How Data, Standards, and Automation Are Reshaping Environmental Product Declarations

Key Takeaways from the Digital EPD Session at eClad Conference

1. The EPD market is Scaling Fast, but the Foundation is Still Fragmented

EPDs are growing rapidly across industries, driven by regulatory pressure, customer demand, and procurement requirements. But the underlying systems have not kept pace. As Robert highlighted, the EPD ecosystem has evolved organically over time. Different regions, standards, tools, and workflows have developed independently.

The result is a fragmented landscape where:

    • Data formats are inconsistent
    • Processes vary by region and program operator
    • Digital workflows are not fully standardized
    • Scalability remains limited

This creates a fundamental challenge. The industry is trying to scale outputs without first standardizing the data infrastructure.

2. The Real Bottleneck is Not EPD Creation – It’s the Data.

Across every EPD workflow, the same bottlenecks appear:

    • Data collection
    • Data transformation
    • Data completeness and consistency
    • LCA modeling
    • EPD & LCA verification
    • Non-harmonized calculation rules

These challenges are not new. But they become exponentially more complex as companies try to scale across hundreds or thousands of products. The takeaway is clear: EPD challenges are not primarily about reporting. They are about data architecture.

3. Digital EPDs are the Path Forward. But Only if Done Correctly

Digital EPDs have the potential to solve many of these challenges.

They enable:

    • Automated data validation
    • Structured, machine-readable datasets
    • Faster integration into downstream systems
    • Scalable lifecycle assessments

However, the current reality is more complicated. In many cases today, the process is still reversed. Teams generate a PDF first, then manually transfer data into digital formats. This introduces errors, inconsistencies, and inefficiencies.

The future state is the opposite. A digital dataset should be the single source of truth. From that, any human-readable format, including PDFs, can be generated.

4. Verification Must Evolve to Support Automation and Scale

As EPD volumes grow, traditional verification approaches become a bottleneck.

The current verification guidelines are often:

    • Tool-specific instead of tool-agnostic
    • Lacking detailed requirements
    • Not designed for automated workflows

To address this, new approaches are emerging that focus on:

    • Tool-based verification frameworks
    • Logging and traceability of data and mapping
    • Scalable validation processes
    • Integration of AI-assisted verification

The goal is not just faster verification. It is more consistent and reliable verification at scale.

5. The Shift to Digital Enables Interoperability and Global Alignment

One of the biggest barriers to scaling EPDs today is lack of harmonization. Different program operators, regions, and standards require different formats and calculations. This creates duplication, inefficiency, and inconsistencies.

Digital EPD initiatives aim to solve this by:

    • Standardizing machine-readable formats
    • Enabling interoperability across systems
    • Reducing reliance on region-specific PDF formats
    • Supporting global comparability

This is a foundational shift. It moves EPDs from static documents to interoperable data assets.

6. EPDs are Not the End Goal. Decision-Making Is.

One of the most important points from the session was simple but critical. Companies are not creating EPDs just to have EPDs. They are creating them to enable better decisions.

Whether at the product level, building level, or portfolio level, EPD data should support:

    • Material selection decisions
    • Product design improvements
    • Procurement strategies
    • Regulatory compliance

Without this connection to decision-making, EPDs remain a reporting exercise rather than a business capability.

The Core Problem: EPD Workflows Are Not Built for Scale

Across industries, the challenge is consistent. Organizations are trying to scale EPDs using processes that were never designed for volume, speed, or interoperability.

This leads to:

    • Manual, time-intensive data collection
    • Inconsistent and non-harmonized datasets
    • Duplicated effort across regions and standards
    • Limited ability to reuse or integrate data
    • Slow and costly verification processes

The result is a system that struggles to keep up with growing demand.

The Solution: From EPD Documents to Product Lifecycle Intelligence

The path forward is not just digitization. It is transformation. Instead of creating EPDs one by one, the model shifts to:

    • Ingest all available product and supply chain data
    • Structure it into a unified, digital data model
    • Create digital twins of products
    • Apply logic to generate lifecycle insights
    • Output results across multiple use cases

This approach enables:

    • Automated EPD generation
    • Substance compliance analysis
    • Lifecycle impact modeling
    • Continuous data improvement

All from the same underlying data foundation. This is what Makersite defines as Product Lifecycle Intelligence.

From Data to Decisions: Why This Matters Now

For manufacturers and construction stakeholders, this shift is critical. The market is demanding:

    • More EPDs
    • More specific EPDs
    • Faster turnaround
    • Higher data quality
    • Greater transparency

At the same time, products are becoming more complex and configurable. This creates a new requirement: the ability to generate accurate, scalable, and decision-ready environmental data.

Companies that can do this gain a significant advantage:

    • Faster compliance and reporting
    • Improved product design decisions
    • Reduced operational effort
    • Stronger, more credible sustainability claims

What the Market Is Moving Toward

The conversation is changing. Organizations are no longer asking: “Can we create EPDs?”

They are asking:

    • Can we scale EPDs across entire product portfolios?
    • Can we trust and verify the data consistently?
    • Can we integrate EPDs into digital workflows and systems?
    • Can we use EPD data to drive real decisions?

This reflects a broader shift:

    • From static documents to dynamic data
    • From manual workflows to automated systems
    • From reporting outputs to decision intelligence

Final Thought

The biggest takeaway from the session: EPDs are evolving from documents into infrastructure. Digital EPDs, standardized data models, and automated workflows are not just improving reporting. They are enabling a new foundation for environmental decision-making.

By moving toward connected, digital, and scalable data systems, organizations can turn EPDs from a compliance requirement into a strategic capability.

Want to Scale EPDs Without Scaling Manual Effort?

If your team is working to:

    • Automate EPD generation
    • Improve data quality and consistency
    • Reduce verification bottlenecks
    • Connect EPDs to product and design decisions

See how Makersite enables digital EPD workflows, lifecycle intelligence, and scalable sustainability insights across your product portfolio.

Download Makersite’s EPD ebook 

Electronics On-Demand: Turning Component Data into Sustainable Product Decisions

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Key Takeaways

Pressure is mounting from every direction. Customers demand product carbon footprints and configuration-level reporting. Regulators are raising expectations for material transparency and compliance documentation. Engineering teams must move faster. Procurement needs better supplier visibility and alternatives. Sustainability teams are expected to deliver precise answers that legacy product data environments cannot support. 

That is why we hosted the Electronics On-Demand Masterclass with SiliconExpert. The session showcased a practical shift across the electronics industry. Manufacturers must move beyond fragmented BOMs, generic assumptions, and manual supplier outreach by connecting component-level intelligence to lifecycle modeling. This enables faster, more accurate sustainability, and compliance decisions. 

At the center of this shift: SiliconExpert supplies authoritative component intelligence and materials and compliance data. Makersite transforms that data into lifecycle insights and product-level decisions. Put simply, SiliconExpert supplies the ingredients, and Makersite is the chef that turns them into five‑star sustainability outcomes.

1. Component-level data is the missing link in electronics sustainability

Ambition is not the barrier. Usable data is. Most manufacturers only have MPNs on their BOMs. What is missing is validated material composition, environmental impact metrics, and compliance status needed for PCFs, LCAs, and eco design. Without component-level intelligence, sustainability work relies on estimates instead of defensible insights.

