ESG Data in Manufacturing: Tools and Best Practices

ESG data is now a critical focus for manufacturers, shaping contracts, financing, and valuations. It goes beyond traditional metrics by addressing environmental, social, and governance (ESG) factors, such as energy use, workplace safety, and leadership transparency. This shift is driven by tighter U.S. regulations, investor demands, and customer expectations.

Key points:

• Environmental metrics include emissions, energy, water, and waste.
• Social metrics cover safety, training, diversity, and labor practices.
• Governance metrics focus on ethics, compliance, and data security.

To manage ESG data effectively:

• Use frameworks like SASB, ISSB, and GHG Protocol to align reporting.
• Integrate systems like ERP, MES, and ESG software for accurate tracking.
• Automate data collection and implement quality controls for audit readiness.

ESG data isn't just about compliance - it directly impacts financial performance, contract opportunities, and operational efficiency.

Simplifying ESG Data Management: A Practical Approach

ESG Reporting Frameworks and Standards

Navigating ESG reporting can feel overwhelming, especially with the variety of frameworks available, each emphasizing different aspects of sustainability. For manufacturers, understanding these frameworks is key to creating a unified data system that meets diverse reporting requirements. Below, we’ll dive into the major frameworks and how they connect to manufacturing metrics.

Major ESG Frameworks for Manufacturing

SASB (Sustainability Accounting Standards Board), now part of ISSB, provides industry-specific standards for 77 sectors. It focuses on financially critical metrics like energy intensity and workplace safety, making it especially relevant for manufacturers.

The ISSB's IFRS S1 and S2 standards expand on SASB’s foundation by integrating TCFD recommendations. These standards establish a global baseline for sustainability and climate disclosures. IFRS S2, in particular, addresses climate-related risks and opportunities through detailed emissions data and scenario analysis. For U.S. manufacturers, these standards align with SEC climate rules and global market demands.

GHG Protocol is the go-to standard for measuring greenhouse gas emissions. It defines Scope 1 (direct emissions from fuel use and on-site processes), Scope 2 (indirect emissions from purchased electricity, steam, or heating), and Scope 3 (indirect emissions across the value chain). This protocol serves as the backbone for emissions data in all other frameworks.

TCFD (Task Force on Climate-related Financial Disclosures) provides a principles-based framework structured around four pillars: governance, strategy, risk management, and metrics & targets. It helps manufacturers assess how climate risks - both physical (like flooding or heat stress) and transitional (such as carbon pricing or shifting customer preferences) - impact financial performance. TCFD also requires scenario analysis to evaluate different climate futures.

CDP (Carbon Disclosure Project) operates as a platform for environmental data disclosure, covering climate, water, and forests. In 2023, over 21,000 organizations reported through CDP, with many manufacturers participating due to supply chain requests. CDP emphasizes emissions, energy use, climate governance, and supplier engagement, rewarding companies with well-documented ESG systems.

GRI (Global Reporting Initiative) takes a broader view, addressing sustainability concerns for employees, communities, regulators, and civil society. Unlike SASB and ISSB, which focus on investor-centric data, GRI emphasizes comprehensive reporting on energy, emissions, water, waste, and labor practices. It’s designed to communicate ESG performance to a wider audience.

Matching Frameworks to Manufacturing Metrics

After understanding these frameworks, manufacturers can align their operational metrics to meet the requirements of each.

A smart approach is to recognize the overlap in data requirements across frameworks. By building a centralized, robust data system, manufacturers can streamline reporting and meet multiple standards simultaneously.

For environmental metrics, emissions data is universally required. The GHG Protocol provides the method for calculating Scope 1, 2, and 3 emissions (measured in metric tons of CO₂e). Frameworks like SASB, ISSB, TCFD, and CDP may require further breakdowns by facility or product line. Water and waste metrics are also critical, typically reported in gallons or cubic meters for water and short tons or pounds for waste.

For social metrics, workplace safety is a key focus. Metrics such as OSHA recordable incident rates and lost-time incident rates (calculated per 200,000 hours worked) are commonly reported. Additional metrics include employee training hours, turnover rates, diversity statistics, and supply chain labor practices, which are often monitored through audits for issues like child labor or forced labor.

For governance metrics, frameworks prioritize transparency and accountability. This includes disclosing board and management structures related to ESG, anti-corruption policies, whistleblower systems, and data security practices. ESG-linked executive compensation is also gaining traction as an indicator of leadership commitment to sustainability goals.

