Using AI for Carbon Footprint Tracking: How Supply Chains Can Meet Sustainability Goals

February 19, 2025

Understanding Carbon Footprint in Supply Chains

What is carbon footprint tracking in the context of supply chains?

Carbon footprint tracking in supply chains refers to measuring the total greenhouse gas emissions produced by various activities within the supply chain, from sourcing raw materials to delivering the final product. This includes emissions from transportation, production, and storage. Tracking these emissions is crucial for businesses aiming to reduce their environmental impact and meet sustainability goals.

Key Factors Contributing to Carbon Emissions in Logistics and Supply Chains

Transportation and Freight Movement: Transportation is one of the largest contributors to carbon emissions in supply chains. Emissions arise from the fuel consumption of trucks, ships, and aeroplanes used to move goods between suppliers, manufacturers, and customers. The longer the distances and the more frequent the shipments, the higher the emissions.
Energy Use in Warehouses: Warehouses require significant energy for lighting, heating, cooling, and equipment operation. If the energy comes from non-renewable sources, this adds to the overall carbon footprint. Inefficient energy use and poor building insulation further increase emissions.
Production and Manufacturing Processes: The manufacturing phase in a supply chain can generate substantial emissions, especially if fossil fuels are used for energy or if the production methods are not optimized for energy efficiency. The energy required to process raw materials also contributes significantly to emissions.
Inefficient Supply Chain Routing and Logistics: Inefficient logistics management can result in unnecessary fuel consumption and longer transport times. For instance, underutilized transport vehicles, poorly planned routes, and delayed shipments can lead to increased emissions.
Packaging Materials: The production and disposal of packaging materials, especially plastic, contribute to carbon emissions. Packaging that is not recyclable or made from sustainable materials increases the environmental impact of supply chains.

Challenges of Manually Tracking Carbon Emissions and the Need for AI-based Solutions

Tracking carbon emissions in supply chains is critical for organizations aiming to meet sustainability goals and reduce their environmental impact. However, manual tracking often presents significant challenges, including time-consuming processes, data inconsistencies, and errors. With the growing complexity of global supply chains, relying on traditional methods can hinder the ability to make informed, timely decisions. This is where AI-based solutions come in, offering a more efficient, accurate, and scalable approach to carbon footprint management.

Here’s a comparison of the challenges faced with manual tracking versus the advantages of implementing AI-based solutions:

Challenge	Manually Tracking Carbon Emissions	AI-based Solutions
Data Collection	Time-consuming, often requiring multiple sources and inconsistent data.	Automates real-time data collection from multiple sources, ensuring accuracy and consistency.
Real-time Tracking	Difficult to track emissions in real-time, leading to delays in identifying issues.	Provides real-time tracking and monitoring, allowing for immediate adjustments.
Data Accuracy	Prone to human error, leading to inaccurate reporting and analysis.	AI algorithms ensure precise calculations, minimizing errors in emissions data.
Resource Intensive	Requires significant manpower and time to gather and analyze data.	Reduces the need for manual resources by automating data analysis and reporting.
Scalability	Difficult to scale as supply chains grow, leading to gaps in emissions data.	Easily scalable to handle larger, more complex supply chains, ensuring continuous tracking.
Decision-Making Speed	Slower decision-making due to manual data analysis and reporting.	Speeds up decision-making by providing actionable insights quickly and automatically.

AI’s Role in Carbon Footprint Tracking

As supply chains become more complex, tracking and reducing carbon emissions has become an increasingly important focus for businesses. AI plays a crucial role in streamlining carbon footprint tracking, offering powerful tools that provide accurate data, real-time insights, and predictive capabilities. These AI-driven solutions not only improve the precision of emissions tracking but also empower companies to identify inefficiencies, make data-backed decisions, and optimize their operations toward sustainability.

