Turning Factory Data into Carbon Performance Insight

Across our client’s global production facilities, the operational maturity at the site level is impressive, and decarbonization is treated as a core operational priority.

Real-time meters feed intelligent building systems. Control room dashboards track load and performance. Teams have visibility into consumption and efficiency. At the local level, factories run smoothly.

But zoom out across five global production sites, and the challenge becomes one of scale. Before this initiative, each facility operated largely as its own island.

Energy and emissions data resided in disconnected local systems. There was no shared data model, no standardized collection framework, and no consistent way to ask a question across facilities and receive a comparable answer.

At the same time, the company’s climate ambition was bold with goals of reducing their carbon footprint 37% by 2032 and reaching net-zero emissions by 2050.

Achieving those targets would require enterprise-wide carbon transparency across operations, the supply chain, and the product lifecycle. This case study details how SoftServe helped a global toy manufacturer design and build a carbon accounting platform to make that possible.

The climate strategy team recognized that they needed to assess current capabilities and design a scalable path forward. They looked for a partner who could combine advisory depth, technical expertise, and implementation experience, so they selected SoftServe to lead the transformation.

SoftServe led a structured initiative to assess the organization’s carbon data landscape, design a scalable data foundation, and define a phased roadmap that would guide the client’s future implementation efforts.

Delivering the vision of a carbon accounting platform required getting the data fundamentals right from the outset:

Aligning the Enterprise

During three intensive on-site workshops, SoftServe brought together cross-functional leaders from sustainability, operations, engineering, data, and digital to assess the current state and define the blueprint for enterprise-wide carbon accounting. The objectives were to:

3-Day Workshop at a Glance

The Structural Gaps Exposed

Key Structural Gaps

While the long-term vision extended across the enterprise, factories were selected as the proving ground.

The workshops made it clear that factory operations offered the right starting point. They combined measurable emissions, operational control, and existing digital infrastructure, making them ideal for validating the broader carbon intelligence architecture.

The ultimate vision for factories extends beyond energy monitoring to include:

• Digital twins to monitor performance and simulate operational changes
• Operational analytics to identify emissions hotspots
• Forecasting models for energy consumption
• Energy intensity benchmarking, such as MWh per tonne of product
• Scenario analysis to answer practical questions like, “What would it take to reduce emissions at this site by X percent?”
• Modeling the cost and energy impact of reduction initiatives

The insights gathered during the on-site workshops were synthesized into a clear picture of how carbon data flowed across the organization, where it worked, where it broke down, and where it needed to evolve.

That work resulted in three foundational outputs.

1. Initial Framing of the Engagement. A clear articulation of objectives, scope, and success criteria. This document aligned every team on what the program was trying to achieve and how success would be measured.
2. As-Is Technology Landscape. A mapped view of systems, data flows, ownership structures, and gaps across every facility and functional team. For the first time, the organization had a single document that showed a full picture of how carbon data moved across the enterprise.
3. Proposed Carbon Accounting Delivery Model. A structured roadmap outlining how GHG inventory, analytics, governance, and tooling should evolve through to full enterprise scale.

• Aggregate carbon data across domains and value chain stages
• Automate emissions calculations at the most granular level available
• Translate long-term climate targets into short-term carbon budgets
• Track reduction levers across business units
• Provide transparency from enterprise totals down to activity-level emissions

• Standardized carbon data layers from enterprise totals to activity-level utility data
• A centralized emission factor catalog
• Automated emissions calculations aligned with the GHG Protocol
• Defined governance around data ownership and updates
• Integration with factory meters, operational systems, supplier inputs, and group platforms

• Emissions trajectory tracking against targets
• Carbon budgeting visibility across domains
• Hotspot identification at factory, process, and activity levels
• Forecasting and scenario modeling
• Automated footprint reporting

• Energy productivity metrics such as energy per tonne
• Cost optimization decisions
• Renewable integration strategies
• Supplier emissions benchmarking
• Evaluation of reduction initiatives

• Centralized dashboard consolidating emissions data across all facilities
• Automated emissions factors database replacing manual inputs
• Consistent GHG reporting aligned to SBTi and GHG Protocol standards

• Identify high-emissions areas by process and activity
• Model what-if scenarios for site-level reduction targets
• Recommend energy reduction projects by value chain step

• Carbon budget tracking against emission reduction goals at facility level
• Aggregated energy savings reporting across reduction initiatives
• Historical benchmarking and trend analysis by site and process area

• Benchmark facilities by energy intensity and spend
• Assess suppliers based on emissions reduction initiatives
• Connect procurement decisions to carbon outcomes

• Dynamic energy consumption forecasting by facility and functional area
• Scenario modeling to evaluate reduction options and trade-offs
• Carbon-aware recommendations tied to productivity and cost outcomes

1. Foundation
   • Understanding the full business and technical landscape.
   • Defining the solution architecture.
   • Aligning stakeholders around scope and ownership.
2. Data Model
   • Mapping carbon data at the most granular level possible.
   • Designing and beginning construction of the initial Carbon Data Model.
3. Insights
   • Identifying and prioritizing the first management and factory reports.
   • Designing and validating dashboards with end users before development began.

• The architecture vision was validated
• Data contracts were reviewed
• The first reports were conceptually designed with their future users

• Carbon data is being mapped end-to-end, bringing greater clarity to ownership and data flows.
• Standardized KPIs and harmonized data models are replacing fragmented reporting approaches.
• Manual compilation is declining as emissions calculations become more consistent and repeatable.
• Factory leaders have improved visibility into energy use and emissions hotspots.
• Sustainability teams have a clearer view of carbon budgets and progress against enterprise targets.