Decarbonising the Supply Chain: Reducing Scope 3 Emissions Through Predictive Planning

Decarbonising the supply chain has become a strategic priority for companies seeking to combine profitability, resilience and sustainability. Regulatory pressure, consumer expectations and corporate net-zero commitments demand that businesses look beyond their direct emissions. The real challenge lies in Scope 3 – the indirect emissions generated by suppliers, transport and distribution.

This article explores how predictive planning and AI-based technologies are transforming carbon management in operations, from measurement to decision-making.

The Scope 3 Challenge in Supply Chain Decarbonisation

The environmental impact of a supply chain extends far beyond emissions produced in facilities or company-owned vehicles. Most CO₂ comes from external activities such as raw materials, transport, packaging and even the use of the final product.

While many companies have successfully reduced their direct emissions, Scope 3 remains the blind spot of corporate sustainability. Measuring and reducing it requires accurate data, collaboration with partners and tools capable of analysing thousands of variables in real time.

Understanding Scopes 1, 2 and 3 – and Why the Third Is the Most Critical

According to the Greenhouse Gas Protocol (GHG Protocol), emissions are classified into three categories:

• Scope 1: Direct emissions from sources controlled by the company, such as vehicles, boilers or machinery.
• Scope 2: Indirect emissions resulting from the consumption of purchased electricity or heat.
• Scope 3: All indirect emissions generated throughout the product lifecycle, from suppliers to the end consumer.0

Scope 3 is the most complex to manage because it involves the entire value network. Every supplier, logistics link and operational decision contributes to the total footprint. Without visibility and integrated data, any reduction strategy will remain incomplete.

Why 70–80% of Total Emissions Come from Suppliers and Transport

Industry studies estimate that between 70 and 80% of corporate emissions fall outside direct company control. International transport, component manufacturing, packaging and storage are the main sources of CO₂ in the supply chain.

That’s why advanced ESG commitments no longer focus solely on internal energy efficiency but also on sustainable sourcing and logistics planning. Reducing transport footprints, choosing certified green suppliers and aligning production with real demand are key steps towards comprehensive decarbonisation.

How Digitalisation and Traceability Are Changing the Way Climate Impact Is Measured

For years, Scope 3 emissions were estimated using averages or generic factors. Today, digitalisation makes it possible to measure the real impact of each process with accuracy.

Advanced SaaS platforms collect real-time information from ERP, TMS and WMS systems, combining it with data on energy consumption, logistics routes and suppliers.

Digital traceability enables companies to identify the exact origin of each batch, calculate its associated carbon footprint and make operational decisions based on complete visibility. This evolution marks the shift from annual reporting to dynamic, predictive environmental management.

Measuring and Controlling Emissions – From Estimates to Real Data

Reducing emissions starts with reliable measurement. Without consistent, traceable data, it’s impossible to set realistic goals or evaluate progress. The challenge now is to move from estimation to precise quantification.

Leading sustainability-focused companies are adopting AI-driven tools and predictive models that integrate operational, financial and environmental data into a single platform. The result is decision-making based on evidence, not assumptions.

The Role of the GHG Protocol in Standardising Carbon Footprint Calculation

The GHG Protocol is the global standard for measuring and reporting emissions. It provides a shared framework that allows comparison across companies, sectors and regions.

Its adoption is essential to avoid duplication and ensure that every link in the chain calculates emissions under the same criteria.

With this framework, companies can structure their emissions data by activity, product or supplier, making it easier to prioritise actions and integrate targets into planning.

Integrating Operational Data with ESG Metrics in SaaS Platforms

SaaS systems specialising in supply chain planning are evolving to embed ESG indicators directly into decision models.

This means that variables such as energy consumption, transport mode or material sourcing can influence the master plan rather than being treated as separate reports.

When sustainability becomes part of the optimisation model, every logistics or production decision becomes an opportunity to cut emissions. This approach enables businesses to balance financial and environmental goals within a single planning environment.

Ensuring Data Quality, Granularity and Traceability for Reliable Decisions

Data quality is the foundation of any decarbonisation strategy. Without granularity and traceability, predictive models lose accuracy.

Best practices include using primary data (actual consumption and transport data) instead of averages and automating data capture to minimise human error.

Data traceability, knowing where it comes from and how it’s processed, ensures transparency for auditors and regulators. In an era of increasing scrutiny of ESG reporting, having a robust data foundation is as important as reducing emissions themselves.

Practical Strategies to Reduce Scope 3 Emissions

Reducing Scope 3 emissions cannot be achieved through reporting or pledges alone. It requires concrete, measurable and coordinated actions. Predictive planning helps identify high-impact areas and propose operational strategies based on real data.

