The past decade has seen an immense surge in demand for ecommerce solutions, consequently causing an increase in traffic congestion and consequential carbon emissions. 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.
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.
Demand planning optimization
Apart from reducing congestion and emissions, ML can also help reduce the overall impact of logistics on the environment by decreasing waste. Overproduction, overstocking, and stockouts exemplify inefficiencies that lead to waste. To combat this, companies are increasingly investing in demand planning optimization solutions.
Using ML-driven demand planning optimization systems, companies can generate more accurate forecasts by analyzing large volumes of historical data and accounting for external factors such as weather, seasonality, and economic conditions. This way, it becomes possible to determine the optimal amount of stock they need to have at any given time. Effective inventory management helps cut off inventory waste and keeps the supply chain lean, reducing the number of packaging materials required for transport.
Collaborative shipping
Collaborative shipping means sharing transport between organizations to decrease transportation costs and streamline logistics overall. By sharing GPS data, drop-off location information, and vehicles’ stock capacity across a network of delivery vehicles, it becomes possible to effectively implement a sharing economy aspect into logistics. As a result, collaborative shipping helps reduce the number of trips needed, decreasing fuel consumption and carbon footprint.
Truly efficient collaborative shipping becomes possible with advanced ML models. Uber Freight is such an example: the system analyzes real-time logistics data of different carriers and shippers, then automatically matches trucks with shipments according to the criteria set by the customers.
Afterword
The global push for sustainability is driving companies across the globe to explore more efficient ways of managing their supply chains. Machine learning and AI technologies can effectively supplement traditional logistics methods, enabling companies to reduce their carbon footprint and increase operational efficiency.
While it may be counter-intuitive, reducing carbon emissions with the help of AI doesn’t have to come at a price; it can even cut operational costs. Philip Ashton, CEO of 7bridges, an AI-powered logistics platform, revealed the results of tests that ran simulated supply chain models to decrease carbon emissions. When AI models were optimized for both cost and sustainability, it was possible to reduce carbon footprint by 19% while simultaneously cutting costs by another 19%.
While there are still many opportunities to explore, it is clear that AI and machine learning can play a significant role in reducing waste and increasing sustainability in logistics. The potential benefits of using ML-driven solutions to optimize delivery routes, schedule staff, and perform a myriad of other tasks directly impact the sustainability of supply chain operations.
