlessly with external systems, ensuring compliance with ease.
5. Future-Proof Your Strategy: Build scalable systems that adapt to evolving regulations, emerging markets, and sustainability requirements, keeping your business ahead of the curve.

With these steps, you can transform compliance from a challenge into a strategic advantage, driving innovation and fostering sustainable growth.

What to Do Tomorrow — Whether You Have a System in Place or Not

Have:

• Grade your existing BOMs for compliance gaps and missing data points. This helps prioritize where action is needed most.
• Set up dashboards to provide live updates to stakeholders on product compliance status, supplier responsiveness, and upcoming regulatory risks.
• Evaluate supplier alternatives early to avoid costly, last-minute substitutions, especially for materials flagged by upcoming regulations like PFAS or RoHS.

Have Not:

• Start by mapping what data you have today, often in spreadsheets, ERP, or PLM tools, and identify gaps.
• Engage with suppliers to begin collecting material declarations in standardized formats like IPC 1752.
• Explore solutions like Makersite to centralize your compliance data and automate reporting, laying the foundation for scalable, future-ready compliance processes.

Compliance doesn’t have to be a burden. With the right tools and approach, it becomes a competitive advantage, helping you enter new markets faster, reduce operational risk, and design more sustainable, innovative products.