To simplify compliance and reduce audit risks, manufacturers can create a matrix that maps core metrics to each framework’s requirements. For instance, Scope 1 and 2 emissions data calculated via the GHG Protocol can be cross-referenced with SASB metrics, TCFD pillars, CDP questions, and GRI disclosures. Integrating ESG data with financial and operational systems enhances both compliance and overall performance tracking.

Comparison Table of ESG Standards

Here’s a snapshot of how these frameworks apply to U.S. manufacturers:

Building an ESG Data System

Once you've grasped the frameworks and standards, the next step is creating a data system capable of delivering the metrics you need. For manufacturers, this involves connecting operational technology, financial systems, and ESG-specific tools into a single architecture that ensures accurate and auditable data.

Components of an ESG Data System

A unified data system is essential for consistent ESG reporting. It pulls data from multiple sources across your manufacturing operations, aiming to automate data collection from existing systems instead of relying on manual input.

• ERP Systems:
  ERP systems act as the backbone for financial and procurement data. They track site-level electricity and fuel spending, production volumes, material purchases, logistics costs, and capital expenditures. This data supports analyses like energy costs, waste assessments, and Scope 3 purchasing footprints. Since ERP systems are structured for financial reporting, they often include the controls and audit trails needed for ESG disclosures.
• Manufacturing Execution Systems (MES):
  MES collect production-level data, such as production volumes, scrap, rework, and run times. By pairing this data with energy or emissions figures, you can calculate metrics like kilowatt-hours per unit produced or waste per ton of output, helping track efficiency over time.
• SCADA and Process Historians:
  SCADA systems and process historians gather high-frequency data directly from equipment and production lines, monitoring parameters like power usage, fuel flow rates, and operating temperatures. This detailed information allows precise calculations of energy use, process emissions, and water consumption, enabling robust Scope 1 emissions and energy efficiency metrics.
• Energy Management Systems:
  These platforms centralize facility-level consumption data from submeters, building management systems, and utility data platforms. They capture interval readings for electricity (kWh or MWh), natural gas (therms or MMBtu), steam, and compressed air. Such data is critical for compiling Scope 1 and 2 greenhouse gas inventories, managing utility costs, and conducting peak-demand analyses. By connecting to utility APIs, these systems eliminate manual meter readings.
• Additional Operational Systems:
  Other systems also play a role. Environment, Health, and Safety (EHS) platforms log workplace incidents, chemical inventories, permits, and waste records. Human Resources systems provide data on workforce demographics, turnover, training hours, and diversity. Procurement and supplier portals collect supplier ESG questionnaires and certifications.

Since these systems were not originally designed to work together, a centralized repository is the best solution. Integration through APIs or data tools ensures standardized definitions, units, and calculations. Once consolidated, calculation engines can generate metrics like Scope 1–3 emissions or energy and water intensity using configurable emission factors (e.g., converting kilowatt-hours to metric tons of CO₂e with EPA guidelines). Reporting layers then produce dashboards and disclosure templates, allowing updates on a regular schedule - weekly or monthly instead of annually.

Switching from spreadsheets to a centralized platform offers immediate advantages: fewer manual errors, quicker reporting cycles, and deeper insights. This integration strengthens the connection between ESG performance and financial results. Advisory firms like Phoenix Strategy Group (link) can help design these systems, ensuring seamless integration between financial and ESG data as your business evolves.

Governance and Ownership of ESG Data

Integrating data sources is just the beginning - strong governance and clear ownership are key to maintaining data integrity. Reliable ESG data requires more than just technology; it needs a governance structure that assigns accountability, much like financial reporting.

Start by securing executive sponsorship. Form a sustainability committee at the board or C-suite level, often led by the CFO, COO, or Chief Sustainability Officer, to set ESG strategy, approve targets, and oversee risk and disclosure practices. This high-level oversight signals a commitment to rigorous ESG management.

Create a cross-functional ESG working group with representatives from operations, EHS, finance, HR, procurement, and IT. This team should define the ESG data model, establish a reporting calendar, and build a control framework. Assigning a data owner and a dedicated data steward for each key metric ensures accountability. For example, an EHS director might oversee safety metrics, while an analyst manages day-to-day data quality. Documenting these roles in a RACI matrix ensures that changes - like updates to emission factors or adjustments to organizational boundaries - are properly reviewed and approved.

