Artificial intelligence has become a buzzword, often seeming to invade every part of our lives and businesses. Logistics is no exception, with AI logistics growing in importance over the last few years. AI is well-positioned to solve a variety of problems in logistics, including market volatility, safety, and climate change.
When used correctly, AI promises to reduce shipping costs, make supply chains more reliable, and reduce emissions by developing more efficient supply networks. Over the past few years, events have shown that increased resilience and redundancy is vital in logistics, with long supply chains proving fragile under threats such as the COVID-19 pandemic and the conflict in Ukraine, as well as outsized impacts from shipping issues such as the Ever Given.
So, how do we use AI to improve logistics, without falling into the common trap of over-reliance?
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AI in Logistics
First of all, by artificial intelligence, we are (mostly) not talking about generative artificial intelligence here. This is not ChatGPT or other text or image generators that create typically inferior “art” and “content.” However, generative AI can have some uses in summarizing information in the logistics industry.
The kind of AI being used in logistics is dominated by two areas. The first is warehouse automation and robotics, with robots working alongside human workers or, in some cases, replacing them.
The second is improved data analysis that can streamline processes from the factory to the last mile. Data analysis, such as automated route optimization, can assist transportation workers in their jobs by making them easier, faster, and mitigating truck driver shortages by allowing a worker to do more work without spending more hours on the road.
AI can also supply predictive analytics. By analyzing past and present data, and comparing it to online research, AI can provide more accurate predictions of the market, allowing for better pre-positioning of inventory and overall planning.
Top 10 Best Uses of AI in Logistics and Examples
Every company’s needs are different. Your company may not use AI logistics in quite the same way as another logistics company. Best uses vary depending on factors such as whether you are transporting perishable goods, managing a supply chain for consumer electronics, or moving bulk commodities, for example. However, there are some basic uses that almost all companies can benefit from. Here are ten we believe are the best uses of AI.
Predictive Maintenance for Delivery Vehicles
Having a delivery vehicle break down on the highway somewhere is a nightmare for you and your employees. You now have a stranded driver, a late shipment and, potentially, angry customers.
Predictive maintenance means bringing vehicles in for maintenance before they break down. It optimizes the performance and lifespan of your equipment. Traditionally, preventive maintenance has been done by relying on the manufacturer’s recommendations for maintenance, such as how often oil should be changed and tires rotated.
AI can make maintenance more accurate and efficient by using data from across your fleet to assess the condition of vehicles. Unlike time-based maintenance, this takes into account things such as the length of routes, the terrain, etc. For example, if you routinely run trucks across passes in the Rockies or Sierras, those trucks are going to need brake maintenance more often than trucks that travel across the Dakotas. Predictive maintenance takes this into account and allows you to do maintenance at the right time.
This improves reliability and safety while reducing downtime.
Delivery Load Optimization
Obviously, you don’t want to overload your vehicles and risk an accident. At the same time, you don’t want them running half empty. AI can use predictive analytics to combine load planning and route optimization to keep, as much as possible, your trucks always full.
For example, a moving company that uses large trucks to carry multiple homes to another state can use delivery load optimization to plan the order of pickups and drop offs to be more efficient while ensuring that the truck is loaded properly. For last-mile deliveries, load planning includes the order in which packages are loaded onto the truck.
A load optimization algorithm can work in real time to re-optimize the plan due to any alterations. For example, the first load our movers are picking up is a one-bedroom apartment, but it turns out that the owners have about twice as much as a normal one-bedroom apartment. The algorithm can rejigger trucks to make sure that everyone still gets moved, reducing delays and costs to both you and your customers.
Scalability
One of the things about AI is that the larger the problem you give it, the more data it has to work with, and the better the results. AI naturally scales with your business, learning and adapting to changes.
Modern AI models don’t necessarily need to be tailored to a specific problem size and characteristics. You can keep training the model and use the same base model for a fleet of 200 trucks that you used for a fleet of 20. Machine learning and ongoing training allows your algorithms to adapt.
