How AI is Redefining Last-Mile Logistics?

While AI’s impact on logistics is mostly talked about in the context of self-driving vehicles and other futuristic tech, its real effects lie in the less glamorous but much more critical field of last-mile logistics.

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After all, last-mile logistics accounts for 53% of your shipping costs, so it’s essential to make it as efficient as possible.

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This final leg of the supply chain, where goods travel from distribution hubs to customers’ doorsteps, has historically (and still is) plagued by inefficiencies that lead to rising costs and customer dissatisfaction.

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AI has the potential to quietly revolutionise this space, allowing for new levels of precision and customer satisfaction, all essential for lowering e-commerce costs.

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In this article, I’ll take a look at how these processes work, along with real-world examples to show how they can help your business right now.

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1. AI is already having an impact on last-mile logistics

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If you want to up your last-mile logistics game, the solution isn’t fancy gadgets (as much as you and your customers might delight at the oddly cute delivery bots).

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It’s about embracing the behind-the-scenes technologies that are already driving measurable improvements.

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The transformative power of AI in last-mile logistics isn’t just theoretical, it’s already delivering tangible results for companies around the world.

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Many companies are already utilising AI to help manage warehouses more efficiently, but some of the biggest companies are going even further when it comes to last-mile logistics.

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1.1 Amazon

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The current king of e-commerce didn’t get to where it is today without embracing emerging technologies, and AI is no different for Amazon.

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Ever wondered how they can manage to complete the millions of same- and next-day deliveries they complete each day? That’s AI in action.

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They’re already using AI to power their vast logistics network, and have managed to massively reduce delivery times and costs.

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Amazon’s AI use has sped up their supply chain by a massive 75% and allows them to maintain that efficiency even at peak busy times.

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They analyse historical data to forecast demand around events like their Cyber Monday sales and during the holiday season, allowing them to proactively ensure their supply chain is ready to handle an increase in orders.

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Another aspect are their investments in robot inventory management systems Sequoia and Digit. These warehouse robots reduced incident rates and lost-time incident rates by 15% and 18%, respectively.

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1.2 Walmart

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While they’re most famous for their big-box stores, Walmart also manages a vast delivery logistics network to both stores and customers.

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Through its investment in AI-powered micro-fulfilment centres, Walmart has been able to offer same-day delivery on thousands of items, competing with Amazon in the e-commerce space.  

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Walmart has also invested in robotics as a key feature of their AI-driven logistics with their Alphabots.

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Through their partnership with AnyLogic, they developed sophisticated algorithms that allow the robots to pick 95% of online orders in under 8 minutes.

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1.3 Bigblue

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Bigblue is revolutionising last-mile logistics with AI-powered solutions that drive efficiency and optimise delivery performance.

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Its proprietary WMS, Atlas, improves operational efficiency by 25%, automating time-consuming processes like order scheduling and fulfilment prioritisation.

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The system’s batch picking algorithm groups similar orders to streamline collection, while picking path and storage optimisation ensure the shortest and most efficient routes in warehouses, even factoring in physical constraints.

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‍Atlas’ intelligent restocking algorithm leverages historical and real-time demand data to maintain stock availability at optimal levels.

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Beyond warehousing, B AI also optimises packaging selection to minimise costs and waste while complying with merchant specifications.

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Additionally, delivery times are continuously monitored, and in case of delays, automated customer notifications are sent, reducing uncertainty and improving satisfaction.

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Bigblue’s Smart Delivery offer powered by IA continuously analyses over 10 million parcels delivered, dynamically reallocating carrier choices at a postcode level based on recent performance data.

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This ensures that brands always use the most reliable and cost-effective delivery solutions.

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In 2024, this system outperformed standard carriers by 9% in on-time deliveries.

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1.4 DHL

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The logistics company uses AI to optimise delivery routes and schedules, reducing fuel consumption and operational costs while improving delivery accuracy.

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They’ve combined their AI-driven parcel sorting system with their already legendary to give a huge boost to their efficiency and reduce delivery times.

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AI technology from Wise Systems allows DHL to map out a route with over a hundred stops and order the most optimal sequence of delivery, while also taking into account factors like urgent medical deliveries, deliveries that have set delivery times, or just optimising the distance between stops.

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1.5 Instacart

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Grocery service Instacart is another major brand using localisation APIs and AI-powered inventory to streamline its delivery operations, ensuring that customers receive their orders quickly and efficiently.

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Instacart also uses AI to help customers create their grocery lists, showing how AI can be utilised for bespoke experiences at high efficiency.

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Instacart uses a combination of different algorithms to ensure customers can complete their orders.

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First, its Item Availability Model looks at the history of how often customers are able to order items and assigns them an availability score between 0.0 and 1.0 (e.g. a score of 0.8 means the items have an 80% chance of being available in a store).

