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 whichArtificial Intelligence is transforming the logistics industry as we know it:
- 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 accurately than ever before.
In general, predictive analytics solutions in logistics and supply chains 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 its 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.
Also Read: Uses Cases of AI in Supply Chain Management
2. 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, can improve many facets of the supply chain like route optimization and supply chain transparency.
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 that 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.
3. 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 the retail giant Amazon, which utilizes computer vision systems that can help to unload a trailer of inventory in only 30 minutes compared to hours without using such systems.