As regulations evolve (e.g., proposed SEC climate rules), many manufacturers are adopting internal controls similar to those used in financial reporting. These include segregation of duties, version control for methodologies, documented assumptions, and periodic internal audits. Finance teams and partners like Phoenix Strategy Group (link) can help align ESG controls with financial reporting standards, especially when ESG data is tied to SEC filings or lender requirements. Clear governance also means setting fixed deadlines for data collection, validation, and sign-off, with every data point backed by an auditable trail for transparency during external reviews.

U.S. Data Formats and Units

Consistency in measurement units is critical for ESG reporting, especially for U.S. manufacturers who often work with mixed systems. While operations might track energy in kilowatt-hours and natural gas in therms, water is typically measured in gallons, and waste in short tons - even though many global ESG frameworks require metric units. The best practice is to store data in metric units while displaying it in U.S. formats for internal use.

For instance, utility data might record electricity in kilowatt-hours or megawatt-hours and natural gas in therms or MMBtu. A good ESG system automatically converts these into a common energy base (like MWh or gigajoules) and calculates metric tons of CO₂e using approved emission factors. Keeping the original units alongside the converted values helps reconcile data with utility invoices.

Similarly, water usage - often tracked in gallons - can be converted into cubic meters for external reports aligned with frameworks like GRI or SASB, while internal dashboards still display gallons. This approach ensures compliance with reporting standards without disrupting day-to-day operations.

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Tools for Managing ESG Data

When it comes to managing ESG data, manufacturers often rely on three key technology layers: existing ERP and financial systems for storing relevant data, process data platforms for capturing real-time operational metrics, and specialized ESG reporting software for consolidating data into framework-aligned disclosures. Let’s break down how these systems work together, starting with ERP and financial systems.

ERP and Financial Systems

ERP systems like SAP, Microsoft Dynamics, and Oracle already house a wealth of ESG-relevant data. These systems track everything from energy usage and costs by facility, to material inputs, waste and recycling expenses, logistics costs, supplier spending, and safety incident costs. However, because this data is primarily structured for financial reporting, it needs careful mapping and configuration to be useful for ESG disclosures.

Finance and IT teams can enhance these systems by adding metadata - like facility IDs or time periods - to integrate and aggregate consumption data by plant, product line, or region. Many manufacturers are even customizing their ERP systems to include ESG-specific fields, such as "emissions source" or "waste type." This allows transactions to be tagged with ESG details right at the source.

For example, platforms like SAP and Microsoft Dynamics can generate standard reports and dashboards to track metrics such as energy intensity (kWh per unit produced), scrap rates, and supplier ESG performance. Aligning your chart of accounts with ESG frameworks like GRI or SASB simplifies this process further.

Oracle highlights that ESG data often comes from multiple internal systems, including HR, finance, supply chain, and manufacturing, and warns that integrating these sources for disclosure can be more complex than many organizations expect.^[5]

Using ERP systems as the backbone ensures strong financial controls, while role-based access and workflows allow plant controllers and sustainability teams to validate ESG data before it’s included in external reports.

Process Data Platforms

While ERP systems focus on financial metrics, process data platforms provide the real-time operational data needed for precise environmental measurements. Tools like Manufacturing Execution Systems (MES), SCADA systems, and process data historians track key metrics such as electricity and fuel consumption, production throughput, scrap volumes, water usage, and even environmental control readings like NOx concentrations. By linking this data to product, batch, or shift identifiers, manufacturers can calculate critical intensity metrics, such as emissions per ton of product or water usage per unit produced.

Because these platforms are already integral to quality control and operational efficiency, their time-series data can also support automated reporting for environmental metrics. Manufacturers often use integration tools to push this data into a central repository, where it can be reconciled with ERP data using shared identifiers like facility codes and timestamps. This enables traceable, audit-ready ESG metrics.

ESG Reporting Software

Specialized ESG reporting software adds another layer, offering tools for calculations, framework-specific templates, and workflows designed for compliance and investor reporting. Platforms like Sphera, Workiva, Cority, and IBM Envizi consolidate data from various sources, calculate greenhouse gas emissions across Scopes 1, 2, and 3, and generate reports aligned with frameworks like GRI and SASB.

These tools also include features like configurable dashboards, scenario modeling, variance analysis, and document management with version control. Some even offer supplier portals, enabling manufacturers to collect supplier-specific emissions data rather than relying on generic assumptions, which significantly improves Scope 3 accuracy.