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If an item with a low availability score is selected, their Item Replacement Recommendation Model will then prompt customers to select a potential backup item, which reduces customer frustration over items they order not being available.

Now, let’s take a look at how these AI applications work and the impact they can have on your business.

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2. Anticipating last-mile logistics challenges with predictive analytics

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Probably the most important aspect of AI in your last-mile logistics is predictive analytics.

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These systems and algorithms can analyse immense amounts of historical and real-time data, like traffic patterns, weather, and delivery success rates.

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Likewise, they even include customer behaviour to optimise your delivery routes and schedules.

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AI can analyse these historical patterns to identify potential issues before they arise, allowing you to take steps to tackle the problems proactively.  

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Some of the biggest delivery companies in the world, like FedEx, use AI-powered route optimisation tools to adjust delivery routes in real-time.

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You can use these systems to analyse data from GPS devices, traffic cameras, and historical trends to identify the fastest and most fuel-efficient routes, reducing delivery times and operational costs.  

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2.1 How Amazon uses predictive analytics to increase customer satisfaction

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One standout example is Amazon’s use of predictive analytics to anticipate delivery failures.

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Amazon’s algorithms analyse huge amounts of data from different sets (such as customer delivery preferences, past delivery attempts, and even local events) to predict the likelihood of a failed delivery and adjust schedules accordingly.

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They’re not just reducing the need for costly redelivery attempts, they’re improving customer satisfaction every time a package arrives on time, when expected.

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However, the effectiveness of these algorithms depends on robust data feed management.

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Without clean, accurate, and up-to-date data, even the most advanced AI models can struggle to provide tangible insights.

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You’ll need systems that can aggregate and process data from multiple sources, ensuring both AI and actual human stakeholders have the information they need to make informed decisions.  

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3. Micro-fulfilment centres bring inventory closer to customers

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We’re well past the days of using big centralised warehouses as the sole basis for inventory.

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The future of last-mile logistics lies in micro-fulfilment centres (MFCs), and AI plays an important role in making them effective.

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These compact, strategically located warehouses are designed to store high-demand products closer to customers, reducing delivery distances and times.

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AI can be used to work out just where the MFCs should be, what inventory to stock, and how to manage supply chain flows.  

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3.1 Walmart’s investment in MFCs pays off

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Let’s look at Walmart’s example in a bit more depth. The retail giant has invested heavily in AI-powered MFCs to support its growing e-commerce business.  

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Walmart’s AI systems analyse data on customer purchasing patterns to predict which products are likely to sell out in specific regions and ensure those items are stocked in nearby MFCs.

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This not only speeds up delivery times but also reduces the need for long-haul transportation, cutting costs and environmental impact.  

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4. The cost-benefit dilemma of last-mile logistics

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Many experts in the industry jokingly refer to last-mile logistics as a financial black hole.

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Especially nowadays, the investment imperative is high. Not only do you have to pick the right AI API, but you also have to worry about specific issues, such as localisation.

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With solutions like localisation APIs and real-time translation (like Google managed to do with its new earbuds), things are a bit easier, but not less hard on your wallet.

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Of course, that’s the ebb and flow of keeping up with the latest breakthroughs. Sure, there are a lot of options on the table, but can you withstand the costs?

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Human workers are still going to be involved in many steps, and they’ll need to be trained in how to use all these systems.

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All your interconnected networks in your ever-expanding IoT will need enhanced Wi-Fi security. And, of course, you’ll need to constantly monitor and optimise your AI processes as you go, which costs a lot.

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But if you time everything correctly and make the right decisions, it will be a worthwhile investment.

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4.1 Dynamic pricing through AI

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We’re probably all familiar at this point with dynamic pricing because of services like Uber, but similar principles can be applied to last-mile logistics.

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AI-driven pricing models can analyse factors like fuel costs, driver availability, and delivery urgency to determine cost-effective prices for every delivery.  

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Uber is already applying these principles to their logistics arm, Uber Freight.

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The company uses AI to adjust shipping rates in real-time based on market demand and supply.

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During periods of low demand, prices may drop to incentivise customers to choose slower delivery options, allowing logistics providers to consolidate shipments and reduce costs.

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It works in reverse as well. During peak times, prices can adjust to reflect the higher demand for expedited services.

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This dynamic approach ensures that resources are allocated efficiently, driving down overall logistics costs.  

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Conclusion

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AI is quietly transforming the logistics industry, turning inefficiency into opportunity and setting new standards for speed, cost, and reliability.

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If you want to unlock a new era of logistics efficiency, you need to start leveraging predictive algorithms, micro-fulfillment centres, dynamic pricing, and the many other capabilities AI is going to unlock.

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This isn’t just a story about technology; it’s a story about how AI is reshaping the way we live, work, and receive the goods we depend on every day.

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For businesses willing to embrace these tools, the future of last-mile logistics has never looked brighter.