You also don’t need to spend time on designing new routes when you expand into a new area. The AI logistics algorithm can develop new routes, although feedback from your drivers remains important.
Real-Time Delivery Monitoring
Traditional mail tracking works by having the courier scan the items in at each destination. Real-time monitoring allows the recipient to see where the package is at all times, using GPS, especially during last-mile delivery.
AI makes this more accurate and increases supply chain transparency. Customers can see right away if there’s a problem (the package is heading to the wrong address, for example), and you can also detect and resolve issues faster, sometimes before the customer notices. For internal delivery tracking, you can identify issues, update ETAs in real time, and even see if your driver is unexpectedly stationary, which could indicate a breakdown or an accident and that they need help. Overall, real-time delivery monitoring can be a great tool for improving customer satisfaction.
Inventory Management
In addition to optimizing loads, AI can optimize your inventory. Modern just-in-time systems have shown their fragility through recent world events. At the same time, keeping extra inventory is expensive.
Advanced predictive analytics can continually learn demand and automatically order new stock as needed. AI can also provide insights into inventory levels, demand, and supply, allowing you to avoid overstocking and understocking and also helping sales and marketing understand how their efforts are working.
It increases accuracy, reduces costs, and improves fulfillment times.
Automated Scheduling and Dispatching
If you bring together route optimization, real-time monitoring, and inventory management, you can automate scheduling and dispatching completely. When a customer places an order, the AI can look at where the item is, predict shipping time, and automatically add it to a dispatch.
For bulk transport, automated scheduling systems can work with your customers to schedule pickup times properly, enhancing accuracy. By reducing human input, you also reduce the risk for manual errors. This doesn’t mean your employees are replaceable– but it does free them up to do tasks that require more critical thinking and less tedious task work.
Fully automated scheduling and dispatching is also better for your drivers, who can get a clear and accurate schedule that is also accurate to the time they typically spend on the road. Only unexpected issues, such as a road being closed, will slow them down, and even then, the AI can react to those situations in real time.
DIVE DEEPER: Routing & Dispatch Product Overview
Customer Service Enhancement
This is one use case where generative AI can shine. When your customers contact you, AI can handle routine and preliminary inquiries and then forward to a live agent as needed. Chatbots have provided this service to businesses and customers for a long time, but generative AI makes the chatbot more responsive and natural. Customers may not even be able to tell they are talking to a bot.
AI-powered customer service agents can handle a broader range of inquiries, which means your team only gets the most interesting and challenging problems, making their jobs more interesting and fulfilling. Meanwhile, the customer gets a quicker response and is not put on hold, forced to wait, or sent back to the website when it didn’t solve their problem in the first place.
AI can also use data to provide more personalized service, including personalized product recommendations to online shoppers, or checking the account history to see what deals or offers might be appreciated.
While this kind of service shines for retail, you can use it at any level including business to business to get routine inquiries handled quickly and seamlessly.
Demand Forecasting
We’ve touched on demand forecasting when we talked about inventory management. But AI-powered demand forecasting can also help provide better customer service, improve sales, and potentially assess demand for a new product.
All of this improves your bottom line without putting extra work on sales, marketing, or fulfillment. This allows you to plan your supply chain in the long term, reduce waste, and potentially increase service levels by as much as 65%.
Risk Management
The way that AI currently works, it can’t predict an unforseen catastrophe. However, it can anticipate other risks such as labor shortages and natural disasters. AI-based risk management helps with proactive risk identification by detecting small anomalies that can indicate an emerging risk before a human will notice it. For example, a slight delay in supplier delivery times from their historical average is something to look at. Human eyes can then determine if this is the result of bad weather, poor luck or something more serious, such as impending industrial action, that may warrant temporary or permanent supplier diversification.
AI can also simulate the outcome of a threat, and provide multiple possibilities that can be used to develop a contingency plan. AI will then learn from how well the plan worked and make better decisions in the future.
Supply Chain Visibility
Improved supply chain visibility allows all shareholders to see what is going on. Whether it’s a single customer tracking their package, or a network of suppliers looking to work together to improve efficiency, increasing visibility helps everyone.
