How to optimize parcel delivery? This is a question logistics professionals have been asking for years. Only recently, there was a better way to optimize last-mile delivery than by simply optimizing the route. But now, with custom deep learning solutions that can optimize processes in real time, you can significantly reduce delivery costs and speed up delivery times.

The Last Mile Delivery and Big Data

The need for last-mile delivery optimization is steadily growing and becoming more and more complex. This is partly due to the increase in e-commerce orders and the growing demand for same-day and next-day delivery. Supply chain companies need to adapt their operations and optimize routes in real time.

 Recent advances in real-time data collection and processing technologies have prompted logistics companies to develop predictive systems to improve customer service and optimize logistics processes.

Previously, route optimization and delivery planning were time-consuming and frequently inaccurate manual processes. 

What is parcel delivery optimization, and why is it important?

Parcel delivery optimization reduces parcel delivery costs, streamlines scheduling, and speeds up delivery times using big data and machine learning.

Last-mile logistics and parcel delivery provide unique obstacles to traditional supply chain management.

Last-mile delivery performance challenges

We typically mean sending small parcels and medium-sized commodities as discrete shipments when we talk about parcel delivery. Postal systems, couriers, and smart package lockers all provide this service. Parcels are usually light and easily handled by one person. A typical packet comes in a cardboard box or envelope with economical protective materials.

Sometimes, drivers have to travel long distances to make one delivery. This increases fuel consumption and vehicle wear and tear, raising delivery costs.

Finally, the “last mile” problem with parcel deliveries is traffic. Many businesses and domestic addresses are located in city centers with high traffic congestion. This can cause delays in delivery and lead to increased fuel consumption as drivers idle in traffic.

Senders can be both small businesses and large companies that need to send goods locally. However, some parcels are still shipped internationally. With the increasing number of warehouses and the availability of distribution centers, shipper companies are streamlining their processes to ship from nearby warehouses rather than from a manufacturing site or central warehouse. This saves time and money and reduces the carbon footprint.

All of these issues make it challenging to optimize parcel delivery routes and minimize costs. But with machine learning, it’s possible to overcome these challenges and optimize parcel delivery. Machine learning can predict traffic, plan the optimal route for each shipment, and even choose the optimal time for delivery.

The Courier Delivery Problem

The courier delivery problem is a classic vehicle routing problem with many real-world applications. In its most general form, the problem is to find the shortest or cheapest route that visits a given set of customers and returns to the depot. The problem can be further constrained by adding time windows, capacity constraints, and other conditions that must be met.

The operations research community has studied the courier delivery problem extensively, and many different graph algorithms have been proposed to solve it. However, most of these algorithms are well suited for scheduling and autonomous optimization but are not predictive in nature. They cannot self-optimize based on the predictions they provide. Recent advances in machine learning and data science have led to the developing of new deep learning methods for solving the courier delivery problem that can optimize the delivery network in real-time based on multiple predictions.

Predicting delivery times

Another important example of using machine learning to optimize parcel delivery is predicting delivery times. This is crucial information for both customers and companies because it allows them to plan their day and make sure that someone will be available to pick up a package. It also helps companies track their shipments and ensure that they are delivered on time.

Tracking shipments is one thing, but accurate time windows for parcel delivery are quite another, and they are needed to meet growing customer expectations. Customers are now demanding transparent tracking of their packages at the last mile. But as they order time-sensitive items, the need for accurate delivery time predictions becomes even greater.

To provide accurate predictions, machine learning models take into account a number of factors, such as the schedule of assigned drivers, delivery distance, number of necessary visits to the warehouse, road conditions, and so on. This information can be gathered from GPS data, weather forecasts, social media data, and other sources to provide even more accurate information for end users and even more visibility with updated arrival time notifications that improve customer satisfaction.

Predicting failed deliveries

When we discuss delivery time forecasting, it’s important to note that we assume that someone will be at the delivery address to receive a package at a particular time. But what if they don’t turn up at home or the business is already closed? Unsuccessful deliveries significantly increase the cost of the entire process, not only because a second attempt is required but also because undelivered packages must be returned to the warehouse and eventually back to the sender.