AI Enhanced Emission Reduction Funnel

AI-driven Data Collection and Analysis for Accurate Tracking: AI-powered systems automate the collection and analysis of emissions data, eliminating human error and ensuring high accuracy. Machine learning models process vast amounts of information from various points in the supply chain, ensuring businesses have precise data to track their carbon footprint.
Real-time Monitoring and Forecasting of Carbon Emissions: With AI, companies can monitor their carbon emissions in real time, allowing them to adjust their operations immediately if emissions exceed set targets. AI also forecasts future emissions, enabling businesses to plan more effectively and meet sustainability goals without unexpected surprises.
AI Algorithms to Identify Emission Hotspots and Inefficiencies: AI’s ability to process large volumes of data allows it to pinpoint emission hotspots and inefficiencies in the supply chain. By identifying these areas, companies can focus their efforts on making targeted improvements that drive significant reductions in emissions.
Automating Data Integration from Various Sources for a Unified Carbon Footprint View: AI integrates data from diverse sources—like suppliers, transport networks, and energy consumption—into a single view. This unified approach ensures that businesses can analyze their carbon footprint holistically, enabling more informed decision-making and streamlined operations.
AI for Predictive Maintenance to Reduce Emissions: By using predictive analytics, AI can determine when machinery is likely to require maintenance, helping companies avoid inefficient operations that lead to unnecessary energy consumption. Proactive maintenance ensures that equipment runs efficiently, reducing the likelihood of higher emissions due to malfunctioning or overused machinery.
AI-powered Supply Chain Optimization for Lower Carbon Footprint: AI also optimizes various supply chain activities, including route planning and inventory management, to minimize emissions. By considering factors such as weather, real-time traffic conditions, and demand fluctuations, AI suggests the most energy-efficient methods, ultimately lowering the carbon footprint across the entire supply chain.

AI Tools for Carbon Footprint Optimization

AI is reshaping the way businesses optimize their supply chain operations to reduce their environmental impact. From reducing emissions through predictive route optimization to enhancing warehouse energy efficiency, AI tools are making carbon footprint tracking and reduction more effective. Several AI-driven approaches are already providing tangible results across industries, driving both environmental sustainability and operational efficiency.

AI-powered predictive models for optimizing routes, reducing fuel consumption: AI algorithms process large volumes of data to determine the most efficient delivery routes based on real-time variables such as weather conditions, traffic patterns, and delivery constraints. By continuously analyzing and adjusting routes, AI minimizes fuel consumption, reduces idle time, and lowers carbon emissions. These models enhance fleet efficiency, ensuring that logistics operations align with sustainability goals while maintaining timely deliveries.

Machine learning models to predict and reduce waste generation in supply chains: Machine learning analyzes patterns in waste production across supply chain processes, identifying inefficiencies that contribute to overproduction, excess inventory, or packaging waste. These models can forecast demand more accurately, optimize material usage, and recommend sustainable alternatives to minimize waste. By providing actionable insights, AI helps businesses streamline operations, reduce environmental impact, and support more responsible sourcing and production practices.

Figure-2-Monitor Ware house Activity

Warehouse automation for reducing energy use: In warehouses, AI can regulate energy consumption by automating lighting, heating, and cooling based on real-time demand. This reduces unnecessary energy use during periods of low activity. For instance, predictive AI models can anticipate the flow of inventory, adjusting warehouse systems accordingly to cut down on wasteful energy consumption. The integration of machine learning with smart devices in warehouses has the potential to lower the overall carbon footprint, making supply chains more sustainable.
AI-based decision support systems to recommend sustainable alternatives and process improvements: AI-based decision support systems assist businesses in evaluating and recommending process changes to improve sustainability. These systems can consider various factors, such as energy use, waste production, and resource consumption, to suggest more sustainable alternatives. For instance, machine learning models can recommend energy-efficient production processes or eco-friendly suppliers based on carbon footprint data.

Real-World Applications and Case Studies

Omdena has been at the forefront of using AI to tackle some of the most pressing challenges in supply chain sustainability. Through innovative solutions, we have helped businesses not only meet but exceed their carbon footprint reduction goals. Below are examples of our impactful work:

Omdena Case Study 1: AI-Powered Delivery Route Optimization in LATAM

AI-powered delivery route optimization in LATAM

Problem: Major Latin American cities like Bogotá, Lima, and Mexico City suffer from severe traffic congestion, leading to inefficient last-mile delivery routes. This results in higher fuel consumption, delayed deliveries, and significant carbon emissions, making optimization essential for logistics companies.
Solution: Omdena partnered with Carryt, a logistics company, to develop an AI-powered route optimization system. We integrated Google OR-Tools for vehicle routing, combined with real-time geospatial data from OpenStreetMap and advanced algorithms for shortest path finding. The system was designed to optimize over 1 million deliveries per month, reducing inefficiencies in delivery planning.
Impact: The solution reduced CO2 emissions, minimized fuel consumption, and improved fleet efficiency. Carryt saw a significant reduction in delivery times and lower operational costs, leading to more sustainable delivery practices across the region. Omdena’s expertise in combining AI with real-time data helped streamline logistics and drive measurable environmental benefits.

