Artificial intelligence has shaken up many industries, giving professionals more ways to do their jobs better by streamlining processes and reducing costs. Some have explored how to apply it to logistics demand forecasting since so many variables influence what customers need and when. How can these professionals get the best results with these relatively new applications?

Read also: Artificial Intelligence – How it is Shaping and Redefining Logistics

Improving Deliveries During Peak Times

Demand forecasting can become more complicated as customer bases grow and the number of products increases. Operations are even more pressured when there are more items to move from place to place.

Amazon’s AI applications illustrate various compelling ways to apply the technology for smoother workflows. For example, the company’s demand forecasting tool uses machine learning to evaluate more than 400 million offerings daily, determining the necessary quantities to keep in stock at various locations. Those calculations improve distribution efforts and get parcels to customers sooner. It integrated deep learning into its demand forecasting tool about a decade ago, resulting in a 15-fold accuracy increase over two years.

Amazon’s team uses demand forecasting to coordinate shipments with millions of worldwide vendors to restock the brand’s inventory. Such behind-the-scenes steps ensure customers’ orders arrive promptly, even during the busiest times of the year.

The e-commerce retailer site also uses AI to manage the demand associated with parcels arriving at each delivery station. Those locations are the last stop for packages before they reach customers’ doorsteps. A Massachusetts facility receives up to 65,000 packages on an average day and as many as 100,000 during peak demand.

AI helps logistics professionals predict things they’d otherwise not know, such as that a truck will arrive at a delivery station ahead of schedule. Additionally, more than 20 machine learning models optimize drivers’ routes. These workers also use generative AI to clarify delivery notes or other contextual information that makes the difference in packages being delivered on time or late.

Showing the Impacts of Specific Outcomes

Excelling at logistics demand forecasting does not mean making perfect predictions. Rather, it involves creating a range of possibilities to inform professionals’ strategic decisions. Anything from an unexpected storm to a celebrity wearing a specific designer’s product to a highly anticipated event could cause demand spikes that affect logistics teams.

Decision-makers increasingly use AI-powered digital twins to test various scenarios in a virtual environment before potentially experiencing them in real life. Those tools can show users what would happen if a labor strike, pandemic, major natural disaster or other unexpected event affected their logistics operations and elevated demand due to the sudden scarcity. Then, even though people cannot anticipate those supply chain shocks, they can prepare for them if they happen.

Such information increases accuracy because it prevents uncertainty about the aspects within people’s control. Logistics professionals are not scrambling to cope with present scenarios they never considered because they have run them through the digital twin.

Staff can also apply what they learn from data analysis tools to their digital twin usage. Perhaps executives want to study various ways to reduce emissions. They may have already chosen microdistribution centers as strong contenders for their plans. Research indicates those facilities will cut emissions by up to 26% by 2025. How could leaders maximize the results by putting distributions in one place versus another? Digital twins and AI can reveal those critical conclusions.

In the ideal scenarios, leaders can cope with demand while making other advantageous changes, whether they result in decreased emissions or better driver productivity. AI-enabled digital twins remove the guesswork that frequently occurs when professionals weigh the pros and cons of different possibilities. Although digital twins cannot predict the future, they can reveal the ripple effects of specific decisions.

Reducing Freight Inefficiencies and Unnecessary Costs

Logistics demand forecasting also applies to transportation lane efficiency. Sometimes, shippers must pay more than expected to move their products, especially if freight professionals perceive the goods as less desirable.

However, one manufacturing enterprise took a better approach by combining AI with operations data, including demand forecasting. The enterprise uses this method to manage more than 300 transportation lanes. The technology reviews which aspects to tweak to keep everything running efficiently. For example, a high-demand item takes precedence for space in a truck over a less-popular one.

The system analyzes activities across all transportation lanes, accounting for the priority levels of each offering based on demand and truck-related constraints or costs. It then determines the optimal load number based on which items must ship and the associated expenses. Before this AI-based approach, the typical assumption was anyone who needed a truck could get one. Even when that was true, some shippers had to pay huge prices to meet the present demand.

The technology also establishes incentives or penalties for cases where people transport items outside the time or cost windows recommended by the platform. Moreover, users can see a 30-day forecast that simulates loading to determine which goods will depart during that period.

One reason this setup works well is the AI communicates with other tools the business uses. An example of that in practice is communications between the artificial intelligence planner and a transportation management system. Those exchanges result in vehicles reserved and tenders created for the goods that will ship over the next month.

Lastly, the system determines the specific contents of each load as late as possible in the process, driven largely by demand. This method reduces delays and maintains a priority-based system for items.

Making the Most of Logistics Demand Forecasting

These examples show how logistics demand forecasting enhanced by AI can pay off for executives choosing to use it. However, anyone considering that approach should begin by considering which goals the technology can help them meet. Which demand-related mistakes have they made most recently that artificial intelligence may have prevented?

People must also determine their budgets and the scale of planned tech rollouts. Will a company proceed with a small-scale trial before potentially increasing its tech utilization based on the outcomes? Addressing employees’ concerns and questions will also help plans succeed. Many resist change initially but become more open to it when they see how the new tech will make their jobs easier or more impactful.