2. PCFs require part-specific modeling, not generic assumptions

Demand for PCFs across data center infrastructure and enterprise electronics exposes the limits of average weights and generic datasets. These approaches fail to support: 

    • Supplier-level comparisons 
    • Component-level optimization 
    • Configuration-specific reporting 
    • Defensible customer disclosures 

Manufacturers need part-specific, BOM-level modeling to be credible and actionable.

3. Compliance and sustainability converge into a single workflow

RoHS, REACH, and other compliance data are increasingly the same inputs required for sustainability analysis. Leading companies combine compliance and sustainability into one workflow. Component intelligence feeds both regulatory reporting and environmental decision-making.

In this collaboration SiliconExpert delivers compliance data, IPC material declarations, and component specifications. Makersite automates lifecycle modeling and sustainability insights from that data

4. Eco design becomes actionable when data enters engineering early

A powerful outcome from the session was the ability to compare qualified component alternatives by environmental impact. Example results included: 

    • Approximately 10 percent reduction in carbon footprint from a single part swap
    • Additional reductions in water use 

This is the difference between reporting an impact and reducing it. When sustainability data is available inside engineering and sourcing workflows, it becomes a lever for product improvement.

5. Scale is unlocked by reducing manual supplier data collection

Manual FMD requests, spreadsheet harmonization, validation, and model building does not scale across thousands of components. Makersite and SiliconExpert change the equation: 

    • 75 to 85 percent or more of electronic components already have data coverage 
    • Supplier data collection effort can be reduced by up to approximately 90 percent 

This turns sustainability from a resource-heavy project into a scalable capability across product portfolios. 

The Core Problem: Lacking Usable Component Data

The industry faces consistent challenges: 

    • BOMs listing MPNs but lacking material composition 
    • Fragmented compliance datasets 
    • Manual supplier workflows 
    • Disconnected sustainability modeling 

These gaps make it difficult to answer critical questions: What is the PCF for a product or configuration? Which component alternative lowers impact? Where are high-impact materials or compliance risks? How do we scale across thousands of parts? 

The issue is not the absence of data. It is the absence of connected usable component-level data. 

The Solution: Connect Component Intelligence to Product Decisions

The Electronics On-Demand approach turns disconnected component data into usable sustainability and compliance insights. It starts with the BOM and MPNs, enriches parts with material and compliance context, and translates that into lifecycle insights for full product impact assessment. Outcomes include: 

    • Product carbon footprints at BOM and configuration levels 
    • Lifecycle impact insights across multiple categories 
    • Compliance and material risk visibility 
    • Supplier and alternative comparisons 

Instead of manual collection and modeling for thousands of parts, teams can adopt a scalable flow where sustainability and compliance insights are generated alongside product decisions. 

This is a shift from data collection to decision intelligence. 

From Data to Decisions: Why This Matters

For data center suppliers and electronics manufacturers, this is a business capability, not a side project. Customers expect: 

    • Product carbon footprints 
    • Configuration-level reporting 
    • Material transparency 
    • Fast, defensible responses to sustainability questionnaires 

Products are becoming more configurable and more dependent on complex supply chains. This creates a requirement to generate accurate, component-level sustainability insights at speed. Companies that can do this gain a clear advantage: 

    • Faster customer response times 
    • Stronger, credible sustainability claims 
    • Better product design decisions 
    • Lower operational effort 

The Market is Moving

Electronics Manufacturers have moved beyond asking, “Can we do LCA for electronics?” 

They are now asking: 

    • Can we scale across thousands of components? 
    • Can we trust the data? 
    • Can we use it in real decisions, not just reports? 
    • Can we embed it into PLM and engineering workflows? 

The market is shifting from data collection to data confidence to decision intelligence. 

Real World Examples

ThinkPad can now generate more precise, traceable, and defensible PCFs that look beyond model-level estimates

Lenovo used Makersite to deliver configuration‑level, ISO‑aligned PCFs across enterprise products. By structuring millions of supplier FMDs and adopting component‑level modeling, Lenovo replaced portfolio averages with auditable, configuration‑specific footprints—speeding reporting, strengthening sustainability claims, and improving bid competitiveness.

See how Makersite helped Lenovo >

Microsoft reduce the carbon footprint of Surface Pro 10 by up to 28%

Makersite partnered with Microsoft to operationalize a repeatable, auditable LCA methodology that scales supplier‑validated LCAs across product lines. By ingesting supplier FMDs and integrating BOM‑level modeling into engineering workflows, Microsoft moved from generic portfolio estimates to traceable, configuration‑ready lifecycle insights—raising supplier data coverage from ~20% to ~70%.

Learn how Makersite is used for ecodesign >

 

Final Thought

Sustainability in electronics starts at the component level. Value is unlocked when that component data drives decisions. By combining SiliconExpert component intelligence with Makersite’s AI-driven lifecycle modeling, manufacturers can move from fragmented data to scalable, decision-ready sustainability insights.

Curious to Learn More?

Book a Demo >

9 AI-Powered PLM Software Solutions for Manufacturers in 2026

What is AI Powered PLM?

AI-powered PLM refers to Product Lifecycle Management systems enhanced with artificial intelligence to improve how manufacturers manage, analyze and act on product data across the lifecycle. Traditional PLM systems are systems of record. They store CAD files, manage engineering change orders, track part structures and maintain BOM integrity. AI-powered PLM systems go further. They transform structured product data into decision intelligence.

In practice, AI in PLM can mean:

  • Automatically classifying and cleansing part data
  • Predicting the impact of engineering changes
  • Optimizing simulation models
  • Mapping multi-tier suppliers
  • Filling gaps in material or process data
  • Enriching BOMs with cost, risk, carbon or compliance signals
  • Enabling real time trade off analysis across engineering and procurement

For enterprise manufacturers managing thousands of components across global supply chains, AI-powered PLM becomes less about automation and more about infrastructure. It connects engineering, procurement, compliance and sustainability inside the digital thread.

However, not all AI in PLM is equal.

Some vendors embed AI directly into engineering workflows. Some apply AI primarily to simulation and digital twins. Some use AI to harmonize enterprise data across ERP and PLM. Others focus on sustainability intelligence and supplier risk modeling. For global enterprise manufacturers above operating complex, configurable BOMs, the critical question is not whether AI exists inside the platform.

The critical question is: Does the AI operate at BOM level and influence real product decisions across engineering, sourcing and compliance?

Below are nine AI-powered PLM software solutions shaping enterprise manufacturing in 2026.

1. Makersite

Makersite is a granular, AI-powered Product Lifecycle Intelligence platform purpose built for complex manufacturing sectors, with a strong presence in electronics, automotive, industrial machinery, construction, chemicals and industrial goods.

Makersite tackles the core issue of enterprise PLM environments: structured product data exists, but cross functional intelligence does not. BOMs sit in PLM. Supplier data sits in ERP. Environmental data lives in separate tools. Critical decisions are made without a unified intelligence layer. Rather than replacing PLM systems, Makersite connects to them and enriches structured product data using deeply specialized AI.

