Artificial intelligence (AI) is a vast topic of computer science which aims to create intelligent machines. There are a variety of definitions depending on the focus, scope, and aims of the intelligence being worked on. Broadly AI is defined as the ability for a digital computer or computer-controlled robot to perform tasks commonly completed by an intelligent being. It is generally divided into two groups. Weak- or Narrow-AI is when the machine focuses on a single narrow task and is pre-programmed with the information needed to complete the task. Strong AI is when the machine is able to think and perform complex tasks on its own, as a human being would.
Artificial intelligence has numerous and ever-expanding uses across a variety of industries. Every day we continue to identify more practical uses of existing AI, or future uses as the technology expands and improves. In the logistics and transportation industries, AI technology is increasingly popular to solve a variety of challenges and inefficiencies present in the sector. While today we are discussing five of the most commonly used of AI in logistics, it is important to remember that the field continues to grow rapidly and this list is not an exhaustive representation of all the uses of artificial intelligence in logistics and transportation.
Intelligent and Automated Warehousing
Warehouse inefficiency is a common problem in logistics, increasing wasted time and energy within the operation, which in turn can drive up costs. Companies continue to seek new and better solutions to improve their warehouse efficiency and maintain more sustainable business practices. It is impossible to say what the 'right' solution is, but many warehouses are seeing great success with AI automation of their warehouse operations.
Many of the manual labor tasks within warehouses have been partially or completely done by robots for decades including moving of palettes, stocking shelves, or lifting heavy items. Increasingly, however, warehouse robotics are becoming automated, allowing them to complete tasks with limited human intervention. AI robots are able to stock shelves, move items, and even pick-and-pack orders, all on their own. The trend is moving towards robots who are able to analyze the warehouse floor and make adjustments for efficiency without any human intervention. In the end, the goal is that the AI machines will be able to learn and evolve as they work to improve their own operations and therefore the operations of the business as a whole.
Blockchain began as the technology used to securely track cryptocurrency transactions in a secure and distributed ledger. The system creates a ledger of secure and uneditable blocks which are distributed across a network, allowing them to be securely viewed without risk of being altered. In this way, the technology provides a highly secure way of tracking cryptocurrency transactions.
Today, blockchain has many more applications beyond cryptocurrency. One major use of blockchain is within product safety and tracking throughout a supply network, most commonly food and medications. By tracking the movements of a product through a supply chain and recording these movements as transactions on blocks in the blockchain, you create an accurate and uneditable record of the path a product took to arrive at its destination.
AI machines designed to do so can analyze these blockchains to quickly and easily to determine where products come from. This is extremely important when it comes to recalled products such as contaminated food or medicines, as retailers can easily determine which shipments are at risk. Without blockchain and AI, this process is lengthy and generally results in discarding of safe products as a precaution.
Supply Chain Software
Software solutions within supply chains are an area where artificial intelligence is advancing very quickly. For example, some software is beginning to utilize predictive algorithms to manage inventory and orders, stock, and even sale prices. These algorithms are able to identify patterns of sales over time to determine what is statistically most likely and therefore what actions are best suited for this likely outcome. These algorithms range in function and complexity but are consistently proving to be more accurate than human predictions can be.
Machine learning algorithms are also in regular use in delivery today, helping companies plan routes and optimize delivery schedules, taking into account vehicle capabilities and capacities, traffic, weather and road data, special site-specific considerations and other factors. By incorporating IoT devices and real-time sensor data, companies can create feedback loops that can then be analyzed against predictions by the optimization system. By constantly comparing algorithmic predictions to real-life results, the system can use this feedback to 'learn' and improve its accuracy over time.
Vehicles have been getting smarter for years. Early into making cars more 'intelligent' vehicles were fitted with sensors to detect nearby dangers, lane departures, or issues within the vehicle's mechanics. Next came things like assisted parking, in which the car is able to park for you when set up correctly. Now, with AI technology advancing faster than ever, vehicles of all sizes are being produced which are capable of piloting themselves.
Many consumers are aware of self-driving cars, but consumer vehicles are not the only vehicles being made autonomous. Fleet vehicles such as delivery trucks and vans, warehouse and yard vehicles responsible for moving palettes or shipping containers, and even mass transportation vehicles like buses are all being explored for self-driving possibilities. These vehicles would be able to operate in conjunction with a human driver, or completely alone, allowing for the ability to redistribute labor to more important tasks.
All of the logistics sectors will benefit from self-driving vehicles, as will members of the public with whom we share the road. Autonomous vehicles have the ability to reduce time spent driving, optimizing routes and reducing fuel usage. They also have the potential to eliminate or greatly reduce human errors in traffic or while approaching a loading dock, leading to safer driving conditions for everyone.
Cars are becoming increasingly intelligent, with more and more onboard computers and self-driving capabilities being released each year. Eventually, all cars on the road will be able to communicate with each other, and with your mobile devices, to increase the safety of vehicles on our roadways. The next big piece to this road-safety puzzle is to connect the roadways themselves.
Several companies are working on creating smart roads and producing different solutions based on the needs of their regions. In some areas, roadways made of solar panels with attached LED lights are being produced which are able to generate electricity as well as use the lights to modify traffic patterns or alert drivers to important roadway conditions. In addition, these solar panels are heated and therefore will not get icy during winter.
Another company is working to product smart pavement, in which IoT devices are connected to the roadways. These devices are able to monitor traffic and conditions on the road, including monitoring for collisions or other accidents. The pavement is able to connect to your mobile device, to smart vehicles, and to emergency services to keep everyone aware of changing situations.
Routeique and AI
Routeique is proud to be working with artificial intelligence and machine learning to better serve our customers and ensure we are on the cutting edge of supply chain technology. Our route optimization system, our vehicle brain IoT device, and our membership in the Blockchain in Transportation Alliance all speak towards our dedication for improving and advancing AI technology in the logistics sector.