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Listed below are some proven tips for today's logistics leaders on how to leverage data analytics to drive both carbon and cost savings while focusing on the direct environmental impact of those methods.
Fremont, CA: Combining data analytics and process automation can help drive significant efficiencies by lowering costs, streamlining operational processes, and improving communication between shippers, carriers, and brokers.
Transportation logistics companies can reduce their carbon footprint and the environmental impact of moving freight throughout the supply chain by improving fuel efficiency and operational efficiency with artificial intelligence and machine learning to drive data analytics.
The following are some of the proven tips for today's logistics leaders on how to leverage data analytics to drive both carbon and cost savings while focusing on the direct environmental impact of those methods.
Make Your Data do the Work for You
Using AI and machine learning to analyze data helps streamline operations and reduce emissions in various ways.
AI-powered systems monitor data generated by day-to-day logistics activities. This includes analyzing volumes, distances, and mode selections and documenting inefficient modes, routing, and empty miles caused by low utilization. These systems also consider the impact of fleet planning and routing, dwell time and detention tracking (during which trucks sit idle while waiting for scheduled pick-ups and drop-offs), and a slew of other factors influencing carbon fuel consumption.
According to CDP, an international nonprofit that promotes environmental disclosure, greenhouse gases emitted by companies' supply chains are five times greater than those emitted by direct operations. However, managing greener supply chains can result in significant long-term financial and commercial benefits for organizations.
Artificial intelligence and machine learning are already assisting forward-thinking carriers in reducing deadhead miles and inefficient loading and routing. These technologies help combine less-than-truckload loads into multi-stop truckloads and make other model-selection recommendations to reduce fuel consumption. This same technology is used to significantly monitor and predict better routes based on traffic patterns, weather, and historical drive times, thereby optimizing time spent in transit and lowering vehicle emissions.
When it comes to monitoring and reducing carbon emissions, AI and machine learning have the potential to be game-changers. They work together to provide deep insights into multiple aspects of a company's carbon footprint and to identify cost-effective ways to accelerate sustainable transformation, such as:
• Monitoring emissions — assisting businesses in determining where improvements are needed along the supply chain;
• Emission forecasting entails forecasting future emissions based on historical data and current reduction efforts.
• Reducing emissions entails providing detailed insight into how an organization can improve efficiency in transportation and other areas to reduce its carbon footprint.
Rather than seeing sustainability and carbon reduction as a burden, logistics operations should recognize that climate action provides an opportunity to create value by entering new markets and meeting the rising demand for low-carbon, greener services.