As of early 2024, over 23,000 verified Environmental Product Declarations (EPDs) existed for construction products, showcasing how digital tools are being used to manage product-level environmental data.^[1] For manufacturers with simpler operations, extending existing BI tools like Power BI or Tableau with ESG data models might suffice. However, growing regulatory demands and investor scrutiny often require the capabilities of dedicated ESG platforms.

PwC notes that many companies still rely on fragmented systems and manual processes for ESG data, but upcoming SEC regulations may require controls similar to those used for financial reporting.^[4]

The move from spreadsheets to integrated software platforms is largely driven by regulatory pressure and the merging of ESG and financial reporting. This shift enables real-time monitoring of key metrics like energy intensity, emissions, and safety performance.

To ensure high-quality, auditable data, manufacturers should implement governance policies that define metric ownership, standardize naming conventions, and document calculation methods. Automated validation rules can flag errors like abnormal energy use or missing readings early on. Periodic reconciliation between financial, operational, and ESG datasets further strengthens data credibility.

Advisory firms like Phoenix Strategy Group (https://phoenixstrategy.group) can assist in integrating these systems, mapping ESG needs to existing data structures, and designing models that connect ERP, MES/SCADA, and ESG reporting platforms seamlessly.

Best Practices for ESG Data Accuracy

Ensuring ESG data is accurate demands a level of discipline similar to financial reporting. The difference between data that holds up under investor scrutiny and data that falls apart during an audit often boils down to three key practices: prioritizing what’s most important, implementing strong quality controls, and embracing automation.

Focus on Material Metrics

Start by concentrating on ESG metrics that are financially material and directly influenced by your operations. For U.S. manufacturers, this typically includes:

• Energy intensity (measured in kWh or MMBtu per unit of output)
• Greenhouse gas emissions (Scope 1, 2, and increasingly Scope 3, expressed in metric tons of CO₂e)
• Water usage (gallons)
• Waste generation (tons, categorized by hazardous versus non-hazardous and recycling rates)

On the social front, metrics like OSHA recordable incident rates, lost-time injury frequency, workforce diversity, and training hours often take precedence. Governance measures typically include board oversight of ESG issues, the effectiveness of ethics programs, and cybersecurity policies.

Conduct a materiality assessment using SASB guidelines, which provide industry-specific insights. For example, a heavy industrial manufacturer should emphasize energy intensity and carbon emissions, while a food and beverage company might focus more on water use and waste reduction. This approach ensures your efforts are focused on metrics that satisfy regulatory requirements and impact financial performance, avoiding wasted energy on less critical indicators.

Data Quality Controls

Reliable ESG data relies on robust quality controls. Standardize definitions and units across all locations and document them in an ESG data dictionary. For instance, every facility should define “production unit” the same way and consistently use kWh for electricity, gallons for water, and tons for waste.

Monthly reconciliations of utility data are essential. Cross-check meter readings or utility bills against internal records, flagging any discrepancies. For example, if your internal system shows 50,000 kWh consumed in March but the utility bill reflects 55,000 kWh, investigate and document the 10% variance before including the data in your ESG report.

Audit trails are critical for defensible data. Maintain logs detailing who entered or modified each data point and when. Keep supporting documents - such as utility invoices, lab reports, and safety logs - readily accessible. If an auditor asks how Scope 1 emissions were calculated for a specific facility, you should be able to provide natural gas bills, emission factors (e.g., EPA’s eGRID), calculation records, and the approval chain.

Validation rules are another safeguard. Configure systems to flag anomalies like unusually high energy use or missing data. Require explanations for manual adjustments. Combining automation with these controls significantly reduces errors and speeds up reporting.

According to PwC, proposed SEC climate disclosure rules will likely bring ESG data under the same level of scrutiny as financial reporting, making robust management practices a necessity.^[4]

For high-risk metrics like greenhouse gases, energy consumption, and safety incidents, treat them with SOX-like controls. This means implementing segregation of duties, independent reviews, and periodic audits. If your financial data requires rigorous sign-offs, your ESG data should meet the same standards.

Automating Data Collection and Reporting

Once robust quality controls are in place, automation can take your ESG reporting to the next level. Eliminating manual data entry reduces errors and speeds up processes. IoT sensors and submeters can capture real-time data on energy, water, and other critical parameters, linking consumption directly to production for more accurate intensity calculations.

