The Impact of AI on Logistics

With artificial intelligence (AI) comes the opportunity for businesses to leverage it to create efficiencies through the automation of time-consuming processes. Thanks to machine learning, AI only becomes more efficient over time, with predictive analytics distilling data into relevant insights companies can use to achieve their strategic goals. Though AI is already impacting most industries, its presence can arguably be felt most strongly in the world of logistics.

Logistics companies have numerous potential avenues for leveraging AI in their operations. The biggest gap in AI as it stands is a lack of professionals able to interpret the sheer volume of data it produces. Third-party solutions are currently the most viable for companies, as the alternative of hiring a team of data scientists is prohibitive at this time. It’s possible, as costs decrease in the future, that it will become more feasible for logistics companies to engineer in-house solutions for their benefit.

In an industry governed by data, AI is revolutionary for helping logistics companies be proactive with demand forecasting and capacity planning. Predictive analytics can reduce operational costs by enabling better vehicle distribution and avoiding supply chain risks. AI can even monitor online activity to preemptively identify potential issues with suppliers or materials. 

Labor is another aspect of logistics that can be made easier and safer through AI. Robotic systems are already in use in distribution centers worldwide, helping to automate material conveyance and sorting. Additionally, the field of autonomous transportation has already seen some success when it comes to fielding fleets of AI-assisted vehicles. While the end goal of full automation is a ways away, new safety features and autopilot options offer assistance for human drivers. Software-managed trucks driving in formation can help cut down fuel costs as well. 

Saving fuel is also possible through the interpretation of Big Data through AI. Route optimization is complicated—in fact, it’s a balancing act in which it’s far too easy to overcommit or undercommit on vehicle deployment. Accounting for other factors such as weather, road work, and changes in fuel cost makes it so that there is no static solution for logistics routing. Big Data is also a player in solving the Last Mile problem. A lot of costs in the logistics industry are tied to difficulties in finalizing the last stage of the shipping process. By using AI to gain insights on common problems and providing real-time updates to customers and shipping agents, companies can save time and money for themselves.

In fact, our company Hala SCS has already started making good use of AI-integrated software. We’ve saved up to 20% costs in some cases by optimizing the routes and creating efficient utilization of the fleet. Our software leverages market-proven algorithms and geographic network modeling to enhance order fulfillment for large batches and from as many warehouses as available in the shortest, most efficient, routes available.

And AI can only get better from here. While a lot of data is considered “junk data”, better algorithms can retroactively distill useful information from past records, even when many are outdated or inaccurate. From there, AI can identify patterns and get started on producing usable figures for companies.

AI’s efficiency gains should not be ignored, especially when a data-driven approach is the only way to handle the millions of moving parts that come with logistics. Companies should invest early in innovative solutions or risk falling behind.