EINF for manufacturing: technical implementation guide

If you're in manufacturing and want a serious EINF, think more about "data quality + traceability + business coherence" than "nice text". The CNMV's 2023 EINF supervision report provides very clear signals: when they detect relevant incoherencies, they force commitments for future correction or even corrective notes, and the topics that most provoked actions include business model and value chain, climate change, human rights, and Taxonomy disclosures.

Translation to real plant-level work:

Business model and value chain: Saying "we manufacture X" isn't enough. You must explain where the impacts are. For example, if you purchase aluminium or chemicals, your footprint and human rights risks are more in upstream than at the smokestack.

Climate change: Total annual numbers aren't sufficient. They'll examine methodology, perimeter, comparability, and whether your intensities "reconcile" with production.

Taxonomy: Although it sounds financial, in industry it forces a very operational conversation about capex, opex and technical alignment of activities.

Additionally, ESMA insists on something that hurts manufacturing: materiality assessment (impact and financial) as the starting point and the need to properly explain process, context and key judgments.

Useful Framework for Manufacturing: "Process to Impact to KPI Matrix" (and Why It Works)

To avoid "generic EINF", use a matrix that connects industrial process with measurable data:

1. Process and Assets: Furnaces, boilers, compressors, production lines, wastewater treatment, warehouses, internal fleet.

2. Inputs: Electricity, gas, steam, water, raw materials, packaging, auxiliary chemicals.

3. Outputs: Product, co-products, emissions (combustion and process), effluents, waste (hazardous and non-hazardous), residual heat, noise, etc.

4. Risks and Impacts: Regulatory, reputational, costs, safety, supply continuity, physical climate risks.

5. Controls: Permits, maintenance, equipment calibration, HSE audits, supplier qualification.

6. KPIs: Totals and intensities, with allocation rules.

The powerful part is that this matrix forces you to define "calculation rules" per plant, which is exactly what the verifier will ask for later.

Typical Data Problems in Industry (and How to Solve Them Without Inventing Anything)

1) Energy and Emissions Allocation in Multi-Product Plants

If a plant makes several products or formats, your intensity (kWh per tonne, tCO2e per unit) depends on how you allocate common consumption (steam, compressed air, HVAC, wastewater treatment). Best practices:

Prioritize direct measurement through submetering where material (furnaces, compressors, boiler, line A vs B).

If no submetering exists, use defensible physical drivers: line hours, kg processed, theoretical thermal consumption per recipe, etc.

Freeze methodology per fiscal year and document changes as "change in estimate", with comparative.

2) Waste Data with Traceability

In manufacturing, waste and its management are pure materiality. Law 7/2022 reinforces the circular economy approach and traceability in waste management, with obligations and registries that directly impact how you report.

Operationally, what typically fails in EINF is:

• Mixing production waste with "municipal-like waste"
• Not separating hazardous waste by code and waste manager
• Being unable to demonstrate final destination (recovery vs disposal) with consistent documentation

3) Packaging and Extended Producer Responsibility

In industry and B2B, commercial and industrial packaging is no longer "a minor topic". RD 1055/2022 defines packaging broadly and establishes prevention, reuse and packaging waste management obligations.

This opens a very actionable reporting line in EINF:

• Inventory of packaging placed on market by material (plastic, paper, wood, metal)
• Ecodesign and reduction measures (weights, mono-material, reusables)
• Evidence: Technical sheets, purchases, specifications, agreements with SCRAP or equivalent systems, if applicable

EINF and Climate in 2025: Watch Two New Pieces That Intersect

A) Carbon Footprint and Reduction Plan: Royal Decree 214/2025

In Spain, RD 214/2025 creates the carbon footprint registry and, in its chapter IV, introduces obligation to calculate footprint and reduction plan for obligated subjects, with clarifying note from MITECO to specify scope and article 11.

Key point for manufacturing: Although EINF and that RD aren't "the same thing", they share base data (energy, fuels, refrigerants, production). If you don't align methodology, you risk:

• Giving a Scope 1 and 2 in EINF that doesn't match your official footprint or your reduction plan
• Changing factors or perimeter without explaining it

B) Energy Audits in Large Companies: RD 56/2016

RD 56/2016 requires energy audit every 4 years, covering at least 85% of total final energy consumption of installations in Spain within scope.