How AI is used:

  • Context rich gap filling: Dedicated industry trained AI agents infer missing supplier, material and process data by analyzing BOM structure, manufacturing context and sourcing patterns across multi tier supply chains.
  • Automated background database matching: AI automatically maps BOM inputs to environmental datasets, risk databases and compliance indicators, reducing manual mapping effort dramatically.
  • What if scenario modeling: AI enables real time trade off analysis across carbon, cost, supplier risk and regulatory exposure at configuration level.
  • Multi tier supplier mapping: AI reconciles inconsistent supplier naming and identifies relationships across complex global networks.

Differentiator:

Makersite’s differentiator is its combination of a large structured manufacturing data foundation with highly specialized AI agents trained on industrial context. Its AI understands manufacturing logic, making it highly accurate for complex, configurable BOMs. Best for enterprise manufacturers managing complex BOMs who need accurate environmental, cost and compliance modeling integrated into engineering workflows.

2. Siemens Teamcenter with AI Capabilities

Siemens Teamcenter is a leading enterprise PLM system with embedded AI-focused on engineering optimization and digital twin enablement. Teamcenter addresses the need for structured product data governance at global scale. Its AI capabilities enhance internal engineering processes rather than external supplier intelligence.

How AI is used:

  • Intelligent part classification to reduce manual categorization
  • Change management automation through predictive impact analysis
  • Digital twin optimization using simulation driven AI
  • Knowledge reuse across engineering programs

Differentiator:

Teamcenter’s differentiator is the depth of AI embedded directly inside core engineering workflows and digital twin environments. The AI operates within the system of record rather than as an external layer. Best for large global manufacturers with mature PLM environments focused on engineering performance and simulation optimization.

3. PTC Windchill

PTC Windchill combines PLM with IoT data through its broader ecosystem, using AI to enhance lifecycle visibility and configuration management. Windchill addresses the need to connect product data with real world performance signals.

How AI is used:

  • Predictive analytics on product performance
  • Configuration optimization across variants
  • Closed loop lifecycle insights from connected product data
  • Automated impact analysis across engineering changes

Differentiator:

Windchill’s differentiator is its integration of PLM with IoT and service data, allowing AI to inform decisions using real world performance feedback. Best for industrial machinery and heavy equipment manufacturers managing connected assets and configurable products.

4. Dassault Systèmes 3DEXPERIENCE

Dassault’s 3DEXPERIENCE platform embeds AI primarily within simulation and advanced modeling workflows. The platform addresses the need for design optimization and performance simulation in highly engineered environments.

How AI is used:

  • Simulation driven optimization of materials and structures
  • Predictive modeling of performance scenarios
  • AI-assisted design exploration
  • Digital twin refinement

Differentiator:

Dassault’s differentiator lies in simulation depth. AI enhances computational modeling rather than multi tier supplier intelligence. Best for aerospace and automotive manufacturers with heavy reliance on simulation and advanced materials engineering.

5. SAP PLM with AI

SAP integrates PLM functionality into its ERP backbone, using AI for data harmonization and predictive enterprise analytics. SAP addresses enterprise wide data consistency and financial integration.

How AI is used:

  • Master data harmonization across systems
  • Predictive supply chain insights
  • Demand forecasting and risk identification
  • Intelligent workflow automation

Differentiator:

SAP’s differentiator is enterprise integration. AI connects lifecycle data with financial and procurement systems at scale. Best for global enterprises prioritizing unified ERP and lifecycle data governance.

6. Aras Innovator

Aras Innovator is a flexible PLM platform that supports AI extensions through configurable architecture. Aras addresses manufacturers that require adaptable lifecycle workflows across diverse product portfolios.

How AI is used:

  • Custom analytics and reporting extensions
  • AI powered document search and knowledge retrieval
  • Configurable workflow automation

Differentiator:

Aras differentiates through architectural flexibility. AI capabilities are shaped by implementation rather than delivered as fixed modules. Best for manufacturers seeking customizable PLM infrastructure with tailored AI workflows.

7. Oracle Agile PLM

Oracle Agile remains strong in compliance driven PLM environments, particularly in electronics and high tech sectors. Agile addresses structured documentation, regulatory management and controlled product record environments.

How AI is used:

  • Automated classification and search
  • Compliance analytics through Oracle Cloud services
  • Risk monitoring across supplier documentation

Differentiator:

Oracle Agile differentiates through compliance centric PLM strength, with AI augmenting documentation and regulatory tracking. Best for electronics manufacturers managing strict compliance and documentation requirements.

8. Propel PLM

Propel is a cloud native PLM built on Salesforce infrastructure, targeting modern manufacturing companies. Propel addresses collaboration and lifecycle visibility in cloud first environments.

How AI is used:

  • CRM integrated product insights
  • Workflow automation through Salesforce AI
  • Analytics across customer and product lifecycle data

Differentiator:

Propel differentiates through tight CRM and PLM integration, bringing AI insights across customer and product domains. Best for growth oriented manufacturers aligning product management with customer intelligence.

9. Sustainability Platforms Adjacent to PLM

Platforms such as Sphera focus on compliance databases and environmental risk monitoring that operate adjacent to PLM systems. These platforms address regulatory intelligence rather than engineering integrated intelligence.

How AI is used:

  • Automated regulatory tracking
  • Risk signal monitoring
  • Data normalization for reporting

Differentiator:

These platforms differentiate through regulatory database breadth and compliance depth rather than embedded product level intelligence. Best compliance focused sustainability programs that operate parallel to engineering workflows.

 

When evaluating AI-powered PLM Software Solutions, enterprise manufacturers should ask these questions

1. Is the platform a system of record or an intelligence layer?

Some platforms replace or serve as core PLM systems. Others operate as AI intelligence layers that integrate with existing PLM and ERP environments.

If your organization already runs Siemens Teamcenter, PTC Windchill or Dassault, replacing PLM may not be realistic. In that case, an AI enrichment layer may be more strategic.

Clarify whether you are modernizing infrastructure or augmenting it.

2. Does AI operate at BOM level depth?

High level dashboards are not enough for complex manufacturing.

Ask:

• Can the platform ingest multi level BOMs?
• Can it analyze configuration variants?
• Does AI enrich individual line items?
• Can it model trade offs at component level?

For manufacturers managing thousands of components per product, BOM level intelligence is critical.

This shifts sustainability from retrospective reporting to proactive design decision support.

3. How does the platform handle missing supplier or material data?

Incomplete data is the norm, not the exception.

Evaluate:

• Does the system rely solely on declared supplier data?
• Does it use context aware AI to infer missing attributes?
• Are modeling assumptions transparent and traceable?
• Can estimated values be replaced with primary data later?

The ability to manage uncertainty intelligently often determines scalability.

4. How well does it integrate with existing enterprise systems?

AI-powered PLM should not create new silos.

Assess:

• API depth with PLM and ERP systems
• Compatibility with supplier portals
• Ability to export structured outputs for reporting
• Security and data governance controls

Enterprise adoption depends on seamless integration into current workflows.

5. Does it support cross functional decision making?

PLM historically served engineering.

Modern AI-powered PLM must also serve:

• Procurement teams evaluating supplier risk
• Sustainability teams modeling Scope 3 impact
• Compliance teams tracking regulatory exposure
• Finance teams analyzing cost exposure

Ask whether the platform enables concurrent evaluation of carbon, cost and compliance trade offs.

