How AI and IoT are Transforming Transport Management AI-powered IoT systems provide predictive analytics, playing a crucial role in running businesses more efficiently.
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Artificial intelligence (AI) and the Internet of Things (IoT) are both cutting-edge technologies that are starting to get traction in the world around us. Fortune Business Insights notes that the size of the global AI market hit $27 billion in 2019, and estimates put it to grow tenfold by 2027. The IoT is just as vibrant a sector. PR News Wire contends that the amount of IoT devices will cross 24 billion by 2030. Both of these advances count as "smart" technologies. The way they're impacting the world today will increase as more IoT devices make their way onto the market, and the cost of implementing AI goes down. But how could tech like this change transport management?
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Solving problems in transport management
Globally, transportation management and logistics can be complex fields. Today's world is constantly connected, making it possible to get wind of an error and correct for it faster than ever before. However, even though we can get data quickly thanks to the Internet, there's still the issue of efficiency in data collection before transfer. Visibility, reporting, analysis, and communication are all parts of this global network of transportation management data. However, if one of these arms breaks down, the entire system could become inefficient. A few feedback systems could come into play to correct for these issues, but they create complex solutions for a simple problem.
Cost management is another major issue facing global transportation. The price of shipping, even regionally, fluctuates based on local factors such as the cost of fuel and route availability. As supply lines become longer, they also get harder to maintain. Terms of purchase can also fluctuate based on the source of a good. Aggregating cost prices and coming up with the most efficient purchase option is a critical element of transportation management. But even so, coming up with a viable purchase option that will cut back on costs in buying the goods and shipping them across borders can take substantial time and effort. Since markets are dynamic, this analysis must be ongoing.
Delivery performance is another area that transportation management needs to pay attention to. When a company buys a product, it expects to get the product to its storage facility in the shortest time possible. Efficient shipping routes would make that a simple matter. However, not all transportation routes offer the same shipping information. In addition, balancing logistical costs such as paying workers and fuel costs can impact a business's profitability, making some goods simply less viable than others in the long run.
The solutions that AI and IoT provide
So how can global transport management leverage AI and IoT to get its goods to their destinations at the lowest cost and effort? Thanks to these interlinked technologies, we have a handful of upcoming innovations that may solve several of these problems simultaneously.
1. Self-driving (autonomous) vehicles
By now, everyone should know about the battle to develop the first autonomous car. From Uber to Google to Tesla, everyone's rushing to become the first to create a safe self-driving car. Unfortunately, the technology is far from mature. Today's self-driving vehicles demonstrate many flaws, making them useful as an auto-pilot feature but far from the expected cars that could theoretically run themselves. AI is working to help improve these vehicles' capabilities. By using visual sensors in cameras, AI can "read" road signs and pick up visual cues to modulate the vehicle's speed and trajectory. IoT devices ensure that the car can communicate with nearby automobiles and update its position to a central database. For logistics planners, this could remove the issue with reporting since the load reports itself. If it gets into difficulties through a traffic stop or an accident, the company can be informed immediately.
2. Better decision-making
Artificial intelligence learns quite differently from humans. In most cases, researchers give the AI a start position and an end position, and task it with getting from one to the other. The AI makes decisions and comes up with a conclusion. However, sometimes those decisions lead to terrible results. In training an AI, researchers can give it tens of thousands of test data points to refine its decision-making skills. When applied to transport management, AI can leverage data points for IoT devices within the cargo route. Thus, it can spot patterns across a massive range of data far more efficiently than humans. The iterative process means that efficient transportation routes and patterns show up more often, letting the business plan for problems in advance.
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3. Analytical uses
Humans have been trying to predict the future since time immemorial. In the past, they'd use analytics to tell what a child might grow up to achieve. Today, we take a more statistics-based approach in analytics, but it's still a lot of shooting in the dark. Luckily, AI and IoT bring a way to develop an analytical engine that considers billions of data points. Streaming data from IoT devices comes into an AI engine that adds it to a constantly growing pool of information. Through processes that spot patterns, the AI can reliably predict the changes in a product's cost, allowing a business to adapt. By knowing what's in store, a transport management business can shift around its resources to meet a coming crisis before it happens.
4. Warehouse and inventory management
Anyone who has been tasked with managing a warehouse knows how complex this part of transport management can get. But what if the products on shelves knew how much of them they were? Would it be easier to plan logistics in that case? What about knowing when the demand for a product would go up based on the inventory at distribution locations? Sophisticated algorithms could be used to predict these things and allow an inventory department to cater to them. IoT provides for direct reporting from the products themselves. A central AI could then aggregate these reports and show a regional map with "hotspots" where demand may spike based on previous sales numbers. All of this could be done in real-time, reducing lag in ordering and ensuring each distribution center has enough product from the warehouse.
What are the practical business benefits?
AI and IoT in combination could mean massive savings for a company's operating costs, resulting in a much healthier bottom line. A business could also benefit from improved efficiency. IoT devices and AI don't need to take breaks, so the company's data center would be running every hour of every day to ensure things go smoothly. Whether it's doing inventory for a shoe supply store or an online car auction, these benefits extend to all areas of transport management. Logistics could get a whole lot easier in the next decade.
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