This is gold for your EINF because:

• It gives you a technical baseline and a pipeline of savings measures with payback
• It allows you to report not only consumption, but also "implemented measures" and results

Verification: How to Prepare So It's Not Torture

In practice, non-financial information verification in Spain relies heavily on ISAE 3000 (Revised), the IAASB standard for assurance engagements other than audits of historical financial information.

What this means for you in an industrial EINF:

• If your KPI comes from manual Excel without controls, they'll ask for evidence and reconciliations
• If there's scope limitation (incomplete data), the verifier can qualify conclusion or put reservations

Minimum "verification-ready" package per KPI:

1. Definition: What it measures, unit, perimeter, what it excludes.

2. Source: System (meter, invoices, MES, waste manager).

3. Calculation rule: Conversions, factors, allocation.

4. Control: Who validates, periodicity, how errors are corrected.

5. Evidence: Invoices, readings, reports, contracts, manager records, calibrations.

CSRD and ESRS Transition: How It Affects a Manufacturing EINF in Spain Today

As of November 19, 2025, CNMV and ICAC published a second joint communication indicating that CSRD remained pending transposition in Spain and that they would accept reports under ESRS as long as they also comply with Law 11/2018.

And at EU level, there are two movements impacting calendars and requirements:

Directive (EU) 2025/794 "stop-the-clock", which adjusts application dates for reporting and due diligence requirements.

Delegated Regulation (EU) 2025/1416 "quick fix", which postpones certain disclosure requirements for some companies.

Practical advice for manufacturing: Although you continue doing "EINF", structure your data as if they were ESRS in what costs you most to change later (materiality, traceability, value chain, climate metrics, waste and personnel).

ESMA additionally emphasizes the connectivity between financial and sustainability information, which in industry translates to capex, opex, environmental provisions, permits and operational risks.

Quick Example of Industrial "EINF Story" That Convinces (Without Marketing)

Typical case: Plant with high gas and electricity consumption, process waste and B2B packaging.

Materiality: Climate (energy cost and regulatory), waste (cost and permits), safety (frequency and stoppages), supply chain (critical raw material).

KPIs: Intensities per tonne and totals, with breakdown by plant.

Plans: Efficiency measures derived from energy audit, scrap reduction, change to reusable packaging where viable.

Evidence: Submetering in key equipment, waste manager contracts and delivery notes, packaging inventory according to RD 1055/2022, and coherence with footprint and plan from RD 214/2025.

If you set it up like this, the EINF stops being "compliance" and becomes a management system that withstands verification and external review.

Methodologies and Standards: How They Fit in an Industrial EINF (Without Mixing Apples and Oranges)

In manufacturing, EINF typically ends up "hanging" from 3 families of standards, each with a different purpose:

Corporate GHG Inventory (Organization Level)

GHG Protocol Corporate Standard as the accounting and reporting framework (organizational boundaries, scopes, principles, recalculation for structural changes).

ISO 14064-1 as specification and guidance to design, manage, report and verify a corporate GHG inventory. Very useful for "landing" controls and evidence in industrial environment.

EU Regulatory Reporting (Disclosure Structure)

CSRD (directive) defines the obligation framework and the shift from "non-financial" to "sustainability" with audit/assurance and digitalization requirements.

ESRS (delegated regulation) defines what is reported and how it's organized: ESRS 1-2 and thematic ones (E1-E5, S1-S4, G1).

Taxonomy and XBRL: EFRAG publishes the taxonomy layer for digital tagging of ESRS. This affects "data modeling" because it pushes you toward granularity and well-defined datapoints.

Product and Value Chain (When EINF Gets Into Product Footprint or Real Circularity)

LCA: ISO 14040 and 14044 (principles, phases, requirements, critical review).

Product carbon footprint: ISO 14067, consistent with ISO 14040/44, to justify "CFP" numbers and module assumptions.

EU Environmental Footprint (PEF/OEF) recommended by Commission as harmonized LCA method for product and organization impacts, with its 2021 recommendation.

Operational idea: In EINF for manufacturing, use GHG Protocol or ISO 14064-1 for the "calculation engine", and ESRS for the "reporting skeleton". LCA/PEF enters when you're asked about circularity, product footprint or design decisions.