6. Can it scale across global, multi-tier supply chains?

Enterprise manufacturers operate across regions, currencies and regulatory regimes.

Evaluate:

• Multi tier supplier mapping capabilities
• Localization for regulatory frameworks
• Ability to support digital product passport requirements
• Performance at enterprise data volumes

Scalability is not just about user count. It is about data complexity.

7. Does it influence decisions before design freeze?

Many tools accelerate reporting. Fewer influence product design.

The most strategic AI-powered PLM solutions:

• Integrate directly into early design workflows
• Enable what if scenario modeling
• Provide insights during sourcing decisions
• Support engineering trade off analysis in real time

If intelligence only appears after the product is finalized, the strategic value is limited.

Final Thought: The Future of PLM Is Decision Intelligence

PLM modernization is no longer a technology upgrade.

It is a strategic shift in how manufacturers make product decisions.

As supply chains become more complex and regulatory expectations intensify, intelligence cannot remain siloed in reporting tools or disconnected systems. AI-powered PLM must operate inside the digital thread, linking engineering structure with supplier visibility, cost dynamics and sustainability impact in real time.

The competitive advantage will not come from managing more product data.

It will come from transforming product data into actionable intelligence at the exact moment decisions are made.

Still Have Questions? Let’s Dig Deeper

What makes PLM software “AI-powered” versus traditional PLM systems?

Traditional PLM systems act as systems of record. They manage CAD files, BOM structures, engineering change orders and product documentation. Intelligence typically comes from human analysis layered on top of structured data.

AI-powered PLM introduces machine learning, semantic mapping and predictive modeling directly into the lifecycle workflow. Instead of simply storing product data, AI enriched systems classify components automatically, infer missing attributes, predict the impact of engineering changes, map suppliers across inconsistent naming structures and generate scenario based insights in real time.

The key difference is that AI-powered PLM transforms product data into decision intelligence rather than static documentation.

How does AI in PLM handle incomplete or inconsistent BOM data?

Incomplete BOM data is one of the biggest constraints in enterprise manufacturing. Supplier declarations may be missing. Material compositions may be partially defined. Multi tier sourcing data is rarely transparent.

AI-powered PLM platforms address this through context aware modeling. Instead of relying solely on declared attributes, AI analyzes the component’s category, application, manufacturing context and known supplier patterns to infer likely material compositions or process assumptions.

More advanced platforms also reconcile duplicate supplier records, normalize inconsistent naming conventions and map parts to standardized datasets automatically. This reduces manual cleansing and accelerates time to insight without compromising engineering governance.

Can AI powered PLM replace sustainability or compliance tools?

In most enterprise architectures, AI-powered PLM does not replace sustainability or compliance platforms. It complements them.

PLM remains the system of record for structured product data. Sustainability tools manage regulatory reporting frameworks. Compliance systems track substance declarations and documentation.

AI-powered PLM acts as a connective layer. It enriches product data with environmental, cost and risk intelligence before reporting begins. Instead of exporting static BOMs to downstream tools, manufacturers can integrate intelligence upstream in the product development lifecycle.

This shifts sustainability from retrospective reporting to proactive design decision support.

How accurate are AI generated environmental or supplier estimates?

Accuracy depends heavily on the platform’s underlying data foundation and modeling methodology.

Some tools rely primarily on spend based emissions or generalized industry averages. Others use contextual AI trained on manufacturing datasets to infer missing attributes more precisely.

For exploratory portfolio level analysis, estimated modeling may be sufficient. For regulatory reporting, digital product passports or configuration level carbon footprints, manufacturers typically require platforms grounded in verified engineering logic and structured lifecycle datasets.

AI should enhance data quality, not obscure it.

When should AI-powered PLM be used in the product development lifecycle?

Historically, lifecycle analysis and risk assessments were conducted after product design was largely finalized. This limited the ability to influence outcomes.

AI-powered PLM shifts intelligence earlier into R and D and sourcing workflows. Because AI can instantly evaluate alternative materials, suppliers or configurations, engineering and procurement teams can compare carbon, cost and compliance trade offs before tooling or production begins.

The greatest value of AI in PLM is realized when intelligence informs decisions before design freeze, not after product launch.

Is AI-powered PLM relevant for companies with mature PLM systems?

Yes. In fact, mature PLM environments benefit the most.

Enterprise manufacturers using systems such as Teamcenter, Windchill or 3DEXPERIENCE already have structured product data. What is often missing is cross functional intelligence layered across cost, supplier risk and sustainability dimensions.

AI-powered PLM does not replace core engineering systems. It supplements them by enriching structured data and connecting it to broader enterprise objectives.

For organizations with global supply chains, AI becomes an infrastructure enhancement rather than a system replacement.

From Manual LCAs to Cloud-Scale Measurement: Microsoft’s CHEM Methodology

The challenge: You can’t decarbonize what you can’t measure

For hyperscalers and data center operators, embodied carbon in ICT hardware represents a major share of Scope 3 emissions. In a recent whitepaper, Microsoft notes that reducing this impact requires reliable and granular measurement across a rapidly evolving hardware landscape and a deeply layered global supply chain.

While life cycle assessment (LCA) is a well established methodology for quantifying environmental impacts, Microsoft states that traditional approaches are difficult to apply consistently at cloud scale. Manual steps such as reconstructing complex BOMs and mapping materials to life cycle inventory datasets can take more than 100 hours per server, which makes it difficult to scale process-based LCA across thousands of hardware configurations without significant effort.

The shift: From manual modeling to scalable measurement

To overcome these limitations, Microsoft developed the Cloud Hardware Emissions Methodology, or CHEM. CHEM is an LCA based methodology designed to automate and scale embodied carbon measurement across Azure hardware, while preserving the level of detail needed to identify emissions hotspots and evaluate decarbonization interventions.

How CHEM is built

CHEM was developed using Azure data services alongside cloud based automated LCA software, including Makersite, which Microsoft uses to implement and scale process based  LCA models across complex hardware configurations. This is combined with proxy mapping tooling and state of the art semiconductor life cycle inventory data from the imec Sustainable Semiconductor Technologies and Systems program.

Integrating product data
To reduce manual effort and improve consistency, CHEM integrates directly with Microsoft’s internal product data management systems and full material declarations. This allows complex BOMs hierarches to be transferred automatically into the LCA modeling environment, helping assessments stay aligned as hardware designs evolve.

Automating material to inventory mapping
CHEM automates the mapping of material compositions to representative life cycle inventory datasets from third party sources such as ecoinvent. By reducing manual modeling work, this approach allows practitioners to focus on data quality, supplier specific inputs, and interpretation rather than data entry.

Modeling semiconductors at higher resolution
Microsoft identifies semiconductor components as the primary drivers of embodied carbon in datacenter hardware. To improve accuracy, CHEM incorporates detailed manufacturing data from the imec Sustainable Semiconductor Technologies and Systems program.

Microsoft integrates this data into custom LCA models and uses its automated LCA software environment, including Makersite, to run and scale those models across large numbers of hardware configurations.