This is where machine learning can help by predicting failed deliveries before they happen. Models can take several factors into account, such as customer address, delivery type, historical parcel delivery data, and so on. This information can be used to predict whether a particular delivery will fail and adjust delivery schedules when scheduling deliveries.

Dynamic pricing for last-mile delivery

Often for the end consumer, the cost of shipping is either none at all (most stores offer free shipping options) or meager (e.g., a few euros/dollars). This creates a big problem for delivery companies and shippers, as their operating costs are much higher.

Traditionally, parcel rates are either static (within a specific range of dimensional weight parameters) or calculated based on the size and weight of the parcel, the origin, and the destination and passed on to the shipper. But, as we all know, these factors don’t give a complete picture of how much it costs to ship a particular parcel to a particular customer or how to properly construct the cost of the parcel.This is where machine learning can help, dynamically pricing each delivery based on a number of factors such as distance, size, supply and demand, and so on. This information can be used to calculate a real-time price for each delivery that reflects the actual cost of delivering the parcel. Dynamic pricing is when the price of a good or service fluctuates in real time based on demand. Dynamic pricing models are widely used in logistics and trucking. With this approach, transportation companies can evaluate their approach to pricing strategy and make it more efficient with a higher rate of return.

Large shippers, on the other hand, can use this information to gain more control over delivery costs and carrier performance, forecast delivery budgets, help select a carrier, and optimize delivery operations. They can also use this information to skillfully manage multiple parcel delivery carriers.

Improving smart parcel storage systems

When we talk about last-mile delivery optimization, we can’t forget about smart parcel storage systems. They are becoming increasingly popular in the parcel market because they offer a number of benefits to both customers and businesses.

For customers, parcel lockers provide a convenient way to pick up their parcels because they can pick them up at their convenience, which increases overall customer satisfaction.

For businesses, parcel lockers provide a competitive advantage for efficient delivery management because parcels can be delivered to one location and then sorted and distributed to individual lockers. This approach reduces missed deliveries, failed attempts, and the overall cost of last-mile delivery.

Machine learning can help improve intelligent parcel storage systems in several ways:


  • Smart delivery planning (route and schedule optimization)
  • Predicting pickup times
  • Predicting cell occupancy rates
  • Predicting failed deliveries
  • Parcel consolidation optimization
  • Locate new parcel lockers

All of these factors can be taken into account to make parcel storage systems more efficient and convenient for both customers and businesses. 

How do deep learning models improve customer satisfaction and benefit carriers?

The goal of any business is to satisfy its customers and increase sales. In the case of parcel delivery, this means delivering parcels on time (on an ever-tightening schedule) and in good condition. Recent advances in machine learning and the growing popularity of deep neural network techniques allow carriers and shippers to develop predictive models that can predict various parcel delivery events. Unlike basic regression models, which can be used for simple forecasting, deep learning models can account for many different data sources and make much more accurate predictions and automate advanced decision-making.

Optimizing last-mile delivery is a complex problem that requires a holistic approach to automation and data analysis. It involves integration with all other logistics systems to ensure end-to-end cargo visibility and, in many cases, the ability to process data in real-time. AI is already invading the logistics sector, though not at the same pace for all market players. The current problems of logistics and transportation companies and the inability to move the digitization process is primarily rooted in legacy systems and settings in use. Legacy ERP and TMS do not provide the insight that could provide a competitive advantage or resiliency during the market disruptions we are now seeing.

Many supply chain companies today are transforming their IT ecosystem and moving to cloud providers to use the data they collect and apply it to improve their business. This is also a moment to properly plan for your future AI implementation. You need to approach the digitization process carefully to meet the needs of machine learning models, such as real-time access to data and complete visibility into business operations.

Suppose you want to take full advantage of machine learning and other advanced technologies that can help you optimize your transportation operations. In that case, you need to systematically abandon outdated systems. This is the only way you can improve the efficiency of your business, serve your customers better and stay competitive in the marketplace.

If you want to learn more about how machine learning can help optimize your last-mile delivery operations, don’t hesitate to contact us. Our team of AI experts is ready and willing to work with you to create an exceptional AI-powered solution that will improve efficiency and customer service.