Data Structure and Modeling: The Minimum "Data Backbone" for Plant-Level EINF (and Group)

If you model it well, you stop doing "annual Excel" and move to a repeatable and auditable system. A practical model is to separate 4 domains and force stable IDs:

A) Organizational Domain

LegalEntity (Tax ID, country, consolidation)

Site/Plant (plant code, address, permits, CNAE sector)

OrgBoundary (equity share vs operational control, with temporal validity)

ReportingPeriod (month, quarter, year)

B) Operational-Industrial Domain (Real-Time or Near Real-Time)

Asset (furnace, boiler, compressor, wastewater treatment, production line)

Meter (electricity, gas, steam, water, flow meters, internal counters)

ProductionOrder or Batch (if you have MES) with quantities, recipe, line, times

MaintenanceEvent (shutdowns, refrigerant leaks, calibrations)

C) Supply Chain and Logistics Domain

Supplier, Material, PurchaseOrder, DeliveryNote

TransportLeg (mode, km, weight, Incoterm, logistics provider)

WasteStream (code, hazardousness, manager, destination, delivery note)

D) ESG "People and Governance" Domain

Workforce (HC, contract type, site)

Training, Incident (HSE), AuditFinding

WhistleblowingCase (anonymized, typology, status)

Traceability and Lineage (What Saves You in Verification)

Every KPI must be traceable to source records (invoice, meter reading, production report, delivery note, manager record).

Factor and rule versioning: It's not enough to say "MITECO factor 2024". Save factor, source, download date, and file version.

Business rule as code: dbt or equivalent so calculation is reproducible (same input, same output).

Manufacturing-Specific Quality Controls

Completeness: Percentage of days with meter reading per plant and energy vector.

Plausibility: Physical limits (e.g., kWh per tonne can't jump 5x without cause).

Reconciliation: Billed electricity vs sum of submeters (maximum tolerable difference).

Change control: Every manual correction must leave trace (who, when, why, evidence).

Integrations and Automation: Typical Connectors (and Where They Break)

Recommended architecture (vendor-agnostic):

Batch ingestion: ERP (SAP, Dynamics), procurement, HR, accounting, waste manager.

Time-series ingestion: SCADA, BMS, IoT, energy submetering, water flows.

"Staging" and normalization: Clean units (Nm3, kg, kWh GCV/NCV), timezones, duplicates.

"Curated" for KPIs: Fact tables per plant-month and master dimensions (plant, energy, material, supplier).

"Semantic layer": KPI definitions and mappings to ESRS datapoints.

Integration Patterns That Work Well

Energy invoices: Email parsing + OCR only if no EDI exists. Better: automatic download from supplier portal or EDI if exists (avoid OCR because it introduces silent errors).

Meters and telemetry: Manufacturer API or daily SFTP export from SCADA.

Procurement: ERP API or incremental extract (changes by posting date).

Waste: Integration with manager's system if they have it; if not, structured capture from delivery note with validations.

Typical Failure Points

Units and calorific value: Gas in kWh vs m3, NCV vs GCV. If you don't normalize, your Scope 1 dances.

Perimeters: Rented plants, assets outside Spain, or structural changes. Here ISO 14064-1 and GHG Protocol demand discipline (managed inventory, changes, recalculations).

Suppliers: "Promised" Scope 3 data that arrives late or doesn't arrive. You need fallback.

How to Land ESRS in Manufacturing Without Duplicating Work (Operational Mapping)

Without repeating theory: the trick is mapping your data domains to thematic ESRS so reporting is a "view" of the model, not a separate project:

ESRS E1 (climate): Energy, fuel, refrigerant, production, electricity contracts tables.

ESRS E2-E5: PRTR, waste, water, materials, circularity, environmental incidents.

ESRS S1: HRIS + HSE + training, per site.

ESRS G1: Compliance, whistleblowing channel, third parties, gifts, conflicts.

If you do this mapping from data design, "writing the EINF" becomes assembling narrative and controls, not chasing spreadsheets.

For organizations aligning their disclosures with broader EU sustainability goals, it's also valuable to understand how sustainable finance frameworks connect to industrial reporting. These frameworks help link operational data with financial performance, investment eligibility, and transparency in sustainable activities.

