AI for Smart Parcel Lockers. Optimizing Routing, Scheduling, and Predicting Pickup Times in Last-Mile Delivery Process

As we observe the exponential growth of online shopping, the last mile of delivery is becoming increasingly important. This is the final leg of the journey from the warehouse or store to your doorstep, and it can be a costly and time-consuming process. To ensure that packages are delivered on time and without issue, optimizing the routing, scheduling, and prediction of pickup times for smart parcel lockers is essential. With advances in artificial intelligence (AI), this process can be streamlined and improved significantly.

This article explores how AI can be used to optimize last-mile deliveries to smart parcel lockers.

Home Delivery vs. Smart Parcel Lockers

There are two main types of last-mile delivery: home delivery and smart parcel locker delivery. Home delivery is when a parcel arrives directly on your doorstep, while smart parcel locker delivery is when packages are delivered to a secure locker that can be accessed with a code or key. Both methods have their own advantages and disadvantages.

Home delivery is typically more expensive than smart parcel locker delivery, as it requires additional labor to transport the package from the truck to your doorstep. In addition, home deliveries are often subject to weather conditions and traffic, which can delay or even prevent the package from being delivered on time. Furthermore, there is a fair chance that they won’t be present at the delivery time, resulting in a failed delivery. Failed deliveries are costly for all parties involved, inconvenient for customers, and cause more strain on the environment than necessary. However, home deliveries are generally more convenient for customers, as they do not need to travel to a specific location to pick up their shipments.

On the other hand, smart parcel locker delivery is often more cost-effective than home delivery, as it requires less labor to transport multiple packages from the truck to the destination locker. In addition, smart parcel lockers are typically located in high-traffic areas, making it easier and faster for customers to pick up their shipments whenever possible. Smart lockers not only enable 24/7 pick-up options but also support drop-off and returns of parcels. Thanks to that, some part of the labor connected to last-mile delivery is transferred over to the customer.

The Increasing Popularity of Smart Parcel Lockers in Last Mile Logistics

While home delivery is still the most popular method of last-mile delivery, smart parcel locker delivery is becoming increasingly popular. This is because smart parcel lockers are more cost-effective, convenient, and eco-friendly than home delivery. In addition, smart parcel locker delivery is often faster and more reliable than home delivery.

Artificial Intelligence for Smart Parcel Lockers

As the popularity of smart parcel locker delivery increases, it is needed to manage them more efficiently. AI can optimize the route, schedule, and prediction of pickup times for smart parcel lockers. By doing so, we can make sure packages are delivered on time and without issue.

Dynamic Parcel Routing System

A dynamic parcel routing system is a type of software that uses artificial intelligence to optimize the route of a delivery driver. This system considers traffic data, weather conditions, and the number of packages to be delivered. By doing so, it can calculate the best possible route for the driver to take. This not only saves time but also reduces fuel consumption and emissions by limiting unnecessary driving.

There are a few different types of base route optimization models: the traveling salesman problem, the vehicle routing problem, and the pick-up and delivery problem. The traveling salesman problem is a classic optimization problem that involves finding the shortest possible route that visits each destination exactly once. The vehicle routing problem is similar to the traveling salesman problem, but it also considers the vehicle’s capacity. The pick-up and delivery problem is an extension of the vehicle routing problem that includes picking up and delivering packages along the route.

Distributing and picking up either undelivered or shipped parcels (retrieving parcels) back to distribution centers for the smart parcel lockers network is a critical but challenging task for the smart locker industry. Finding an optimal route for shipments to be delivered and picked up, considering vehicle capacity and traffic congestion, is a classic problem where machine learning brings a competitive edge.

Smart Delivery Scheduling

The problem of last-mile delivery has one additional dimension that adds complexity to the vehicle routing problem - time. In last-mile delivery, packages must be scheduled for delivery to minimize the number of courier visits to an individual parcel locker, optimize delivery times and reduce labor and fuel consumption needed to fulfill deliveries. This is known as the smart scheduling problem.

Smart scheduling uses artificial intelligence to schedule delivery times and suggest a delivery route for smart parcel lockers. This system considers the number of packages to be delivered, the location of the smart parcel locker, and the availability of delivery drivers. By doing so, it can determine when each package should be delivered. This not only saves time but also reduces fuel costs and emissions along the supply chain.

Predicting Pickup Times

Understanding how each customer interacts with parcel lockers is important to provide an optimal locker experience and minimize the cost of smart locker operations. The history of deliveries connected with a particular phone number/email account is a gold mine of information that can be used to understand customer behavior better and optimize the smart locker network.

What can the data tell you? You have pickup patterns connected to the parcel locker addresses. You have shipper/retailer data, end customer address, customer working patterns, or even understand whether they use a car or pickup parcels on foot.

Segmentation of parcel lockers is also crucial. You can connect certain addresses with domestic areas, shopping malls, communication centers, or office districts by getting a better understanding of where specific parcel lockers are located and how people usually pick up their deliveries. You can also identify popular parcel lockers or when more parcels will be sent to each locker. This infers the type of customer that is likely to use a particular smart locker.