Why this matters

By applying CHEM across its cloud hardware fleet, Microsoft describes several practical outcomes:

  • More robust Scope 3 reporting
    Process based data replaces high level financial proxies, supporting disclosures that are more consistent, auditable, and repeatable at scale.
  • Clearer supply chain hotspot identification 
    Granular modeling makes it possible to trace embodied carbon impacts multiple tiers deep and evaluate where targeted interventions could have the greatest effect.
  • Carbon informed hardware design
    CHEM data can be used by system architects to consider embodied carbon alongside power, performance, and cost during hardware design decisions.
  • More precise carbon roadmapping
    Aggregated results across parts, assemblies, and configurations support carbon reduction roadmaps that reflect real manufacturing processes rather than estimates.

A signal for the industry

Microsoft presents CHEM as part of a broader shift toward more scalable, data driven approaches to understanding and reducing the embodied carbon impact of cloud hardware. Th company also highlights ongoing collaboration with industry groups such as the Open Compute Project and the Semiconductor Climate consortium to help improve consistency and standardization in LCA based carbon accounting.

Together, these efforts point toward a future where embodied carbon data is not just reported but operationalized. For organizations managing complex hardware fleets, the CHEM approach illustrates what is required to move from high level estimates towards measurement that can support real supply chain, design, and roadmapping decisions.

This blog is an interpretive summary of Microsoft’s whitepaper ‘How Microsoft is advancing embodied carbon measurement at scale for Azure hardware’, published in 2026. 

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Still Have Questions? Let’s Dig Deeper

How does Microsoft measure embodied carbon for Azure hardware?

Microsoft measures embodied carbon for Azure hardware using the Cloud Hardware Emissions Methodology (CHEM), a process based life cycle assessment methodology. CHEM integrates internal product and supply chain data with environmental lifecycle inventory data to quantify emissions across the full hardware lifecycle.

What is the difference between spend-based and process-based LCA for data centers?

Spend-based methods estimate emissions using financial proxies, which can obscure the true drivers of embodied carbon. Process-based LCA, as used in CHEM, models emissions based on physical manufacturing processes and material flows, enabling more granular and actionable insights into where emissions originate.

How does Microsoft handle the complexity of semiconductor emissions?

Recognizing that semiconductors are a major contributor to embodied carbon, Microsoft incorporates detailed semiconductor life cycle inventory data into CHEM.This includes the use of advanced “virtual fab” models developed with data from the imec Sustainable Semiconductor Technologies and Systems program to represent specific manufacturing process steps rather than generic averages.

Can Life Cycle Assessment (LCA) be automated for hyperscale hardware?

Microsoft’s CHEM methodology demonstrates that significant parts of process-based LCA can be automated when product data systems are connected to cloud-based LCA modeling tools. This reduces the manual effort required to reconstruct BOMs and map materials to life cycle inventory datasets at hyperscale.

What role does Makersite play in the CHEM methodology

Microsoft uses Makersite as part of the CHEM implementation to support automated LCA modeling across complex hardware configurations. Makersite is used to map product structures and materials to environmental datasets, enabling scalable, process-based emission modeling.

Greener and Cheaper: Five Key Insights from Makersite’s Approach to Better Products

At Makersite, our mission is simple: help companies design and manufacture products that are more affordable, safer, and more sustainable. With the world’s largest supply chain database and over 10 million detailed lifecycle assessments completed, we’ve proven that sustainability and profitability are not mutually exclusive. Businesses can reduce environmental impact, cut costs, and manage risks at the same time.

Neil D’Souza was recently interviewed by Stefanie Hauer at podcast – Planetary Business.Here are five key insights from the interview, that define how we work — and how our platform is helping global manufacturers thrive in an era of geopolitical complexity, evolving regulations, and resource constraints.

You can listen to the podcast below.

1. Green Can Be Cheaper — and More Profitable

Our experience shows that it is possible to improve the environmental performance of any product without increasing its price. The key lies in understanding its complete supply chain. Once you know where and how materials are sourced, and how products are made, you can make design changes — such as sourcing from lower-impact suppliers or selecting better materials — that lower lifecycle costs while improving sustainability.

2. Transparency Drives Better Decisions

Historically, environmental data rarely reached engineers, limiting its impact. Makersite changes that by providing granular component-level data on cost, environmental impact, compliance, and supply chain risk. This transparency empowers teams to act on the biggest levers for improvement instead of small, symbolic changes. For example, understanding which steel grades have significantly lower carbon emissions can reshape product design choices.

3. Regulations Like the Digital Product Passport Unlock Opportunity

The EU’s Digital Product Passport represents a major step toward enabling product circularity. Much like nutritional labels for food, it standardises information on product composition, environmental footprint, and disposal instructions. This transparency helps companies reclaim and reuse materials already in circulation — a strategy that reduces dependency on imported raw materials and strengthens long-term resilience.

4. Avoiding the Survival Mode Trap

40% of supply chains have already changed in the past 18 months due to global volatility. Many manufacturers have responded by cutting investment to preserve cash. In our view, this can delay essential innovations and prolong vulnerability. Using the right tools and data to adapt supply chains now — rather than waiting for a “steady state” — is critical to building long-term strength and sustainability.

5. A Clear Vision Enables Change

Change requires a clear picture of a desired future. We have observed a steady increase in companies genuinely committed to doing better. No engineer wants to design a harmful product; they simply need the information to make better decisions. Providing accurate, actionable data on cost, impact, and compliance enables rapid product improvements and accelerates positive change.


Conclusion

At Makersite, we focus on improving products at their source — in design and manufacturing — rather than relying solely on reporting or compliance frameworks. By combining powerful data, AI-driven insights, and a clear product-centric view, we help companies win on margin, risk, and impact simultaneously.

Our ambition is bold: within the next decade, put one billion products on the market that are the most sustainable they can be — without compromising profitability.

If you would like to explore how Makersite can help your organisation make better products, contact us to arrange a demonstration of our platform.

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From Data to Impact: Scaling Sustainability Across Manufacturing Enterprises

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The Hard Truth: Reporting Doesn’t Change Anything

Most manufacturers are drowning in spreadsheets and annual reports, but emissions don’t drop from reporting alone. The real bottleneck isn’t ambition. It’s data that’s too coarse to change real decisions. In our most recent webinar, partnered with NAEM, “From Data to Impact: Scaling Sustainability Across Manufacturing Enterprises” our experts stressed a simple reality: spend-based estimates might check a compliance box, but they will never redesign a product, reshape a supply chain, or win you a tender.

Key Takeaways

  • Activity-based data is essential. Our experts emphasized that bill of materials (BOM)-level data enables manufacturers to move beyond spend-based estimates and into actionable Scope 3.1 reporting.
  • A laddered approach to data quality. Start where you are: BOM-based modeling when available, weight or average factors when necessary, and spend-based methods only as a last resort.
  • Collaboration is non-negotiable. Procurement, engineering, and sustainability functions must be aligned if product sustainability data is to influence real business decisions.
  • Digital twins enable scale. By linking product, supply chain, and impact data, organizations can automate LCAs, close supplier data gaps, and create portfolio-wide transparency.