Common Pitfalls and How Manufacturing Teams Avoid Them

Mistake 1: Treating EINF as a "Sustainability Project"

The problem: Companies assign EINF to sustainability or HSE team without involving Finance, IT or senior management.

Why it fails: EINF is a reporting directive similar to financial reporting. It requires audit-grade data, internal controls and board-level governance.

Solution: Establish a cross-functional EINF programme led by Finance or jointly by Finance and Operations, with clear accountability to senior leadership.

Mistake 2: Starting with Tools Before Defining Requirements

The problem: Companies rush to buy software before clarifying scope, responsibilities, data definitions or materiality assessment.

Why it fails: The tool becomes a data graveyard without clear processes. Garbage in, garbage out.

Solution: First complete your materiality assessment, map data sources, define KPIs and establish governance. Then select a tool that fits your requirements.

Mistake 3: Underestimating Scope 3 and Supply Chain Burden

The problem: Companies assume they can quickly collect detailed data from all suppliers.

Why it fails: Small suppliers don't have sophisticated data systems. Requesting too much too fast damages relationships without improving data quality.

Solution: Segment suppliers by criticality. Focus detailed data requests on your top 20% suppliers (by spend or impact). Use industry averages and estimates for the long tail, with a documented improvement plan.

Mistake 4: Not Granular Enough for Plant-Level Data

The problem: Companies aggregate data at corporate level without plant-specific detail.

Why it fails: In manufacturing, the meaningful data lives at plant level. Corporate aggregates hide the real drivers and make operational improvements impossible.

Solution: Build your data model at plant level from day one. Aggregate up to corporate for reporting, but maintain granular data for management and verification.

Mistake 5: Ignoring the Audit Requirement

The problem: Companies treat EINF as a marketing exercise with approximate numbers and flexible methodologies.

Why it fails: EINF requires external assurance. Auditors will challenge your data sources, calculations and controls.

Solution: Design your EINF system with audit in mind from the start. If you can't trace a number back to a source document with clear calculation methodology, it's not audit-ready.

Recommendations Before Implementing Manufacturing EINF

Define Regulatory Scope and Critical KPIs

Be very clear about which regulatory frameworks you need to comply with (EINF, CSRD, SBTi, Taxonomy, ISOs, etc.) and which KPIs are truly critical for your business.

Not all manufacturers have the same obligations or objectives. A steel producer will prioritize different metrics than a food manufacturer or electronics assembler.

Clarity on scope prevents wasted effort on irrelevant data and ensures you invest resources where they matter most.

Determine Number of Users and Departments Involved

EINF isn't a one-person job. It requires collaboration across Finance, Operations, Procurement, HR, HSE and IT.

Identify who will collect, validate, approve and use the data. A good ESG platform should support multiple users with appropriate access controls and workflow management.

The more intuitive the system, the faster the adoption and less time wasted on training and support.

Identify Necessary Integrations

Your relevant ESG data already exists in your business systems – it's scattered across ERP, MES, SCADA, billing systems, contractor portals and spreadsheets.

A proper EINF solution should integrate directly with these sources, eliminating duplicate data entry and ensuring consistency.

The better your integrations, the more automated and accurate your reporting becomes.

Evaluate Total Cost of Ownership (TCO)

Look beyond the initial licence fee. Consider implementation time, integration costs, training, ongoing maintenance and potential consultancy support.

A solution that seems cheap may become expensive if it requires complex configurations or external services to function properly.

Invest in a cloud-based, modular, ready-to-use platform that can scale without hidden costs or technical dependencies.

Why Dcycle is the Best Solution for Manufacturing EINF

When choosing an ESG management platform for EINF compliance, what really matters isn't just functionality – it's the ability to deliver a comprehensive, flexible solution oriented to the real value of ESG data.

We are not auditors or consultants. We are a Solution designed for companies that want to measure, manage and communicate their ESG impact simply and efficiently.

Our objective is clear: enable every organization to collect all their ESG information and distribute it automatically to different use cases, without complications or manual processes.

