In a world where online shopping has become the norm, timely and accurate last-mile delivery is more critical than ever. Unfortunately, this final step in the delivery process is also one of the most expensive and complicated. Fortunately, artificial intelligence is ready to help. AI is already revolutionizing logistics and supply chain management, and it can help optimize last-mile logistics and reduce costs.
Why is last-mile delivery so expensive?
The high cost of last-mile delivery is due to several factors. One of the main reasons is that last-mile logistics is complex. Many variables must be considered, such as traffic congestion, route restrictions, and customer preferences. In addition, the last-mile is often the most expensive part of the delivery process because of fuel and labor costs.
Finally, companies must consider the cost of failed deliveries, returns, and reverse logistics when planning shipments. These factors make it difficult to optimize last-mile delivery and reduce costs.
The rise of e-commerce and food delivery
The growth of e-commerce has only made last-mile delivery more difficult. With the increase in online sales, the demand for timely and accurate delivery is rising. Customers expect same-day and even hourly delivery options.
Seasonal logistics challenges
Another challenge last-mile delivery providers face is the seasonal nature of their business. Demand for deliveries can vary significantly from month to month or even week to week, with the highest peaks occurring on global holidays and commercial holidays. This makes it difficult for companies to plan resources and schedule deliveries efficiently for specific shopping periods.
How can companies optimize shipping operations?
To optimize delivery operations, you need a system that can effectively manage all of the variables involved in the process. This is where machine learning comes to the rescue. Machine learning algorithms can learn from data and improve over time. This makes them ideal for optimizing complex processes, such as last-mile deliveries.
There are several different machine learning algorithms, but some of the most commonly used in logistics are deep learning models based on neural networks and decision trees. Neural networks do well with complex data sets, and decision trees do well with identifying patterns in the data. Both of these algorithms have been successfully used to optimize last-mile delivery operations.
Machine learning can optimize last-mile delivery by predicting customer preferences and routing vehicles accordingly. Artificial intelligence algorithms can also predict traffic patterns and congestion levels, allowing companies to plan their routes accordingly.
In addition, artificial intelligence algorithms can help determine when deliveries may fail or be returned. This information can improve the planning process and reduce costs.
One of the most critical aspects of last-mile delivery is route optimization. A well-optimized route can save time and money. When planning a delivery route, there are many factors to consider, such as traffic congestion, customer preferences, and resource availability.
Machine learning algorithms can be used to optimize delivery routes. For example, a deep learning model for route planning can be trained on historical data from a company’s order management system to suggest the most efficient route for a given set of deliveries and associated variables. This data might include customer addresses, order details, and delivery times. The model then uses this information to create route optimization suggestions.
Another vital factor in last-mile delivery is planning logistics operations. Planning affects everything from the number of delivery drivers to the number of vehicles needed. It’s essential to schedule deliveries to use resources efficiently and for customers to receive their orders on time.
Artificial intelligence can be used to optimize delivery schedules. For example, a decision tree can be trained on historical data from a company’s order management system. This data could include customer addresses, order details, and delivery times. The tree would then use this information to create suggestions for optimized schedules.
Another challenge in last-mile delivery is pricing. It’s essential to price the order correctly to make a profit and, at the same time, provide excellent value to the customer. Brokers are phasing out brokers in favor of automated machine learning algorithms that help businesses choose the most appropriate carrier to improve efficiency and lower delivery costs. Dynamic pricing in logistics is a topic we covered in detail in our previous article.
The latest frontier in last-mile delivery is autonomous delivery. With the rise of self-driving cars, it’s only time before we see autonomous trucks or dedicated delivery robots. Autonomous vehicles can navigate the streets on their own and deliver packages to customers’ homes. This technology has the potential to revolutionize last-mile delivery. There are many challenges to overcome before this becomes a reality, but there is no doubt that autonomous deliveries will play an essential role in the future of logistics.
Many companies are working on autonomous delivery vehicles. Notable examples include Amazon, UPS, and Google. More companies are likely to enter this field in the coming years.
Chatbots to improve customer service
Chatbots are typically used to support customers or sell products and services. Recently, there has been a growing interest in using chatbots for last-mile delivery.
Chatbots can be used to provide customers with information about their orders. For example, a chatbot can provide customers with a tracking number for their orders. This will allow the customer to track the progress of their order online. Chatbots can also be used to answer customers’ questions about their orders. For example, a chatbot can help a customer solve a problem with their order. This will help increase customer satisfaction with the company’s delivery service.
The future of last-mile delivery is very promising. With the help of artificial intelligence, logistics professionals can optimize their routes and schedules never before possible.
With its ability to analyze large amounts of data and identify patterns, machine learning can help companies optimize routes and schedules in ways that humans could never do alone.
Our team of AI experts has extensive experience developing AI-based software solutions for logistics and supply chains.