Top 5 Changes With AI in Logistics

There is no other way to explain it: Artificial Intelligence revolutionizing the world of logistics. That may sound cliche, or hype or buzz, but it’s true. Tech is fundamentally changing the way packages are moved around the world, from predictive analytics to autonomous vehicles and robotics. Here are the top five ways to make the Artificial Intelligence Logistics industry as we know it:

Predictive Capabilities Skyrocket when AI is implemented in logistics
Artificial Intelligence’s capabilities are increasing the company’s capabilities in the field of attendance demand and network planning. Having a tool for accurate demand forecasting and capacity planning allows companies to be more active. By knowing what to expect, they can reduce the total number of vehicles needed for transportation and redirect them to demand-driven areas, which can significantly reduce operating costs. Tech is using data to its full potential to better track events, prevent losses, and create solutions. This enables organizations to modify how resources are used for maximum benefit — and Artificial Intelligence can make these equations much faster and more accurate than ever.

Generally, logistics and supply chains Increase attendance analytics solutions. However, even if the technology is available, there is still a shortage of people who can understand incomplete and low-quality data, a case that is commonly demonstrated in the logistics industry. Like UPS, only a few large companies can afford to hire a team of data science experts to develop such a tool at home. Meanwhile, other players can also benefit from AI predictive capabilities by implementing solutions already available. The most famous examples are Transmetrics and ClearMetal, both of which are listed on the latest DHL’s Logistics Trend Radar.

AI analysis can also be used to protect against risk. Another good example from DHL is their platform, which monitors over 8 million online and social media posts to identify supply chain issues. With advanced machine learning and natural language processing, the system can understand the sentiments of online communication and identify potential material shortages, accessibility issues, and supplier status.

Robotics
Conversations about Artificial Intelligence are not complete without reference to the field of robotics. Although they look like the future, they are already embedded within the supply chain. Tractica Research estimates that worldwide sales of warehouses and logistics robots will reach .4 22.4 billion by the end of 2021. Robots locate, track, and move inventory within warehouses, delivering and sorting large packages inland distribution centers.

A good example of supply chain robotics is the work of StartupPhizier. Dutch Deep Tech Company is in the business of automating logistics and hiring robots worldwide. Pfizer embeds their deep learning algorithms into robotics and autonomous decision making for the processes of identifying, analyzing, counting, selecting, and changing objects. One of the most laborious parts of the logistic process is choosing, so Pfizer has devised a solution that allows the robot to detect package-type — within 0.2 seconds — and physically move the object to the desired location.

Big, clean data
Artificial Intelligence The answer is not just about robots. The power of Big Data allows logistics companies to predict the most accurate perspectives and optimize their future performance better than ever. The insights of Big Data, especially when generated by AI, improve many aspects of the supply chain, such as root optimization and supply chain transparency. See real-world examples: UPS saves 10 million gallons of energy a year by optimizing their routes, or how companies are getting smarter with last-mile deliveries. The industry itself understands how big data can drive big change: According to a third-party logistics study, 81 percent of carriers and 86 percent of third-party logistics companies believe that efficient use of Big Data is “the key to their supply chain.” “Why? The sector is complex, dynamic and relies on many moving parts. Big data helps to monitor everything.

Creating clean data for AI has become an important step in logistics companies, as most people do not have statistics that are useful to implement. Measuring efficiency gains is difficult because some companies generate their data from multiple points and multiple people. Such statistics cannot be easily improved at the source, so algorithms are being used to analyze historical data, identify problems, and improve data quality to a level that enables considerable transparency in business. When companies have incomplete transit data, a good example of data purge is that AI can gradually go past shipments to create accurate reductions on an unknown quantity. As previously written, these AI algorithms require only 5 to 10 percent of the correct data to create a training dataset, which is used as the basis for data purge and enrichment. Data from there provides an accurate estimate of the total freight characteristics of how full or empty the vehicle is.

Computer vision
Another eye is always a bonus when moving cargo around the world — and this is especially true when those eyes are connected to cutting-edge technology. Computer vision-based AI allows us to see things in new ways: including the supply chain. According to logistics giant DHL, visual inspection powered by AI is “faster than ever to detect damage, classify the damage type and appropriate corrective action.”

IBM Watson is a prime example of what’s possible with AI. The machine is programmed to detect how damaged train wagons are. When cameras were set up along the railroad tracks to collect images of wagons, IBM Watson quickly collected and processed their condition. In a short span of time, the robot’s visual recognition capabilities have improved to an accuracy rate of over 90 percent. Another good example is the retail giant Amazon, which uses Computer Vision Systems to help unload the trailer in just 30 minutes compared to hours without using such systems.

Autonomous vehicles
Last but certainly not least: autonomous vehicles. High-tech driving helps the logistics industry increase safety and efficiency while driverless trucks are still a long way off. Road luggage is set for big changes with high-speed autopilot, lane-assist and assisted-braking features leading to true autonomy. Improved driving systems already allow the installation of multiple trucks to reduce fuel consumption. These structures, which are controlled by computers that communicate with each other in a technique called platooning, are located close to the back of their aircraft. Such driving structures have been proven to save 4.5 percent for the lead truck and 10 percent more fuel consumption for the bottom track. In the meantime, companies such as Tesla, Einride, Daimler and Volkswagen are working on fully autonomous solutions.

Most of these autonomous vehicles are going electric. Charge ranges have been a problem in the past, but electric vehicles have been improving their speed capabilities, and last year Tesla announced that its semi-truck could run 800 km on full batteries and reach an additional 600 km. Charging is just 30 minutes.

conclusion
These five industry changes are notorious, but they are only the tip of the metaphorical iceberg. The most exciting thing about AI in logistics is that there are more than five apps that impact the industry. Technology is having a tremendous impact on the way we ship — and for years and decades to come, we can be sure that more collaboration between logistics companies and startups will help.