The Data Ladder: How to Climb Out of Spend-Based Guesswork

Most organizations are not constrained by a lack of data but by the wrong type of data. Scope 3.1 requires product-specific, activity-based data to enable meaningful action. Without it, sustainability reporting risks becoming an exercise in compliance rather than a driver of competitive advantage. Our experts underscored that to create impact, sustainability insights must flow into design and sourcing decisions, not remain trapped in reporting cycles.

A Practical Data Ladder for Scaling Sustainability Data

  1. BOM-based (preferred): Map the bill of materials, normalize material and process categories, and apply life cycle inventory factors. This is the most actionable level for design and sourcing decisions.
  2. Average/mass-based (backup): When full BOM data is unavailable, use product weight and representative averages to approximate impacts.
  3. Spend-based (fallback): Leverage spend data multiplied by EEIO factors only when no other information exists. This approach should be replaced progressively with activity-based data.

This ladder allows organizations to begin modeling with the data at hand and gradually refine accuracy through supplier engagement and primary data collection.

The point? Start now, climb steadily. Don’t let “perfect data” be the excuse for doing nothing.

The Barriers Manufacturers Face

Scaling sustainability isn’t straightforward. Common challenges include:

  • Data silos across PLM, ERP, and compliance systems.
  • Incomplete product records, such as missing weights or coatings.
  • Rapid change in engineering and sourcing, which often outpaces traditional LCA cycles.
  • Competing priorities across teams, with procurement focused on cost, engineering on manufacturability, and sustainability on reporting deadlines.

Breaking the Silos: From Sustainability Reports to Product Decisions

ERP and PLM systems were built to optimize cost and risk—not sustainability. The result? Fragmented records, incomplete supplier info, and teams working in isolation. The solution made clear: scaling sustainability demands a single source of product truth that procurement, engineers, and sustainability teams can all access and act on.

To overcome these barriers, our experts recommend a repeatable operating model:

  1. Normalizing product records by harmonizing BOMs, applying default assumptions, and defining a single source of truth for material attributes.
  2. Building an LCA-at-scale service via digital twins that connect cost, compliance, and footprint data, keeping models current as designs change.
  3. Prioritizing supplier engagement based on material impact, focusing requests for primary data where it matters most.

Embedding sustainability into workflows so that footprints are considered alongside cost and lead time in procurement events, design reviews, and customer disclosures.

Data Quality Isn’t the Excuse

Yes, your data is messy. Everyone’s data is messy. But the myth that you must “fix data first” before scaling sustainability is paralyzing progress. As our experts highlighted, you can achieve more than you think—even with imperfect data. The maturity curve proves it: novices wrestle spreadsheets, intermediates integrate flows, and advanced players run centralized master data governance. The winners don’t wait—they build maturity as they go.

What Success Looks Like

Signals that sustainability has scaled include:

  • Broad coverage of up to 80% revenue/SKUs using activity-based methods.
  • Automated impact updates tied to BOM or supplier changes.
  • Sustainability metrics integrated into sourcing and design decision gates.
  • Clear supplier mix shifts toward lower-carbon options, informed by quantified trade-offs.

Real-World Win: Microsoft – 28% Footprint Reduction on Surface Pro

Microsoft’s Surface team discovered that manual LCA processes were outdated, slow, and riddled with generic data.

By automating through Makersite, they:

  • Cut LCA effort from months to minutes.
  • Increased accuracy from 20% primary data to 70%.
  • Freed 80% of resources to focus on reductions instead of reporting.
  • Achieved a 28% footprint reduction on the Surface Pro.

The lesson: Automation doesn’t just accelerate reporting—it creates the space to design real carbon reductions.

Read the Full Case Study

Real-World Win: FLS – Tackling Massive Complexity

FLS, a global mining equipment leader, faced customer demand for timely LCA data that far outpaced their manual capacity. Their products involve thousands of BOM lines and hundreds of tons of material.

With Makersite, they:

  • Scaled LCAs across complex, customized portfolios.
  • Embedded carbon transparency into tenders and sales.
  • Gained actionable insights to drive supplier and material decisions.

FLS turned sustainability into a competitive edge—not a reporting chore.

Read the Full Case Study

So, What Should You Do?

  • Run a pilot, not a POC. Prove scale, accuracy, and speed on real SKUs or sites—not toy examples.
  • Get cross-functional buy-in. Procurement, sales, and engineering must see the business value, or sustainability stays underfunded.
  • Pick a partner you trust. The sustainability software space is still the Wild West. Don’t just buy tools—find people who deliver.

Addressing Common Pushbacks

  • “We don’t have the data.” Use the ladder—begin with what is available and improve over time. Hybrid approaches are both recognized and effective.
  • “LCAs take too long.” With a connected digital twin, models update automatically, reducing time to insight.
  • “Scope 3 is just reporting.” When tied to product and sourcing decisions, Scope 3 becomes a lever for both emissions reduction and margin growth.

Still Skeptical? Let’s Address the Hard Questions

  • “We already have a sustainability team handling this.”
    Good—but if their insights never reach procurement or design, you’re leaving value on the table. Scaling means wiring their work directly into product and supplier decisions, not confining it to reports.
  • “We’re not ready for a new tool or vendor.”
    You don’t need another silo—you need a connected digital twin that feeds your existing PLM, ERP, and sourcing systems. The right partner integrates with what you have and accelerates ROI.
  • “We don’t have good enough data to act.”
    No one starts with perfect data. The key is the ladder approach: use what you have, improve as you go, and replace assumptions with primary data over time.
  • “This all sounds too complex.”
    Automating LCAs across thousands of BOM lines for heavy mining equipment is complex, but it can be done. Complexity is exactly why scalable automation exists.
  • “Scope 3 is just reporting.”
    Reporting alone doesn’t change outcomes. But when Scope 3 metrics drive sourcing, design, and tender decisions, they become a lever for cost savings, margin growth, and differentiation.

Closing Thought

Our experts’ message was clear: sustainability at scale isn’t about “more reports.” It’s about hardwiring footprint into product and supplier decisions. That’s the difference between reporting carbon and actually reducing it.

On-Demand Decarbonize by Design: How Product Sustainability Fuels Business Growth

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Masterclass Key Takeaways

Despite headlines hinting at a “sustainability slowdown,” the data tells a very different, but encouraging story. In our recent masterclass with PwC’s David Linich and Makersite CEO Neil D’Souza, we dug into the real business levers behind climate action and decarbonization. Spoiler alert: they’re not just about ESG scores. They’re about growth, resilience, and bottom-line performance.

Sustainability isn’t dead. It’s maturing

PwC’s latest State of Decarbonization study found that more companies are increasing their climate ambitions than pulling back—37% of companies are stepping up their goals, compared to just 16% dialing them down. Many of those lowering targets are simply recalibrating early, overly ambitious goals to reflect more realistic roadmaps. In just five years, there’s been a 9x increase in companies setting emissions reduction targets.

 

Scope 3 is the new frontier—and the biggest opportunity

Progress on Scope 1 and 2 is real, but Scope 3 remains the largest untapped lever. Why? It’s tough to tackle, but it’s also where most value chain emissions, and business value, live. Most enterprises still rank low in supplier engagement maturity. That’s where tools like product carbon footprinting and collaborative design come in.

Product sustainability drives growth. Literally. 