Predictive analytics additionally take into account the time of day, day of the week, and holiday schedules. This data can be used to predict when a smart parcel locker will be used and how long it will take for a package to be picked up. This information can route parcels, schedule courier visits, and optimize the delivery process even further.

Related case study: Implementing AI model to optimize routes and timelines of deliveries

A company from the logistics sector approached us to create a custom AI model that optimizes routes and the scheduling of deliveries.

Our challenge? The key challenge here was to prepare a dedicated AI-based system designed for carriers to optimize delivery time depending on the destination address. Thanks to the model we managed to reduce failed and late delivery rates by 30%. Read our detailed case study of this project.

Predicting Locker Filling Rate (Parcel Locker Capacity)

Locker filling rate is another critical metric that smart locker delivery companies need to optimize. If a smart parcel locker is not being used often, it may be unnecessary to keep it in operation. On the other hand, if a smart parcel locker is being used too often, it may become full and unable to accept new shipments. Setting up additional modules or installing new parcel lockers nearby may not be the optimal answer.

Machine learning solutions can be used to predict locker capacity rates together with accurate forecasts of pickup times and adjust the scheduling of deliveries accordingly. Understanding parcel sizes and weights and connecting them with final lockers are also crucial pieces of information when planning parcel locker capacity. By applying historical data on locker usage, last-mile delivery companies can get more accurate forecasts, avoid filled-up lockers, and optimize their smart locker networks.

The information about the predicted locker filling rate can be used at the moment of ordering delivery. Customers could be asked to select a different parcel locker upon finishing their order if the prediction model forecasts limited space at certain parcel lockers. This kind of feature increases customer satisfaction by providing transparent information and proactively suggesting alternative best solution.

Predicting Failed Deliveries

Another important metric that smart locker delivery companies need to optimize is the number of failed deliveries. A failed delivery occurs whenever a customer fails to pick up the parcel within the limited time frame. No one is happy with a failed delivery. The costs of returning the parcel back to the retailer are high, and the customer is left without their purchase. Parcel locker capacity has limited space that has to serve more parcels, so you cannot extend waiting times indefinitely.

This is where artificial intelligence can help. By analyzing past data, smart locker delivery companies can predict which addresses are more likely to result in a failed delivery. This information can be used to schedule deliveries to these locations at times when it is more likely that someone will be able to pick up the parcel. In this way, smart locker delivery companies can minimize the number of failed deliveries and improve customer satisfaction.

Parcel Consolidation for Parcel Lockers

With the growth of eCommerce and the growing popularity of free-of-charge delivery services, we notice that some customers order multiple shipments within a single day. Consolidating these multiple parcels into a single, smart locker is another possible optimization. This would mean that the customer would have to make only one trip to the smart locker to pick up all their parcels and that the shipments would use only a single locker.

The smart locker delivery company needs to take into account the size of the smart locker, the number of packages that need to be delivered, and the time it will take to consolidate the packages into a single, smart locker. In some cases, delivering each parcel to a separate smart locker may be more efficient.

This decision can be made by using artificial intelligence to predict how long it will take to consolidate the parcels and compare this with the time it would take to deliver each parcel separately. By making this decision in real-time, smart locker delivery companies can optimize their resources and increase customer satisfaction.

Determining the Location of Parcel Lockers

All of the above solutions can be applied daily to improve last-mile logistics. But there is another important challenge that can be solved with machine learning: where should parcel lockers be located? As the smart parcel lockers market grows, the need for new locker locations appears.

The location of the parcel locker affects the number of customers that will use the locker, the number of parcels that can be delivered, and the km that need to be traveled. It is therefore important to choose the optimal location for a smart parcel locker.

Historical data need to be taken into account when choosing the new location, with available data points such as:

• identifying popular parcel lockers,
• customer density,
• usage and filling rates of nearby lockers,
• traffic patterns,
• distance to retail stores,
• area type - closeness to residential and commercial buildings,
• distance to warehouse or distribution center,
• distance to other parcel lockers.

By applying data analysis to these factors with artificial intelligence to find the optimal location for lockers within the smart locker system. By choosing the right spot, delivery companies can meet customer demands and deliver more parcels.

Adding Value to Smart Locker System

Smart parcel locker delivery is not only more cost-effective and convenient than home delivery, but it is also more eco-friendly. Thanks to advances in artificial intelligence and machine learning, we can now optimize the route and schedule and apply predictive analysis to accurately forecast delivery time, pickup times, or capacity rates for smart parcel lockers. This makes sure that packages are delivered on time and without issue.

If you’re looking for a more efficient way to manage your logistics services and business processes, look no further than artificial intelligence. With the help of artificial intelligence and big data analytics, you can improve your locker system and make it even more convenient for your customers. Thanks to route optimization, smart scheduling, and predictions, you can ensure that your smart parcel locker system runs at its best. So why not give AI a try? You might be surprised at how much it can help you improve your smart parcel locker system. Contact us today and discuss various machine learning solutions with our AI experts.