We centralize environmental, social and governance data from any source – ERP, CRM, spreadsheets, internal systems – and convert it into standardized, traceable metrics ready for official reports. Companies can generate documentation compatible with EINF, CSRD, SBTI, European Taxonomy, ISOs or any other standard in minutes.

Why Manufacturing Companies Choose Dcycle

Designed for Operational Reality: We understand that in manufacturing, sustainability data is operational data. Our platform integrates with the systems and processes you already use.

Automated and Simplified: Everything works in the cloud, with no complex installations or technical development needed. In a few clicks, teams can visualize performance, identify improvement areas and prepare audit-ready reports.

Complete Traceability: Every metric links back to source evidence – invoices, meter readings, production logs, waste transfer notes. This isn't just good practice, it's a requirement for external assurance.

Multi-Framework Support: Generate reports for EINF, CSRD, Taxonomy, SBTi, ISO and any other framework from a single dataset. No duplication, no inconsistencies.

Strategic, Not Just Compliance: We firmly believe sustainability should be a strategic lever for competitiveness, not an administrative burden. Our mission is clear: convert ESG data into smarter, more efficient and more profitable business decisions.

With Dcycle, manufacturing companies can control their information, reduce costs, automate processes and guarantee complete traceability of their ESG indicators.

In a market where measuring well is the difference between moving forward and falling behind, our proposition is simple: make sustainability work as a real engine for growth.

Frequently Asked Questions (FAQs)

What should I prioritize when implementing EINF in manufacturing?

When implementing EINF, prioritize three core elements: automation, traceability and adaptability.

Automation means collecting data directly from source systems (meters, ERP, production systems) without manual intervention. This reduces errors, saves time and ensures consistency.

Traceability means every number can be traced back to a source document with clear calculation methodology. This is essential for audit and builds confidence in your data.

Adaptability means your system can accommodate different reporting frameworks (EINF, CSRD, Taxonomy, SBTi, ISOs) and evolve as regulations change, without requiring major reconfiguration.

Also ensure your solution is easy to implement, scalable and compatible with your existing systems. This avoids excessive costs and lets you start working quickly while maintaining data reliability from day one.

What are the main challenges for manufacturers under EINF?

The main challenges are:

Data fragmentation: Sustainability data lives across multiple systems (energy bills, production logs, waste contractors, HR systems, purchasing records). Consolidating it requires careful integration.

Plant-level granularity: Corporate aggregates aren't enough. You need data by facility, process line and product category to support both reporting and operational improvements.

Supply chain complexity: Scope 3 emissions often represent 70-90% of total footprint for manufacturers. Collecting reliable supplier data is difficult, especially from smaller partners.

Resource constraints: Most manufacturers don't have dedicated sustainability teams. EINF competes for attention with production, quality, delivery and cost objectives.

Audit readiness: Unlike previous sustainability reports, EINF requires external assurance. Your data and processes must meet audit standards.

How does verification work for EINF in Spain?

In Spain, EINF verification is mandatory and must be performed by an independent provider using recognized assurance standards (typically ISAE 3000 Revised).

What verifiers examine:

Data sources: Can you trace each number to original documents (invoices, meter readings, contracts)?

Calculation methodologies: Are formulas, factors and conversions documented and correct?

Internal controls: Do you have processes to capture, validate, approve and correct data?

Perimeter consistency: Does your sustainability reporting scope match your financial consolidation?

Material misstatements: Are there errors that would change user decisions?

Preparation tips:

• Build evidence packages before verification starts
• Document all methodologies and assumptions
• Implement financial-grade controls (reconciliations, approvals, segregation of duties)
• Run internal audits to identify gaps early
• Maintain version control for factors, methodologies and perimeters

Can I use the same data for EINF and CSRD?

Yes, absolutely – and you should. The more you align your data infrastructure between EINF and future CSRD requirements, the smoother your transition will be.

What's compatible:

Data sources: Energy, emissions, water, waste, production, workforce, safety – same underlying data.

Calculation methodologies: GHG Protocol, ISO 14064-1, industry-specific factors – can serve both frameworks.

Evidence and controls: Traceability, documentation, approval workflows – requirements are similar.

What differs:

Materiality approach: CSRD requires double materiality (impact AND financial), EINF is more flexible.