Omdena Case Study 2: AI-Powered ESG Monitoring

AI-powered ESG monitoring

Problem: Traditional ESG assessments mainly focus on large corporations, leaving small and medium-sized enterprises (SMEs) underserved. Additionally, greenwashing—misleading sustainability claims—reduced trust in sustainable trade practices, hindering efforts to achieve real sustainability goals.
Solution: Omdena developed an AI-powered ESG monitoring system to address this challenge. Using machine learning, we assigned ESG scores to over 100 companies and implemented Natural Language Processing (NLP) for sentiment analysis. This system achieved 85% accuracy in detecting greenwashing and processed over 10,000 sustainability reports.
Impact: The system significantly enhanced ESG transparency, improving corporate accountability and reducing instances of greenwashing. It empowered both large and small businesses to gain a clearer picture of their sustainability efforts, promoting trust in sustainable trade practices. Our AI-driven solution strengthened ESG monitoring in global trade, supporting a more ethical and sustainable supply chain ecosystem.

Omdena Case Study 3: AI-Powered Sustainability Benchmarking using ESG Data

AI-powered sustainability benchmarking using ESG data

Problem: Companies often struggle to extract meaningful insights from ESG reports and lack a standardized system for benchmarking their sustainability performance against industry peers. This makes it difficult to identify areas for improvement and track progress towards global sustainability goals.
Solution: Omdena collaborated with SustainLab to develop an AI-powered ESG benchmarking system. We utilized Natural Language Processing (NLP) to analyze over 10,000 ESG reports and integrated metadata (country, industry, year) to create a platform that allows companies to compare their sustainability performance globally. The system includes a dashboard for easy benchmarking against competitors.
Impact: The platform expanded the ESG database from 500 to 40,000 reports, automating ESG data collection and reducing manual efforts. It enabled companies to gain valuable insights into their sustainability practices, fostered transparency, and encouraged bold sustainability initiatives. Omdena’s AI-driven benchmarking system has influenced industry standards and helped companies make data-driven decisions to improve their environmental impact.

Overcoming Barriers to AI Adoption in Supply Chains

Implementing AI in supply chains, particularly for sustainability goals, offers tremendous potential but also presents several challenges. Many companies face hurdles such as resource constraints, technological barriers, and resistance to change, which can hinder the successful adoption of AI. Addressing these challenges effectively is crucial for ensuring that AI solutions deliver on their promise of optimizing supply chains and minimizing environmental impact.

Common Challenges Faced by Companies in Implementing AI for Sustainability

High Initial Investment and Resource Requirements: Developing and implementing AI solutions can require significant upfront investment in technology and skilled personnel. Smaller businesses, in particular, may struggle to allocate resources for AI adoption, hindering their ability to invest in AI-driven sustainability initiatives.
Data Quality and Integration Issues: AI relies on large amounts of high-quality data to make accurate predictions and optimize operations. However, many companies struggle with fragmented, inconsistent, or incomplete data across their supply chain. Poor data quality can undermine the effectiveness of AI solutions and prevent companies from achieving reliable sustainability outcomes.
Lack of Skilled Talent: The technical expertise required to implement and maintain AI solutions is often in short supply. A lack of data scientists, AI specialists, and supply chain professionals who are well-versed in AI technologies can delay or complicate AI adoption, leaving businesses unable to capitalize on its potential.
Resistance to Change and Organizational Challenges: Employees and leadership may be hesitant to adopt AI due to fears of job displacement, lack of understanding, or perceived complexity. Organizational resistance to change is a common barrier that can slow the integration of AI into supply chain processes.

Solutions to Mitigate Risks and Ensure Successful AI Adoption

Gradual Implementation and Pilot Programs: Instead of a full-scale AI rollout, companies can start with smaller pilot programs that test AI solutions on specific supply chain tasks. This approach allows businesses to measure the effectiveness of AI in a controlled environment before committing to a larger investment. Pilots also provide an opportunity to refine processes and ensure the AI systems are working as intended.
Investing in Data Management and Integration: Ensuring that data across the supply chain is consistent, accurate, and integrated is essential for AI success. Businesses can invest in data management systems that clean, consolidate, and standardize data, providing AI models with the reliable information they need to function effectively. Data integration platforms can also ensure that AI has access to real-time data from various sources across the supply chain.
Training and Upskilling Employees: To address the talent gap, companies can invest in training and upskilling existing employees to work with AI technologies. Offering AI-related workshops, courses, and certifications can help build internal expertise, enabling businesses to adopt AI more smoothly and build a workforce capable of supporting ongoing AI initiatives.
Clear Communication and Change Management: Overcoming resistance to AI adoption requires strong leadership and clear communication. It’s important for companies to explain the benefits of AI, such as increased efficiency and sustainability, to employees and leadership alike. Change management strategies should emphasize how AI can complement human work rather than replace it and how it can contribute to the company’s long-term success in achieving sustainability goals.

By proactively addressing these challenges and investing in the right resources, companies can successfully navigate the barriers to AI adoption and unlock the full potential of AI for sustainable supply chain management.