The mobility revolution will see the application of four modes that will use Machine Learning. Swami Sivasubramanian , Vice President, Amazon Machine Learning, AWS talks about it .
When Switzerland decided to reduce congestion and pollution by removing tens of thousands of trucks from its Alpine motorways, it built the Gotthard tunnel.
This modern engineering feat is a boon to civil and commercial entities. But these kinds of projects are not the only way to improve the future of transport and logistics today.
In a world where only 29% of Transportation and Logistics (T&L) CEOs are confident that their companies’ revenues will grow in the next year, more and more T&L companies are deciding to leverage cloud-based Machine Learning services to succeed. to become more efficient and improve their customer experience.
The convergence of cloud and artificial intelligence is enabling widespread innovation in autonomous technology, especially in the mobility sector.
This is a radical change. According to PWC, 68% of T&L business leaders believe that changes in core technologies of service delivery will bring a strong boost to their industry over the next five years.
Mobility and Machine Learning
While 65% predict that advances in distribution channels will do the same.
Going into detail, there are four main areas in which machine learning is fueling a mobility revolution for the transport and logistics industry : demand forecasting and route optimization, autonomous driving and mapping, robotics and anomaly detection.
But how do they apply in the reality of a company and what are the benefits?
Convoy is disrupting the $ 800 billion trucking industry by optimizing routes by leveraging machine learning models.
Trucking in the United States is a fragmented network of freight forwarders and haulers working through human intermediaries, an inefficient system that causes 40% of the 95 billion kilometers that American truckers travel each year to be driven empty.
Convoy is able to analyze millions of jobs in the shipping industry to create the most efficient couplings in the industry – increasing profits by reducing empty miles and, most importantly, reducing emissions.
But the trucking industry has a nationwide shortage in the US of at least 100,000 drivers .
A solution? Self-driving trucks. At TuSimple, the technology team has deployed more than 100 cloud-based AI modules to safely and efficiently make autonomous commercial deliveries of over 100 miles.
Even at 65mph on a loaded truck, TuSimple’s advanced AI algorithm is able to distinguish between the types of vehicles sharing the road, determine their speed, and keep TuSimple’s trucks centered safely in their lanes with an accuracy of +/- 5 centimeters.
The mobility revolution and Machine Learning
In Southeast Asia, Autogrill company Grab wanted to enhance its matching algorithms and real-time on-demand delivery.
It turned to machine learning tools to access real-time data computation and data streams that support 1.5 million ride bookings, ultimately improving its matching and delivery performance by 30%.
Another example of AI and machine learning that has positively impacted the T&L industry is Lyft’s use of an AI-powered time series analytics solution.
This technology automatically highlights anomalies that signal larger business problems and detects incidents that require inspection. Lyft has made huge cost savings by not having to invest in large in-house data science or manually inspect dashboards.
Forecast accuracy, of course, is an important factor for T&L companies, and in UAE-based Aramex – which provides international and domestic express delivery, freight forwarding and online shopping services – transit operations in real time they handle thousands of requests every minute.
By implementing a fully managed, cloud-based service that allows its developers and data experts to train, build and deploy AI and ML models, Aramax has seen a 74% increase in the accuracy of forecasting transit times, reducing delivery-related service calls by 40%.
Cloud-based AI and machine learning tools are also at the heart of Amazon.com which is able to successfully and efficiently deliver billions of packages per year – from the moment customers place an order, to evasion, until delivery.
Amazon uses forecasting algorithms to understand what customers might order thus ensuring sufficient supply in warehouses. AWS AI and machine learning services also power Fulfillment Center robots, methods for working with delivery partners, and even for optimizing Amazon’s own delivery routes.
The “lessons” of the last few years are clear: being competitive in the T&L sector has never been so complex and important at the same time, and profitability is the result of true technology-driven efficiency.
Fortunately, the latest innovations in AI and ML are bringing these companies a huge advantage, giving them the tools they need to meet challenges and thrive.