Sustainable products deliver measurable ROI. PwC found that companies marketing products with sustainability attributes are seeing a 6–25% revenue uplift, through price premiums, increased purchase intent, and entirely new revenue streams like circular business models. Double claims, “durable + PFAS-free”, are particularly powerful, boosting purchase intent by up to 30%.

The business case isn’t one-and-done. It’s a drumbeat

One of the biggest blockers? Failing to make (and maintain) the business case. Leaders need to advocate for decarbonization like they would any core investment, with a repeatable story, clear ROI, and alignment to strategic priorities. Capital allocation is key. Leading companies are ring fencing budgets for decarbonization or applying internal carbon prices to support longer-term investments.  

Digital product twins are redefining what’s possible

Legacy LCA methods don’t scale, but scalable, AI-driven platforms can. With digital twins, manufacturers can simulate cost, carbon, risk, and compliance trade-offs in real-time across entire product portfolios, not just pilot SKUs. The shift: From once-a-year compliance reports to daily design decisions.

Circularity is suddenly more economical

Thanks to tariffs, raw material volatility, and shifting customer preferences, circular business models that didn’t pencil out before are now financially viable. But unlocking that value requires scenario planning and data orchestration at scale. From reuse to take-back programs, sustainability and margin of growth are finally aligning.  

Why it Matters

Product sustainability is becoming a top priority for both sales teams and engineers, and for good reason. According to PwC, by 2030, over one-third of global company revenues will come from climate-focused solutions—think lightweight products, alternative fuels, and circular business models for B2B and consumers. It’s clear: meaningful climate action and business growth now go hand in hand.

What You Can Do on Monday

If you’re not ready to overhaul your entire product sustainability strategy (yet), start here:

  • Assess your product sustainability maturity: Take stock of your cross-functional coordination, LCA capabilities, and supplier engagement efforts. Understand your current baseline and identify where to improve. 
  • Build (and sustain) the business case: Clarify how product sustainability directly supports revenue, margin, and compliance goals. Revisit it often to maintain leadership support and investment. 
  • Explore design levers for Scope 3: Pinpoint where your product and sourcing choices influence emissions and cost. Focus efforts where they’ll yield both carbon reduction and commercial value. 
  • Equip Sales & Marketing with the right tools: Provide clear, credible messaging and ROI calculators to help teams communicate sustainability claims effectively, especially in B2B contexts. 
  • Pilot scalable digital tools: Trial digital twins or rapid LCA platforms on a small product set to evaluate speed, cost, and business insight potential before scaling up. 

Take Action

Watch the full masterclass and download PwC’s State of Decarbonization report for sector insights, value chain strategies, and practical playbooks.

Need help scaling your product sustainability efforts? Makersite’s experts are ready to help.

Quantifying Circularity: A Data-Driven Approach to Chip Lifecycle Emissions

Turning Vision into Action: Advancing Circular Manufacturing

To open this masterclass, Gruber and Dillman presented a bold perspective on circular economy strategies, using a case study that compared the environmental and economic impacts of reusable and linear semiconductor chip designs. With sustainability leaders from companies like Amazon, IKEA, and Cisco in attendance, the discussion emphasized integrated, data-driven decision-making as a critical enabler for meeting today’s sustainability standards.

Contrasting scenarios included:

  • A linear model, where the chip is manufactured, used, and discarded.
  • A circular model, where the chip is recovered, re-balled, and reused.

The circular model demonstrated slightly higher emissions for the reprocessing step (2.36 kg CO₂e vs. 1.94 kg CO₂e for linear disposal), but by extending the lifetime of the initial chip in the circular model, where the linear would now be replaced by a new chip (1.94 x 2 = 3.88 kg CO₂e) the benefits of the circular approach is shown. By eliminating the need to manufacture new chips for future production cycles, the circular process reduces total, system-wide emissions while also drastically minimizing raw material extraction, water usage, and land use. 

Circular manufacturing offers a transformative solution for reducing environmental impact and building long-term economic resilience. Forward-thinking companies like Jabil are already operationalizing these principles, turning what was once considered waste into valuable resources through systematic recovery and reuse programs that can also deliver significant cost savings.

Gruber and Dillman’s data-driven example underscores how this model can cut resource consumption, support compliance with evolving sustainability regulations, and drive progress toward a fully circular economy. Businesses adopting these strategies position themselves as sustainability leaders, strengthening their operations against resource scarcity and climate challenges. By embracing circular innovation, companies unlock a powerful pathway to sustainable growth and competitive advantage.

“Circularity isn’t just about recycling—it’s about smarter design, sourcing, and evaluating trade-offs,” said Dillman. “To close the loop, we must assess impacts beyond carbon.”

Designing for Circularity: Key Insights from the Session

Key takeaways included:
  • Sustainability Requires System Thinking: Achieving a circular economy demands cross-functional collaboration across design, procurement, logistics, and recovery. A unified data foundation is critical to driving these efforts effectively.
  • Data-Driven Decisions Over Assumptions: The circular chip example scenario underscores the importance of high-fidelity modeling in evaluating circular strategies. Circular initiatives often lack granular emissions and cost data, making it difficult to assess trade-offs or justify actions internally. Digital tools that enable engineers and sustainability teams to quantify carbon impacts and material costs at the component level provide the analytical rigor needed to support data-backed circularity decisions.
  • Leadership Focuses on Actionable Insights: The strong participation of executives and senior managers in the session underscores growing C-level commitment to sustainable innovation and responsible driven business models.
  • Scalable Platforms Are the New Standard: Fragmented tools fall short in today’s complex landscape, creating new data silos and preventing transparency. Forward-thinking sustainability leaders are turning to scalable platforms and digital tools to seamlessly integrate sustainability, cost efficiency, and product compliance into their operations.

Driving Circularity with Actionable Product Intelligence

As manufacturers push toward circular economy goals, decision-makers are increasingly turning to digital tools that provide high-resolution insights across the product lifecycle. These platforms are enabling sustainability, procurement, and design teams to move beyond assumptions by modeling the environmental and economic implications of circular strategies in real time.

By bringing together lifecycle data, cost metrics, and supply chain considerations, these tools support:

  • Comparative analysis of linear vs. circular models
  • Identification of trade-offs across environmental categories
  • Alignment across teams through shared, data-driven insight.

In a rapidly shifting regulatory and market landscape, the ability to simulate design choices at scale — grounded in real-world data — is essential. Organizations that invest in this type of intelligence aren’t just improving products; they’re reshaping how sustainability is operationalized across the enterprise.

Turning Circular Strategies into Scalable Impact

For manufacturers, achieving sustainability success requires integrating data-driven insights and lifecycle thinking into design and procurement processes. This approach empowers teams to scale effective strategies such as reducing product carbon footprints, ensuring regulatory compliance, and driving operational efficiencies. With data and cross-functional alignment at the core, circularity evolves from a lofty goal to a measurable competitive advantage, positioning businesses as leaders in innovation and sustainability.

Key learnings: Navigating Material & Substance Compliance

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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.