For instance, an automotive parts manufacturer might install IoT-enabled meters on production lines, integrate them with a manufacturing execution system (MES), and automatically generate dashboards showing CO₂e emissions, water usage per unit, and safety incidents.

Automated API integrations can streamline the consolidation of ESG and financial data. Validated data from ERP, HRIS, and process systems can flow directly into a centralized ESG platform, eliminating the need for emailed spreadsheets. This ensures consistent and auditable reporting.

Integrated ESG analytics platforms also enable more frequent reporting - weekly or monthly instead of annual updates. For example, if energy intensity spikes on a production line, automated alerts can notify the plant manager immediately, rather than waiting for quarterly reviews.

Advisory firms like Phoenix Strategy Group (https://phoenixstrategy.group) specialize in helping manufacturers design these systems. Their expertise in data engineering - such as ETL pipelines, data warehouses, and analytics dashboards - lays the groundwork for automated, audit-ready ESG reporting that integrates seamlessly with your existing systems.

Transitioning from spreadsheets to automated platforms isn’t just about efficiency. It’s about creating a data infrastructure that meets the scrutiny of regulators and investors alike. When ESG data is managed with the same rigor as financial data, it builds trust and strengthens your position with stakeholders.

Roadmap for ESG Data Implementation

Creating a reliable ESG data system involves a step-by-step approach, beginning with basic compliance and evolving into a fully integrated, strategic framework. The ultimate aim is to transition from scattered spreadsheets to investor-ready reporting that supports regulatory needs and business strategies.

Main Takeaways for Manufacturers

Before diving into the process, keep these six key principles in mind as they form the foundation of your ESG data journey:

• Choose a primary framework early: Whether it’s SASB for investor metrics or GRI for broader reporting, selecting your framework upfront ensures your efforts align with stakeholder priorities and reporting requirements, avoiding wasted time on irrelevant metrics. ^[6][7]
• Establish a structured data system: Integrate your internal systems into a centralized, well-managed data repository. Connecting tools like ERP systems, manufacturing execution systems, and ESG platforms minimizes errors and inconsistencies common in manual reporting. ^[7][3][5]
• Focus on meaningful metrics: Concentrate on key indicators like energy intensity (kWh or MMBtu per unit), greenhouse gas emissions (metric tons of CO₂e), water usage (gallons), waste generation (tons), and OSHA incident rates. These are the metrics regulators and investors prioritize. ^[6][7]
• Prioritize data quality controls early: Assign clear ownership, use standardized definitions, and implement audit trails and validation checks. With regulatory scrutiny, such as SEC climate disclosure rules, increasing, treating ESG data like financial data is essential. ^[6][8][4]
• Leverage automation and specialized tools: Tools like IoT sensors, ESG software, and process data platforms reduce manual workload and enable frequent reporting. These technologies also help link ESG performance to operational metrics like yield and cost. ^[6][8][3][5]
• View ESG data as a strategic asset: Incorporate ESG metrics into decisions around capital planning, product development, and supply chains. Doing so can uncover efficiency opportunities, reduce risks, and appeal to ESG-conscious stakeholders like customers and lenders. ^[7][3]

These principles set the stage for the four-phase roadmap explained below.

Phased Implementation Approach

The path to a robust ESG data system unfolds in four stages:

Phase 1: Baseline & Compliance Readiness (0–6 months)
Start by identifying your regulatory obligations. Are you preparing for SEC climate rules or customer ESG audits? Once you understand your landscape, select your primary frameworks - SASB and GRI are common choices for manufacturers. ^[6][7][4]

Next, assess your existing data sources. Many metrics can be found in utility bills, ERP systems, manufacturing execution systems, HR platforms, and EHS tools. Establish baselines for energy use, emissions, water consumption, safety incidents, and other ESG metrics. Assign data ownership and implement quarterly validation processes to ensure accuracy. ^[7][8][5]

Phase 2: Systematize & Digitize (6–18 months)
Standardize your approach by defining consistent data units across all locations - kWh for electricity, gallons for water, tons for waste, etc. Automate data collection from meters, production systems, and key suppliers wherever possible. ^[7][8][3]

Introduce governance and internal controls to ensure reliability. This includes review cycles, variance checks, and documented methodologies that mirror financial reporting standards. Automation and standardization at this stage reduce manual errors and improve data consistency. ^{[6][7][3][5][8][4]}