AI can crunch the large quantities of data needed to keep the supply chain visible to everyone, in real time, with problems showing up as they occur. The impact of an event, such as a bridge collapse in Baltimore, can be seen quickly as it cascades through the system. You can then react faster and use existing data to quickly assess how to redirect shipments around the problem and whether you need to temporarily bring on another vendor.
Supply chain visibility also helps you continuously improve your processes and use your supply chain efficiencies to attract customers and clients.
Impact of AI in Logistics
The positive impact of AI in logistics so far is strong, and likely to get stronger. AI has been proven to provide:
Efficiency Improvements
Route and load optimization, as well as automation, reduce delays, ensuring loads and packages get to their destination quickly. Automation improves operational workflow, reducing human error. In addition, predictive maintenance lowers downtime and mitigates the risk of a breakdown on the road. All of this makes operations more efficient and sustainable.
Cost Savings
Costs can be significantly reduced by minimizing fuel consumption and labor costs, and reducing the overhead costs of overstocking. When done correctly, AI implementation can also improve employee satisfaction and reduce turnover, reducing training and hiring costs.
Enhanced Accuracy
Automated dispatching and scheduling eliminates several layers of potential human error. The system is typically more accurate, providing accurate information to customers, assigning routes correctly, and reducing unexpected out of stock situations.
Sustainability
Optimized operations improve sustainability by reducing energy and resource uses. Overall environmental impact can be reduced substantially with route optimization alone, but inventory management can reduce warehouse needs, and risk management can help you mitigate environmental effects even as you reduce your own contributions to them.
Challenges of AI in Logistics
AI has to be implemented correctly. There are very real challenges and obstacles that can interfere with adoption and which need to be worked around.
Adoption Barriers
These AI systems come with an up front cost. They require infrastructure to run, and often that infrastructure is in the cloud. IT costs will inevitably go up. You also need experts who can create the initial models for you and work with you to make sure they stay within guardrails.
Ethical Concerns
Workforce displacement is a real concern with AI. While AI is typically beneficial to drivers, who appreciate the route guidance and the fact that accurate route planning reduces the risk of unexpected overtime, it can displace warehouse workers and even some office staff. AI should be used to complement, not replace human judgement.
Additionally, when personal information ends up in AI training data, it can be spat out again later, resulting in privacy concerns. When possible, AI should not be “fed” personal data (example: employee addresses).
Technical Limitations
AI is only as good as the data you train it on. High data quality is vital for any implementation of AI, and care has to be taken with certain forms of AI, such as large language models, not to feed them AI-generated data. This can result in model collapse, which can mean you need to start over. Data will also tend to support existing biases. If your drivers prefer a certain route because it’s closer to lunch, then that will impact route optimization data, not necessarily in a positive way.
Conclusion
Correctly implementing AI logistics requires expertise– and Elite EXTRA has that. Long before it became a buzzword, we’ve been leveraging AI and automation to provide our customers with enhanced route processing and creation, real-time delivery monitoring, automated dispatching, automated reporting, and more. Interested in learning what it can do for your operations? Take a deeper dive into our white papers below.
Dive deeper into the world of last-mile logistics by downloading our latest white papers for free!
Steps to Automating Last-Mile Deliveries in 2024 & Beyond
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Key Benefits of a Last-Mile Routing and Dispatch Software
Enhance your delivery ROI with Routing and Dispatch Software. Learn how to make your deliveries quick, accurate, and cost-effective.
Sources
https://mitsloan.mit.edu/ideas-made-to-matter/how-artificial-intelligence-transforming-logistics
https://www.forbes.com/councils/forbestechcouncil/2023/08/17/the-true-role-of-ai-in-logistics
https://www.forbes.com/councils/forbestechcouncil/2024/10/11/revolutionizing-logistics-how-ai-and-machine-learning-are-transforming-the-supply-chain
https://www.nature.com/articles/d41586-024-02420-7
