There’s no other way to describe it: Artificial Intelligence (AI) is revolutionizing the world of logistics. That may seem like a cliché, or hype, or buzz, but it is true.
The tech is fundamentally changing the way packages move around the world, from predictive analytics to autonomous vehicles and robotics. Here are the top five ways in which Artificial Intelligence is transforming the logistics industry as we know it:
1) Predictive Capabilities Skyrocket When AI in Logistics is Implemented
The capabilities of AI are seriously ramping up company efficiencies in the areas of predictive demand and network planning. Having a tool for accurate demand forecasting and capacity planning allows companies to be more proactive. By knowing what to expect, they can decrease the number of total vehicles needed for transport and direct them to the locations where the demand is expected, which leads to significantly lower operational costs.
The tech is using data to its full potential to better anticipate events, avoid risks and create solutions. This allows organizations to then modify how resources are used for maximum benefit – and Artificial Intelligence can do these equations much faster and more accurate than ever before.
For example, DHL analyzes 58 different parameters of internal data to create a machine learning model for air freight. Rather than subjective guesswork, this method allows freight forwarders to predict if the average daily transit time is expected to rise or fall up to a week in advance.
Furthermore, this solution can identify other factors which could influence shipment delays like climate and operational variables. Such insights are incredibly valuable in a sector like air freight, where it accounts for only 1 percent of global trade in terms of tonnage but 35 percent in terms of value.
In general, the predictive analytics solutions in logistics and supply chain are on the rise. However, while the technology is available, there is still a scarcity of people who can make sense out of the incomplete and low-quality data, the case commonly presented in the logistics industry.
Only a few largest companies can afford to hire a whole team of data science professionals to develop such a tool in-house, as in the case of UPS. Meanwhile, other players can also benefit from AI predictive capabilities by implementing already available solutions. The most well-known examples are Transmetrics and ClearMetal, which were both mentioned in the latest DHL’s Logistics Trend Radar.
AI analysis can also be used to safeguard against risk. Another good example from DHL is their platform which monitors more than 8 million online and social media posts to identify potential supply chain problems.
Through advanced machine learning and natural language processing the system can understand the sentiment of online conversations and identify potential material shortages, access issues and supplier status.
No conversation about Artificial Intelligence is complete without mentioning the field of robotics. While they may sound like a futuristic concept, they are already embedded inside the supply chain. Tractica Research estimates that the worldwide sales of warehousing and logistics robots will reach $22.4 billion by the end of 2021. Robots are locating, tracking, and moving inventory inside warehouses, they are conveying and sorting oversized packages at ground distribution hubs.
A good example of supply chain robotics is the work of startup Fizyr. The Dutch deep tech company is in the business of automating logistics globally and putting robots to work. Fizyr incorporates their deep learning algorithms into robotics and brings autonomous decision-making to processes that involve identifying, analyzing, counting, picking and manipulating goods.
Picking is one of the most labor-intensive parts of the logistic process, so Fizyr has crafted a solution which allows the robot to identify package-type – in less than less than 0.2 seconds – and physically move the item to the desired location.
3) Big, clean data
The Artificial Intelligence answer is not only about robots, however. The power of Big Data is allowing logistics companies to forecast highly accurate outlooks and optimize future performance better than ever before. The insights of Big Data, especially when generated by AI and protected via patenting it or encrypting it so as to remain a trade secret, can improve many facets of the supply chain like route optimization and supply chain transparency.
Just look at real-world examples: like UPS saving 10 million gallons of fuel annually by optimizing their routes, or how companies are getting smarter with last-mile deliveries.
The industry itself understands how big of a change big data will bring: according to Third Party Logistics Study, 81 percent of shippers and 86 percent of third-party logistics companies believe that using Big Data effectively will become “a core competency of their supply chain organizations.” Why? Because the sector is complex, dynamic and relies on many moving parts. Big Data helps to oversee everything.
Generating clean data has become an important step for AI in logistics companies as many simply do not have usable figures to implement. Efficiency gains are difficult to measure as some companies generate their data from multiple points and multiple people. Such figures cannot be easily improved at the source, so algorithms are being used to analyze historical data, identify issues and improve data quality to the level where significant transparency on the business is gained.
A good example of data cleansing in action is when companies have incomplete shipment data, AI can systematically go through past shipments to create precise deductions on the unknown quantity. As written previously, these AI algorithms only require 5 to 10 percent of correct data in order to create a training dataset which can be used as a basis for data cleansing and enrichment.
From there the data offers an accurate estimate of the whole shipments’ properties in how full or empty the vehicle is. Also, if you wish to repurpose the data for other business requirements such as marketing, ensure it follows the relevant laws for marketers.
4) Computer vision
Another set of eyes is always a bonus when moving cargo around the world – and this is especially true when those eyes are connected to state-of-the-art technology. Computer vision-based AI is allowing us to see things in new ways: including the supply chain. According to logistics giant DHL, visual inspection powered by AI is identifying “damage, classifying the damage type, and determining the appropriate corrective action” faster than ever before.”
IBM Watson is a prime example of what can be possible with AI vision. The machine had been programmed to identify what damaged train wagons looked like. Then when cameras were installed along train tracks to gather images of the wagons, IBM Watson quickly gathered and processed their status. Within a short period of time, the robot’s visual recognition capabilities improved to an accuracy rate of more than 90 percent.
Another good example is from retail giant Amazon, who utilize computer vision systems which can help to unload a trailer of inventory in only 30 minutes compared to hours without using such systems.
5) Autonomous vehicles
Last but certainly not least: autonomous vehicles. While driverless trucks may still be a while off, high-tech driving assistance is coming to the logistics industry to increase safety and efficiency. Road haulage is set for big changes with highway autopilot, lane-assist and assisted braking features predicted to lead the way to true autonomy.
Better driving systems already allow for multiple trucks to drive in formation to lower fuel consumption. These formations, intricately controlled by computers which communicate with one another in a method called platooning, follow closely behind other trucks in their fleet.
Such driving formations have been proven to save fuel use by 4.5 percent for the lead truck, and 10 percent for the following truck. In the meantime, companies like Tesla, Einride, Daimler and Volkswagen are working on fully autonomous solutions.
Many of these autonomous vehicles are also going electric. Charge ranges have been a problem in the past, but electric vehicles are quickly improving their distance capabilities with Tesla announcing last year that its Semi Truck will be able to drive as far as 800 kilometers on full batteries and can get an additional 600 kilometers range with just 30 minutes of charging.
AI in Conclusion
These five industry changes are groundbreaking, but they are just the tip of the metaphorical iceberg. The most exciting thing about AI in logistics is there are many more than just five applications impacting the industry. The tech is having a holistic impact on the way we ship – and the forthcoming years and decades are sure to bring more collaboration between logistics companies and startups to deliver even more cutting-edge advancement.
This article is written by Asparuh Koev, the chief executive officer of Transmetrics, predictive analytics software for cargo transport optimization.