Here are three of the most effective approaches:

Route Optimisation, Transport Efficiency and Sustainable Packaging

Transport is one of the main sources of CO₂. AI-driven planning can redesign routes, consolidate loads and select more efficient transport modes.

Scenario simulation makes it possible to compare the impact of each option (road, rail, sea or air) to balance cost, time and carbon footprint.

Packaging also plays a role. Using recyclable materials or modular formats reduces both logistics volume and the emissions associated with transport.

Collaborative Planning with Low-Impact Suppliers

A collaborative approach is key to addressing the core of Scope 3. Integrating suppliers into planning allows companies to share forecasts, adjust deliveries and align ESG goals.

Thanks to SaaS systems, data on consumption, transport and production are updated in real time, creating a synchronised and efficient network.

This collaborative model not only reduces emissions but also enhances resilience and visibility across the supply chain, supporting joint decision-making when disruptions occur.

Reducing Inventory and Waste Through Advanced Forecasting

Excess inventory leads to unnecessary storage, higher energy consumption and increased obsolescence risk.

Predictive forecasting uses machine learning to align demand with high accuracy, avoiding both overproduction and stockouts.

Reducing waste improves both environmental sustainability and financial performance: less inventory means less energy, fewer shipments and a smaller indirect emissions footprint.

AI and Predictive Planning in Service of Sustainability

Artificial intelligence is no longer just for demand forecasting. It’s now a key driver of sustainable optimisation.

Through machine learning algorithms and advanced simulations, businesses can anticipate the environmental impact of their decisions and select the most efficient option in terms of both emissions and cost.

How Artificial Intelligence Simulates Carbon Reduction Scenarios

AI-based models can create “what if” scenarios that show how each decision affects the total footprint.

For example, a company can compare the impact of increasing local production versus importing from another country, or assess the effect of switching transport modes on a specific route.

These simulations – once impossible using spreadsheets – provide clear insight into both environmental and economic returns.

Digital Twins and Environmental Impact Modelling in Planning

Digital twins replicate the entire supply chain virtually – plants, warehouses, suppliers, routes and customers.

By integrating ESG variables, the digital twin can calculate the environmental impact of each movement in real time, adjusting operational decisions to meet emission reduction targets.

This approach combines agility and precision, allowing companies to test multiple scenarios without disrupting operations while optimising cost, service and sustainability.

Balancing Cost, Service and Sustainability – The New Green S&OP Approach

The Sales & Operations Planning (S&OP) process is evolving into a broader, greener model.

Here, decisions are no longer based solely on profitability and service but also on environmental impact.

Integrating carbon footprint metrics into the planning process ensures that each monthly master plan review contributes to decarbonisation goals. The outcome is a more balanced, efficient and ESG-aligned supply chain.

Embedding ESG Goals into Operational Decision-Making

Integrating sustainability goals into day-to-day operations is what turns strategy into tangible results.

It’s no longer about having an ESG plan, but about ensuring that it informs every logistical, production and commercial decision.

Companies that achieve this integration gain visibility, control and agility, cutting emissions without compromising competitiveness.

Embedding Sustainable KPIs into S&OP Processes

ESG KPIs should form part of the operational dashboard. Metrics such as emissions per tonne transported, energy per unit produced or percentage of sustainable suppliers are as critical as traditional financial indicators.

Incorporating them into S&OP enables continuous monitoring of progress toward net-zero goals, helping to identify deviations and prioritise corrective actions.

Assessing Trade-Offs Between Economic and Environmental Efficiency

Every decision involves a balance between cost, service and sustainability. The key is to quantify these trade-offs to make informed choices.

A shorter route might be cheaper but more polluting, while local production could cut emissions but raise unit costs.

Predictive planning enables companies to assess these scenarios in real time, assigning weights to each variable according to strategic priorities.

Turning Sustainability into a Measurable Competitive Advantage

Sustainability is no longer an additional cost but a differentiator. Companies that integrate ESG metrics into their planning strengthen their reputation, reduce regulatory risks and gain preference among clients and partners.

The ability to demonstrate results – such as a 15% cut in logistics emissions or a 10% reduction in energy use per unit – turns sustainability into a tangible, verifiable business advantage.

Towards a Data-Driven Net-Zero Supply Chain

Decarbonising the supply chain requires more than promises. It demands planning, data and technology.

Scope 3 represents today’s biggest challenge but also the greatest opportunity to transform operations into real levers for change. The combination of AI, predictive planning and integrated ESG metrics enables companies to move from diagnosis to action, from estimation to evidence.

In this new paradigm, businesses that turn sustainability into a measurable operational process will lead the way toward a data-driven, net-zero, efficient and resilient supply chain.

At Imperia, we help our clients reduce their carbon footprint by optimising every stage of their supply chain. If you’re ready to improve your company’s sustainability, request a free demo with our experts.