Logistics demand forecasting is a fundamental instrument to evaluate and optimize the supply chain. Companies chose this way to make a business process predictable even in uncomfortable circumstances.
For the logistic industry, it is in some way difficult to implement digital changes for demand forecasting. It happens because logistics used to work with pen and pencil and be flexible in analytical planning. Nevertheless, implementing AI and other digital tools for demand forecasting is a substantial step in improving.
All logistic companies produce many data that can help to optimize the supply chain and other business activities. It is challenging not to use all the developed modern resources for logistic demand forecasting. New predictive technologies give a chance to build an accurate demand forecasting model for capable logistics. Let’s focus on the top benefits the company can get with AI and digitalization in demand forecasting.
The benefits of AI in a company’s supply chain
All the logistic companies already collect data that can work for the business. At the same time, it is not enough only to collect data. It is also a need to analyze and use this data for predicting the demand. It can improve the supply chain, improve cargo vehicle carpathite utilization, find what they need to do in case of unexpected demand, understand the problems companies have in logistics, and the ways to solve them. All these processes can be automatized with digital technologies.
Artificial intelligence can improve the predictable process and safe workforce and time for the logistic industry. Let’s discuss the benefits of implementing AI into logistic demand forecasting.
Read also: How to Use AI in Logistics?
The company that does not analyze the data faces many challenges, primarily because of unnecessary costs. They can have incomplete trucks, avoidable logistic operations, or inefficient technical mending. As a result, they will lose money and time in the process.
Moreover, AI can analyze all the data from unsuccessful operations and predict activities for improving the process. It could be decreasing in fleet size, leasing, parking, and driving cost. For example, courier company Speedy from Bulgaria reduced hub-to-hub cost by 25% by implementing AI technology in logistic demand. They used augmented intelligence to detect unnecessary transportations and canceled them imminently.
Increase Employee Efficiency
AI algorithms can predict the granular demand for every origin-destination, and it will help to save time for the employees. In case the employees have more time and ready decision in planning the logistic activities, they can be more efficient in operations.
AI also can compare the planning decisions made by humans and algorithms and use this data for future machine learning. In some way, it will also improve the demand forecasting and help to make accurate decisions during the logistic process. For example, the AI can define if the drivers need to take a break or finish the chain. With numbers and data analysis, this decision helps to optimize employee efficiency and rationalize time spent.
Optimal Fleet Repositioning
It can be difficult for the company to manage all trucks, containers, and space they use, mainly if the company works in different countries and has thousands of units. In this case, implementing artificial intelligence for planning the company’s work and utilizing all resources to carpathite is the best option.
For example, the TIO trailer service works in 70 locations in Europe. Their problem was in fleet management. After implementing AI technology, they could respond to asset demand more carefully. This analytical work increased the income by 11% and made the company more flexible for the clients.
Selling Extra Logistics Assets
The biggest problem for logistics is not to use any available space in trucks. Half of the trucks in the EU were traveling empty on their way back after delivery. For business, it means that the trucks are losing money while they have a chance to earn them. The problem can be solved with AI for optimizing operations in logistics and planning the activities.
At the same time, logistic operators need to have safe stocks of assets everywhere. If the AI will optimize the operations and companies could solve the activities they do not need. With demand forecasting, the company will know which assets they will need and rent for a time. The AI goal to optimize the supply chain can improve the usage of different assets and define the pick periods when they can lease their assets.
The next benefit of using AI in demand forecasting is the ability to form dynamic pricing for services. Many circumstances influence the cost of logistics, such as seasonality or the contract that will cause the empty returning of trucks.
With AI, the company can form the price for every case and be sure that all possible facts of the inevitable logistic process were taken into consideration. This model of demand forecasting is essential for business and its competitive abilities.
The ability to have dynamic pricing is one of the most valuable benefits in predicting the activities, as it helps to save money and be flexible in managing the cost for the clients.
As we can see from all the digitization benefits, logistic demand forecasting is a complicated but efficient instrument. This tool uses an augmented intelligence approach. We incorporate artificial intelligence in developing the best decision options and the human brain for the final decision to use or not this plan or actions. The combination of the two approaches is the best way for technical supply and activity prediction.
AI already helps companies to reduce costs by 15% and save time and money. The companies can create long-term and short-term goals and use a demand forecast to optimize these goals. Using artificial intelligence tools can make the business successful and more efficient than ever before—the ability to know what and when will be needed is crucial for goal achievement.
Using AI for demand forecasting will also boost customer satisfaction and drive better performance. Implementing AI technologies for logistics is already a part of strategic planning for all big companies.
Chief Innovation Officer (CINO)/Product management, help to create startups from the idea to final release, addicted to inspiring ideas and great products.