The past decade has seen an immense surge in demand for ecommerce solutions, consequently causing an increase in traffic congestion and consequential carbon emissions.


Copyright: – “How Machine Learning Can Help Reduce Waste And Increase Sustainability In Logistics”


The World Economic Forum predicts that without addressing this problem at a large scale, the ever-increasing reliance on commercial vehicles for deliveries is set to increase congestion and emissions by over 30% globally by 2030.

Electric vehicles are one way to reduce this impact, but they are expensive and require significant infrastructure investments. The global push for EVs should be supplemented by advanced technologies like machine learning in the logistics and automotive industries to achieve rather ambitious sustainability goals while maintaining competitive pricing.

According to a report by PWC, using AI for environmental applications has the potential to boost global GDP by 3.1 – 4.4% while also reducing global greenhouse gas emissions by around 1.5 – 4.0%.

The call for ML

Essentially, the supply chain is an intricate network of processes that profoundly impact each other. Therefore, minor improvements in how supply chain participants store and package goods, manage inventory, define routes, schedule staff, and perform a myriad of other tasks have a positive compound effect on both the system performance and the environment.

Identifying these improvement opportunities in real-time is nearly impossible when relying on traditional logistics methods for analysis and decision-making. In this article, we will discuss how ML can help reduce waste and increase sustainability in logistics.

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Route optimization

Detecting the most optimal delivery route significantly reduces fuel consumption, consequently decreasing carbon footprint. According to the United States Environmental Protection Agency, transportation accounted for 28% of greenhouse gas emissions in 2021.

For years, supply chain managers have relied on traditional tools and methods, many of which are still driven by intuition and industry experience.

Using ML and AI, however, companies can now quickly compute optimal routes in real time. By leveraging a combination of machine learning and deep learning models, logistic companies can analyze large datasets of transportation data to identify the shortest routes with the lowest environmental impact.

UPS, one of the global leaders in supply chain management, implemented the AI-powered proprietary route optimization system called ORION and managed to decrease UPS’s carbon footprint by 100,000 metric tonnes per year, equivalent to driving 223 million miles in an average car.[…]

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