How AI & ML Tech Can Help Handle Delivery Peaks

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        Logistics is the process of delivering several packages requested by many different customers. Retailers in the past had their own logistics departments, but these days large scale companies that handle deliveries are more common. As logistics operations involve a lot of changes happening at a fast pace, it is difficult to handle deliveries automatically especially during peak times. Artificial intelligence(AI) and machine learning(ML) are the perfect technologies to integrate with logistics as they are capable of dealing with fast change.

Traditional methods of handling delivery operations involve product packaging, a focus on safe transport and a minimal route planning effort. As the logistics industry considered safety a more important priority than speed, those methods sufficed in those generations. But the times have changed and logistics in today’s world cannot rely on the traditional methods. There is now a demand that e-commerce delivery solutions take speed just as much a priority.

What is Artificial Intelligence and Machine Learning?

Artificial intelligence in simple terms is a machine’s ability to mimic natural intelligence. Artificial intelligence often combines various aspects of processing data, and storing the data as efficiently as possible. The technology is used to handle processes which are complicated for humans to handle. Based on the complexity of the AI being used, it can correct its own errors, and get better results with time. It does it with learning from large amounts of datasets and “learning” to spot errors and prevent them. The process by which this is accomplished is called machine learning.

Machine learning is considered to be a part of AI that is responsible for the machine’s ability to learn. Machine learning is responsible to create algorithms that automatically adjust to changes in the results. Machine learning is trained on what is called training data, which helps the algorithm to perform its task better. ML algorithms can be based on supervised learning models which then become automated gradually. Usually the algorithm is trained towards a particular goal.

Delivery management software’s route optimization is one such goal where the delivery vehicles are assigned the right routes based on various parameters. Weather, traffic, distance and fuel in the vehicle are some of the parameters that the algorithm takes into consideration while deciding routes. AI can handle multiple goals at once and find application in many industries including logistics. 

Read more: Boosting Delivery Management Business with AI

The case of flexibility, agility and delivery peaks

Logistics businesses in the present era must not only be fast and safe, but also flexible. The destinations of the packages can change en route when large companies are concerned. The delivery scheduling and dispatch software thus has to accommodate for these changes. At times of delivery peaks, like black friday the pressure is even higher. This is because every retailer will give discounts on these days which will attract more customers to the delivery service.

While that is good at a business standpoint, it also increases the risks considerably. Delay in one of the shipments may spell a disaster for many others and attract penalties to the logistics service.

Delivery peaks are times where the logistics service receives many orders at once. This happens during festivals and special occasions. Cyber monday, black friday and singles day are some examples of such situations. They tend to increase sales for the retailers and the logistics service, but the increase in orders increases the risk of mishandling the deliveries. A SaaS platform with AI and ML capabilities can be used to reduce such mishaps.  

Here is how AI and ML technology helps handle delivery peaks

It is important for companies to handle delivery peaks efficiently. A lot of risk and revenue is hidden in them, and hence such operations become extremely important for the logistics industry. Measures to reduce failures are taken by accurately predicting the number of orders and managing the orders efficiently. AI and ML also increase profits by offering techniques which are suited perfectly to the logistics industry. Following are the many ways in which AI and machine learning helps handle delivery peaks. 

Prediction of orders

AI and ML technologies can analyze various analytics effectively and thus prepare for the number of orders that arrive on delivery peaks. The type of items that sell the most, the resources required and possible risks in delivery are predicted by analyzing the trends over the years. Logistics tracking systems also track the common traffic conditions and failures that are more likely to occur on particular routes. This helps to prepare for the upcoming delivery peaks in the logistics business. 

Accurate and Precise ETA solution

Traditional methods of logistics used what is known as route planning. It involved listing the different destinations in order of distance and convenience. While this may seem effective, it leaves out many parameters on which the expected time of arrivals (ETA) of delivery packages. Many other parameters like the fuel consumption rates of different vehicles, weather and the traffic in the system affect the delivery systems greatly. AI and ML technologies which are way more powerful than humans can be used to handle such parameters automatically. They increase the visibility of the e-commerce delivery solutions as a whole.

Efficient packing

Packing can also be made more efficient by the use of artificial intelligence and machine learning. Two types of strategies are used in packing in logistics service. One is pre packing, where the materials are pre packed before the order comes in. normal packaging involves the packaging of items after the order is received. Sorting out the materials beforehand is measured and made efficient by AI and ML technology in the delivery management software

Load balancing 

Logistics is one of those industries that are always in short supply of needed resources. On the other hand, tasks like delivery orders keep piling up for the industry. Load balancing is the technical term for balancing the tasks with the minimum amount of resources required. This has traditionally been difficult to do, but with the introduction of AI and ML technologies, it has become commonplace. Artificial intelligence is responsible for load balancing in the last mile delivery software of most large scale logistics operations of the present era.

Batching

Clear visibility of the supply chain with efficient handling is necessary at delivery peaks. Delivery peaks also have certain orders that are similar. Such orders are grouped together and delivered in an efficient network of delivery vehicles. Batching is an area of research in the field of logistics and lots of improvement can be made in it. However, for the sake of efficiency, many logistics companies use some sort of batch processing mechanisms while delivering packages.

Batching helps reduce the resources required for the same task, but requires technology like AI and ML to work in an error free manner. This is because every case of batching is different, and a system that handles change well is a strict requirement to the process.

Read more: How Future Delivery Drones Will Deliver Your Packages

The right carrier capabilities

Many logistics companies offer a SaaS platform to the retailers. Software as a service or SaaS has to be very efficient in catering the needs of different types of individual retailers. Conventional softwares without machine learning capabilities find it very difficult to handle such things. Especially when the retailer is large and deals with delivery peaks often, choosing the right carrier becomes especially important.

Artificial intelligence and machine learning help improve these capabilities for a logistics company. The retailers that choose the logistics service during delivery peaks are thus helped regardless of what their individual difficulties may be.

Analytics

The logistics business deals with a lot of data on a day to day basis, and it is advantageous for the delivery service and the retailers if such data is analyzed. AI and ML because of its extensive capabilities can track such data. The analytics are then used for increasing sales and further optimization of tasks. Any area of growth or any anomaly can also be tracked by such technology. It also acts as a storage mechanism of order and sales data. 

Due to the recent covid-19 pandemic, logistics operations have had a large demand. Nationwide lockdowns and the risk of infection made people increasingly buy online. While this kind of a delivery peak has been unexpected, technologies have helped deliver critical supplies to various parts of the world.

Most such logistics operations are backed up by artificial intelligence and machine learning. Especially in the transportation of medical equipment, timing becomes a very important part of the process, and technology is actively helping the world deal with the situation.

Conclusion

Logistics business, as hard as they are, have had more demands from them. These demands can only be handled by incorporating the latest technologies like AI and ML integration. Large amounts of data can be organized and manipulated in real time with these technologies. Deliforce is a good delivery management system which helps track delivery vehicles in and streamlines the data back to the control center. It is equipped with the latest AI and ML technology that actively monitors all the logistics data. It anticipates any delivery peaks and adjusts the fleet management system accordingly.

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