On-Demand Masterclass: How to Evolve Beyond Spend-Based Scope 3 Reporting

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Sustainability reporting has moved from being a niche requirement to a central element of modern corporate strategy. For many organizations, the focus on Scope 3 emissions — which account for most of their product’s carbon footprint — presents an opportunity to lead in environmental responsibility and drive innovation. Yet, most companies still rely on spend-based methodologies that provide limited accuracy and fail to capture the full picture of their impact. As stakeholders demand greater transparency and regulators implement stricter compliance measures, businesses must evolve their approach to ensure they remain relevant and resilient in a competitive landscape.
 

In a recent Makersite Masterclass, our data experts Fabian Hassel, VP of Services and Pablo Downer Päster, Principal Sustainability Engineer, provide a roadmap for organizations looking to transition away from spend-based approaches.

The session emphasized the need for robust, data-driven strategies that go beyond surface-level reporting, providing organizations with the tools and strategies needed to begin the transition.

Below are 10 key takeaways you can use to optimize your sustainability practices. By implementing these strategies, businesses can not only meet compliance requirements but also unlock long-term value in their operations.
 

Top 10 Key Takeaways

Understand the Limitations of Spend-Based Reporting

Spend-based reporting has been a common practice due to its simplicity and scalability, but it is heavy with drawbacks that limit its effectiveness in meeting modern sustainability goals. We highlighted three major weaknesses of this approach:

  • Lack of Accuracy: Relying on industry averages often fails to capture the complexity and uniqueness of individual supply chains.
  • Overlooked Factors: Key variables, such as material choices, product design, supplier energy sources and production efficiencies, are ignored.
  • Regulatory Risks: Increasingly stringent regulations demand more product level granularity and transparency, making spend-based methodologies insufficient. 

To keep up with global standards, businesses must transition to data-driven methods that go beyond high-level estimates.

 

The Power of Granular Data in Scope 3 Reporting

Accurate, granular data is a necessity for sustainability reporting. Transitioning from general estimates to detailed, material-specific information allows businesses to make better decarbonization decisions and take targeted actions.

Granular data provides:

  • Material-Level Insights: A clear understanding of the impact of each material in the supply chain.
  • Product-Specific Assessments: Precise measurements of emissions tied to specific products.
  • Supplier Data Integration: A more accurate and strategic approach to managing supplier emissions.   

This actionable level of detail equips businesses to develop proactive sustainability strategies rather than merely meeting reporting requirements.


Digital Twins as a Game-Changer

Digital twins are virtual data models that replicate real-world products, processes, and supply chains. We emphasized their transformative potential for Scope 3 reporting.

Digital twins enable companies to: 

  • Simulate scenarios to evaluate sustainability interventions.  
  • Identify “hotspots” of emissions in supply chains to focus on reduction strategies.  
  • Foster greater collaboration among procurement, research, and engineering teams to align sustainability goals.  

For example, a manufacturer of complex systems like wind turbines could use digital twins to visualize emissions across thousands of components and adapt processes accordingly.


Navigate Transition Challenges 

Moving beyond spend-based reporting is rewarding, but it isn’t without challenges. As we identified the most common roadblocks and how to address them: 

  • Data Gaps: Ensuring suppliers provide accurate and comprehensive data.
  • Integration Barriers: Streamlining fragmented data systems into a unified platform.
  • Cost and Complexity: Investing in advanced tools and frameworks for long-term gains. 

Despite these obstacles, high-impact organizations have successfully overcome these barriers with meticulous planning and the right tools.


Industry-Specific Insights for Scope 3 Reporting

Different industries face unique challenges and opportunities in Scope 3 reporting.

Here are some examples discussed in the masterclass: 

  • Automotive: High supply chain complexity coupled with strict emissions regulations.
  • Electronics: Significant impacts from raw materials requiring circular practices.
  • Heavy Machinery: Long product life cycles and complex components necessitate precise data collection.  

Tailoring reporting strategies to industry-specific needs is essential for achieving both accuracy and actionable insights.

 

Preparing for a Shifting Regulatory Landscape

Regulations like the EU Corporate Sustainability Reporting Directive (CSRD) and SEC climate disclosure requirements demand unprecedented levels of transparency. We emphasize that companies need to: 

  • Build traceable and robust supply chain mechanisms.  
  • Adopt methodologies that exceed regulatory expectations to ensure long-term compliance and readiness for future standards. 

Organizations that begin adapting now will gain a head start over competitors once these regulations are fully enforced.

 

Advanced Master Material’s Approach to Scope 3 Reporting

To achieve your Scope 3 reporting goals, the importance of integrating an AI or tech tool to simplify and assist in the transition to more accurate Scope 3 reporting.

What you should look for in a tech tool to help you achieve your goals in Scope 3 Reporting:

  • Automated Data Integration: Seamless integration with ERP and PLM systems consolidates disparate data sources.  
  • Material and Supplier-Specific Modeling: Detailed emissions data to guide informed decision-making.
  • Collaboration Tools: Enables real-time engagement between cross-functional teams, such as procurement and sustainability managers.

By addressing key challenges of data granularity and system integration, you should look for a tool that supports businesses in meeting their sustainability goals effectively.

 

Turning Scope 3 Reporting into a Competitive Advantage

Far from being a regulatory burden, Scope 3 reporting can be a strategic opportunity.

We highlight its potential to drive business growth by: 

  • Market Differentiation: Establishing leadership as a sustainable brand.  
  • Data-Driven Innovation: Creating better products informed by actionable insights.
  • Supply Chain Resilience: Building transparency to adapt to disruptions and mitigate risks.  

Forward-thinking companies are leveraging Scope 3 as a catalyst for innovation and lasting competitive advantage.

 

Real-World Scenarios

We discussed  two different global manufacturing companies we worked with faced the common challenge of inconsistent and missing Scope 3 data, which led to inefficiencies in their product design and cost analysis.

The solution involved:

  • Enhanced Precision: Transitioned from generalized spend-based estimates to precise, material-level reporting, empowering data-driven decisions.
  • Cost Efficiency: Identified inefficiencies and optimized procurement strategies, driving measurable savings and sustainable growth.
  • Compliance Assurance: Secured full regulatory readiness, ensuring confidence and adherence to the highest industry standards.

For a deeper dive into our success stories click here.

 

Actionable Steps to Get Started   

If your organization is ready to evolve beyond spend-based Scope 3 reporting, here are four practical steps to take today:  

  • Assess Your Current Process: Identify gaps and areas for improvement in your current reporting practices.
  • Engage Stakeholders: Collaborate across departments—procurement, sustainability, engineering—to align goals and define data needs.  
  • Adopt Advanced Tools: Leverage advanced data management and integration tools, specifically, sustainability or environmental focused reporting platforms for accurate data integration and emissions modeling.
  • Pilot and Scale: Launch pilot projects to refine methodologies before scaling them across the organization.

Unlock the Full Potential of Scope 3 Reporting  

Accurate Scope 3 reporting is more than just a regulatory requirement—it’s a pathway to innovation, efficiency, and sustainable growth. Companies willing to embrace advanced methodologies and tools will not only meet compliance standards but also position themselves as leaders in a rapidly evolving market. 

By taking incremental steps, businesses can gradually transition to more advanced reporting practices without overwhelming existing systems. 

Curious to learn more about overcoming the Scope 3 Reporting challenges?

Click here to meet with a Makersite team member.