Phase 3: Integrate with Operations & Finance (18–36 months)
In this phase, ESG data becomes part of your core business processes. Link ESG metrics to financial planning models to assess scenarios like energy cost curves or water risk impacts. For example, plant managers can see kWh per unit alongside scrap rates and OEE, making the connection between ESG improvements and operational efficiency clear. ^[7][3][4]

Expand your Scope 3 reporting to include purchased materials, logistics, and end-of-life impacts - critical areas for manufacturers. Use ESG analytics to prioritize capital investments based on both financial returns and environmental benefits, ensuring ESG efforts align with broader business goals. ^[1][6][7][2]

Phase 4: Advanced & Strategic Integration (36+ months)
At this stage, ESG considerations are embedded across your business. Product design teams factor in material choices and recyclability from the start, supported by traceability systems. Real-time ESG dashboards provide leadership with performance insights, and ESG KPIs become part of management incentives. ^[1][7][3][4]

Your disclosures are now investor-grade, aligned with recognized standards, and verified by third-party audits. ESG insights not only enhance operational decisions but also strengthen market positioning and financial performance.

Working with Advisory Partners

For manufacturers looking to accelerate their ESG journey, advisory partners can be invaluable. They bring expertise in financial modeling, data engineering, and ESG reporting, helping you build the necessary infrastructure faster.

Phoenix Strategy Group (https://phoenixstrategy.group) specializes in equipping manufacturers with the tools and systems needed for investor-grade ESG reporting. Their services include data integration, analytics dashboards, and automated processes that seamlessly connect with existing ERP and operational systems.

Additionally, their fractional CFO and FP&A services help embed ESG factors into financial models, conduct scenario analyses for risks like carbon pricing, and prioritize capital projects based on both financial and ESG outcomes. This level of readiness strengthens your position during funding rounds, mergers, or acquisitions, as investors increasingly value sustainability in their assessments.

FAQs

How can manufacturers maintain accurate and reliable ESG data while seamlessly integrating it with their financial and operational systems?

To produce precise and trustworthy ESG data, manufacturers should focus on setting up strong data collection and validation systems. Automated tools can play a key role here, reducing the chances of human error, while regular audits help ensure the data remains accurate and dependable. By connecting ESG systems with financial and operational platforms, manufacturers can simplify workflows. This can be achieved using data engineering solutions designed for compatibility and scalability.

Another critical step is aligning ESG metrics with recognized industry standards. This ensures consistency in how data is analyzed and reported. For added precision and efficiency, manufacturers can collaborate with experts in data integration and strategic advisory services. These partnerships can help companies meet compliance requirements while working toward their sustainability objectives.

What are the advantages of automating ESG data collection and reporting in manufacturing, and how does it support compliance and performance monitoring?

Automating ESG data collection and reporting brings a range of benefits for manufacturers. For starters, it boosts accuracy and efficiency by cutting down on manual errors and streamlining data entry. Instead of spending hours managing spreadsheets, teams can dedicate their energy to analyzing meaningful insights.

It also ensures stronger compliance by maintaining consistent tracking and reporting that aligns with regulatory requirements. Plus, audits become far easier with a well-organized, transparent record of ESG metrics readily available.

On top of that, automation enhances performance monitoring by providing real-time updates and actionable insights. This enables manufacturers to spot trends, establish measurable goals, and make smarter decisions to stay on track with their sustainability targets.

What are the best ways for manufacturers to align their ESG metrics with frameworks like SASB, GRI, and TCFD to meet both regulatory and investor needs?

To align ESG metrics with frameworks such as SASB, GRI, and TCFD, manufacturers should begin by pinpointing the reporting standards that matter most to their industry and stakeholders. By understanding the unique requirements of these frameworks, businesses can ensure their data collection meets both regulatory demands and investor expectations.

The next step is to put in place reliable tools and processes to gather ESG data accurately and consistently across operations. Using advanced data engineering solutions can simplify this process, making data collection more efficient and improving the quality of reporting. It’s also essential for manufacturers to routinely review and adjust their ESG strategies to keep pace with changing regulations and market dynamics.

For those aiming to expand their ESG initiatives, teaming up with experienced advisory firms like Phoenix Strategy Group can be a game-changer. With their expertise in financial and strategic planning, combined with data-driven insights, they help businesses tackle the complexities of ESG reporting without compromising operational performance.

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