11 benefits, use cases for AI in logistics

6 benefits of using AI in logistics

Some benefits of using AI in logistics are improved customer experience and potentially reduced costs.

1. Optimized capacity

Maximizing capacity -- the space that is available on vehicles, ships, planes and warehouses -- is always an important logistics consideration.

AI models can analyze shipment sizes, delivery schedules and contractual commitments, and then they can determine the most efficient way to pack goods into containers and vehicles, which can help reduce the number of trips needed and potentially lower fuel costs as well.

The same approach can help optimize warehouse space, with AI creating a warehouse plan based on predicted peak periods and slow seasons.

2. Better inventory management

AI can play an important role in maintaining optimum inventory levels at every stage of the supply chain and logistics process.

Models can analyze information such as historical sales data, seasonal trends and external factors, like weather or market shifts, and then provide information on short-term demand and long-term trends. Supply chain managers and logistics specialists can use this data to work together to avoid overstocking, which ties up capital.

3. Improved supply chain visibility

Visibility into activities across the entire supply chain and logistics networks, from raw materials to final delivery, can help minimize disruptions.

Because many of these activities involve documents such as purchase orders and delivery notices, LLMs can often extract and consolidate data more easily than traditional analytics systems. LLMs' capabilities can help managers identify emerging bottlenecks across the network before the bottlenecks affect operations.

4. Reduced operational costs

Cost savings in logistics usually come from improving the efficiency of shipping and warehousing. However, base operational costs are also significant, and AI-enabled predictive maintenance can help reduce some of these costs by extending the lifespan of equipment.

In warehouses and fulfillment centers, companies can potentially save money by implementing intelligent temperature and humidity controls that can also take into account the goods being stored.

5. Improved customer experience

Customer satisfaction with logistics rests largely on accurate delivery estimates and real-time tracking.

Across the logistics industry, most delivery estimates are based on fixed schedules. However, AI models are more flexible and can adapt to disruptions. Although delays may still occur, the increased accuracy of delivery estimates from AI can lead to fewer errors and higher customer satisfaction.

6. Proactive risk management and security

Risk management for logistics involves identifying any unusual patterns in shipping routes, suspicious transactions and potential fraud attempts. Security may cover cyberattacks, as well as the physical protection of personnel, vehicles and buildings.

Individual security systems are increasingly intelligent, and AI can combine the data from every security system and then identify any patterns of threats.

6 use cases for AI in logistics

AI has various applications in logistics that can help companies improve their overall operations.

1. Demand forecasting and planning

An important part of logistics planning is ensuring the right resources are available where and when they are needed without unnecessary movement of empty or undercapacity equipment.

AI can take a more comprehensive view of demand modeling than traditional analytics or business intelligence tools because of its ability to analyze data such as sales information, market trends and external factors, like weather and social media posts. The analysis can help logistics managers address emerging issues if needed.

2. Intelligent route optimization

Route optimization can be complex since it involves real-time data on traffic conditions, weather, delivery windows, vehicle type and capacity.

AI can take these factors into account when creating optimal routes and delivery sequences. This can be particularly helpful for air freight and last-mile delivery, which can involve frequent route changes.

3. Autonomous vehicle operations

Autonomous vehicles and drones, which have small AI models hosted on the device, are already affecting short-range logistics. If their use widens, autonomous vehicles could be particularly valuable in remote areas and areas that are congested.

Currently, semiautonomous trucks and vehicles include AI features, such as driver fatigue detection and collision avoidance.

4. Predictive maintenance

The logistics industry relies greatly on machines such as forklift trucks, container cranes and ships, so maintenance is a major expense.

AI and machine learning models can analyze sensor data from vehicles and equipment, including engine performance, patterns of wear and usage history. AI and machine learning models can then use that information to highlight potential failures.

5. Smart warehouse automation

Robots that handle tasks like picking, sorting and packing are now familiar examples of AI in warehousing.

Autonomous robots can navigate warehouse spaces and use computer vision to identify and pick items accurately.

6. Risk management and fraud detection

In logistics, signs of fraud are often subtle and can be suspicious patterns in orders, routing, deliveries and returns.

AI can analyze large quantities of transaction data and identify these patterns, which humans might not notice.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.