Disclosure granularity: ESRS has more specific datapoints and mandatory disclosures.

Digital format: CSRD will require XBRL tagging, EINF currently doesn't.

Practical approach: Build your data model to ESRS granularity now, even if you're only reporting EINF today. This "future-proofs" your infrastructure.

How long does it take to implement an EINF-ready system for manufacturing?

For a typical manufacturing company, a realistic timeline is:

90 days for minimum viable system: Scope definition, materiality assessment, top data sources connected, basic controls in place, first draft disclosures with identified gaps.

6-12 months for full implementation: All material datapoints automated, complete controls, evidence management, workflow approvals, audit-ready documentation and first complete EINF report.

Ongoing improvement: ESG reporting is not a one-time project. Expect continuous refinement of data quality, expansion of Scope 3 coverage, deeper supply chain engagement and more sophisticated analytics.

The key is to start with a solid foundation – clear scope, cross-functional governance, robust data model and the right technology platform. Building on shaky foundations wastes time and creates problems later.

With the right approach and the right tools, EINF compliance becomes a manageable process that strengthens your business rather than burdening it.

Calculation and Assumptions: Emission Factors, Data Gaps, Estimates and Uncertainty (Plant Mode)

Scope 1 and 2 (Base for EINF)

At corporate level, the pattern is Activity data × Emission factor, with coherent and traceable factors. GHG Protocol defines principles and treatment of Scopes, and its Scope 3 guidance gives you strategies for specific data vs proxies.

Spain factors: MITECO publishes an Excel of "Emission factors" that many companies use as operational reference (fuels, etc.). Important: save version and don't mix years within same period without explaining it.

Scope 2 (electricity): In modern reporting, separate:

Location-based: Average grid factor

Market-based: Contracts, guarantees of origin, contractual mix

The Scope 2 guidance is integrated as update to Corporate Standard.

Data Gaps: Three Levels of Defensible Estimation

1. Direct measurement: Meter, invoice, weighing system, consumption report.

2. Engineering estimation: Energy balance by equipment, load curves, operating hours.

3. Economic proxy: EUR spend × monetary factor (last resort for Scope 3).

Golden rule: Document "why" and "what percentage" of the KPI is estimated. In audit, what kills isn't estimating, it's estimating without traceability.

Uncertainty: Turn It Into a Control, Not an Excuse

Identify sources: Measurement, sampling, factors, multi-product allocation.

Give ranges for critical KPIs (e.g., +/- x percent) and prioritize actions to reduce uncertainty where it's material.

ISO 14064-1 is oriented to managing inventories with verifiable approach, which fits very well with this discipline.

Reporting and Audit Best Practices: Evidence, Internal Control and "Verifiability" Without Pain

Leverage What Already Exists: EU ETS MRV If You're an Affected Installation

If your plant is under EU ETS, you already have robust annual MRV discipline (monitoring, reporting, verification). Reusing that dataset (fuels, methodologies, controls) reduces friction and automatically raises your EINF level.

Minimum Evidence per EINF Block (Industrial)

Energy and GHG: Invoices, readings, monitoring plan, versioned factors, reconciliations.

Water: Readings, analytics, permits, incidents.

Waste: Delivery notes, manager contracts, classification, destination. For industry, you can additionally cross-check consistency with PRTR if you're in reporting perimeter.

Production: MES orders or signed production report, for intensities.

People: HRIS extract, HSE reports, incident investigation, training.

Internal Control "Monthly Cycle" Type

Instead of "annual ESG close", apply a mini monthly close per plant:

1. Freeze consumption and production

2. Automatic validations (completeness, outliers)

3. Reconciliation with invoices (energy) and delivery notes (waste)

4. Plant manager approval

5. Immutable snapshot (to avoid rewrites without control)

Assurance: Think Like a Verifier

ISAE 3000 (Revised) is a common reference for non-financial info assurance engagements: it penalizes you for scope limitations and material errors, and rewards clear criteria and sufficient evidence.

"Anti-shock" checklist before sending to verification:

• Definition of each KPI + criterion (what enters, what doesn't)
• Consolidated perimeter with year's changes explained
• Factor and source registry (download, version, date)
• Correction procedure and traceability of adjustments
• Reconciliation tests saved as evidence