AI Insights

AI-Powered Solution to Reduce Carbon Footprint in Supply Chains

November 9, 2023


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In this article, we explore how a global supply chain management company partnered with Omdena to create an AI-powered solution to reduce carbon footprint in supply chains, leading to a 10% reduction in emissions and $5M in annual savings.

Challenge 

A global supply chain management company was facing the challenge of reducing its carbon footprint. The company had a complex supply chain with operations in over 100 countries. This made it difficult to track and measure the company’s carbon emissions, and to identify opportunities to reduce them.

Solution

The company partnered with Omdena to develop an AI-powered solution to reduce its carbon footprint. Omdena’s team of data scientists and machine learning engineers developed a solution that uses machine learning to analyze the company’s supply chain data and identify opportunities to reduce carbon emissions. The solution also develops tailored recommendations for the company’s supply chain managers on how to implement these opportunities.

Machine Learning Models Used:

  • Random Forests: This ensemble learning method is utilized for both classification and regression tasks. It works by constructing multiple decision trees during training time and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random Forests are particularly useful for managing the complex, non-linear relationships often found in supply chain data, allowing for a robust analysis of factors influencing carbon emissions.
  • Gradient Boosting Machines (GBMs): GBMs are another ensemble technique that builds trees one at a time, where each new tree helps to correct errors made by previously trained trees. GBMs have been applied to predict more accurate carbon emission levels based on various supply chain activities, such as transportation methods, fuel types, and operational efficiencies. They are known for their predictive accuracy, especially in complex datasets.
  • Clustering Algorithms (e.g., K-means): Clustering is used to find patterns or groups within the supply chain data that share similar characteristics related to carbon emissions. This unsupervised learning method helps in identifying high-emission hotspots within the supply chain, enabling targeted interventions.
  • Neural Networks: Deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are implemented for more complex analyses, such as forecasting future emission trends based on historical data and external factors like weather patterns. These networks excel in capturing temporal dependencies and spatial patterns within data.

Development of Tailored Recommendations

The AI solution doesn’t just stop at identifying opportunities; it goes further by developing tailored recommendations for supply chain managers on how to implement these opportunities efficiently. Here’s how it’s achieved:

  • Feature Importance Analysis: By utilizing the feature importance generated from models like Random Forests and GBMs, the solution identifies which factors most significantly impact carbon emissions within the supply chain. This insight allows for prioritizing recommendations based on their potential impact.
  • Scenario Simulation: Using the predictive models, the system can simulate various scenarios to assess the potential impact of different interventions. For example, changing transportation modes from air to sea or optimizing routes for fuel efficiency. These simulations help in crafting specific recommendations that can lead to substantial reductions in emissions.
  • Optimization Algorithms: Linear programming and other optimization algorithms are applied to find the most efficient ways to implement changes within the supply chain operations that can lead to reduced emissions. This includes optimizing logistics routes, inventory levels, and production schedules.
  • User-Friendly Interface: To ensure these recommendations are accessible and actionable, they are presented through an easy-to-use interface that allows supply chain managers of varying skill levels to understand and implement them effectively. The interface includes visualizations of potential impacts and step-by-step guides on executing recommendations.

By combining these sophisticated machine learning models with a user-centric approach for developing recommendations, the solution empowers supply chain managers to make data-driven decisions that significantly reduce carbon emissions while maintaining operational efficiency.

Results 

The AI-powered solution has been very successful in helping the company to reduce its carbon footprint. Since implementing the solution, the company has reduced its carbon emissions by 10% and saved $5 million in annual costs.

AI Solution to Reduce Carbon Footprint in Supply Chain Management

Benefits

The AI-powered solution to reduce carbon footprint in supply chains has provided a wide array of benefits to the company, which extend beyond immediate carbon emission reduction and cost savings. These benefits include:

  • Reduced Carbon Emissions: By identifying inefficiencies within the supply chain and opportunities for optimization, the solution directly contributes to a significant reduction in overall carbon emissions, aligning with global sustainability goals.
  • Improved Fuel Efficiency: The solution offers insights into logistics and transportation operations, enabling the company to implement strategies that reduce fuel consumption, such as route optimization and transitioning to fuel-efficient vehicles.
  • Reduced Waste: By analyzing production and distribution processes, the AI solution helps in minimizing waste production at various stages of the supply chain, including excess inventory, packaging materials, and product spoilage.
  • Increased Use of Renewable Energy: The system identifies opportunities for integrating renewable energy sources into the supply chain operations, such as solar-powered warehouses or green logistics solutions, further reducing the carbon footprint.
  • Reduced Costs: Aside from the direct savings from lower fuel consumption and waste reduction, the company benefits from decreased operational costs associated with energy use, material sourcing, and waste management.
  • Enhanced Supply Chain Visibility: The AI solution provides detailed insights into every aspect of the supply chain, offering unprecedented visibility that helps in identifying bottlenecks and inefficiencies.
  • Data-Driven Decision Making: With comprehensive analytics at their disposal, supply chain managers can make informed decisions that are backed by data, leading to more strategic and effective management practices.
  • Compliance and Reporting: The solution aids in tracking regulatory compliance related to environmental standards and simplifies the reporting process for carbon emissions and sustainability efforts, essential for transparency and accountability.
  • Increased Customer Satisfaction: By adopting greener practices and demonstrating a commitment to sustainability, the company can enhance its brand image and appeal to environmentally conscious consumers.
  • Scalability and Flexibility: The AI-powered solution is designed to scale with the company’s operations and adapt to changes in the supply chain, ensuring long-term sustainability and efficiency improvements.

These benefits collectively contribute not only to the environmental objectives of the company but also enhance operational efficiency, cost-effectiveness, and market competitiveness, underscoring the transformative potential of AI in supply chain management

Real World Project: Optimizing Delivery Routes in LATAM Using AI Planning 

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In this project, Omdena partnered with Carryt, a technology company focused on improving logistics through artificial intelligence and route planning, to tackle urban congestion and enhance last-mile logistics efficiency. 

Through an 8-week challenge, 50 AI engineers collaborated to develop a route optimization tool leveraging Google’s Operations Research (OR) Tools and Open Street Map. This tool enables Carryt to visualize optimal delivery routes in Bogota, with potential applicability in other congested cities worldwide. 

The project aimed to optimize the deployment of vehicles and drivers, thus improving customer service levels, reducing carbon emissions by minimizing unnecessary travel, increasing drivers’ well-being, and alleviating the overall impact of the logistics industry on urban congestion. 

This initiative reflects an effort to address the significant challenges posed by rising e-commerce demand and urban population growth, which contribute to increased carbon footprint and congestion, especially in cities like Bogota, Lima, Mexico City, and Rio de Janeiro.

Read more information about this project here!

Conclusion

The AI-powered solution developed by Omdena has helped the global supply chain management company to significantly reduce its carbon footprint. The solution is easy to use and provides the company with the information and tools it needs to make informed decisions about how to reduce its carbon emissions.

Lessons Learned

There are a few key lessons that can be learned from this case study:

  • AI-powered solutions can be very effective in helping supply chains reduce their carbon footprint.
  • It is important to collect and prepare a large and diverse dataset of supply chain data in order to train accurate and effective machine learning models.
  • It is also important to develop models that are interpretable, so that supply chain managers can understand how they work and trust their recommendations.
  • The AI-powered solution should be easy to use for supply chain managers with a variety of skill levels.

Overall, AI-powered solutions have the potential to revolutionize the way that supply chains manage their carbon footprint. By providing supply chain managers with the information and tools they need to reduce their carbon emissions, AI-powered solutions can help businesses to become more sustainable and profitable.

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