Examples of AI in Supply Chain 

Artificial intelligence is highly beneficial in managing various daily supply chain tasks, activities, and processes. It helps you to develop responsiveness based on demand, precise decision-making, and right SC planning. Today, we’ll discuss the top 10 examples of AI in supply chain management; and uses of AI-powered supply chain management.

Top Examples of AI in Supply Chain 

Some of the top 10 examples of AI in supply chain management or uses of AI-powered supply chain management are as follows;

Safety of Workers

AI has the capability to improve the workers’ and employees’ satisfaction levels by performing routine tasks like custom clearance, developing efficiency, and planning routes. In fact, it notifies employees to stay away from dangerous and life-threatening situations. While assisting the decision-making process, it makes sure the health and safety of their workers.

For instance, the AI system would send recalling alerts and notifications to delivery drivers in extremely hot temperatures. When a tornado or wind storm enters the area; they could take safety measures.

Efficiency in Last Mile Delivery

As we are aware of the fact that last mile delivery is a bit expensive option. It has the capability to decrease human errors and process data in seconds so that the company can efficiently bring improvements in last-mile delivery.

The most expensive and challenging stage of the supply chain is when you move products and goods from regional centers to the consumption point. The smart sensors of AI would gather and analyze data like environmental factors, traffic patterns, and weather data. It ensures the timely delivery of products and goods while amplifying and optimizing the delivery experience.


The AI system would source and integrate environmental data from various sources to support the measurement of carbon emission rate and generate reports.

BlueNode is an AI-based software that means carbon and scope 3 emission rates from trade authorities, shippers, rail carriers, maritime, terminal operations, and ports. Everstream Analytics has recently bought BlueNode to amplify its analytical capabilities and help users make data-based decisions; environmental impact, shipping time, balancing cost, and low carbon emission.

Processing Returns

AI and machine learning gather data and insight from the returned product find the underlying causes and develop patterns. It helps retailers and producers to make necessary adjustments and revise the product, packaging, or shipping method; which would reduce the return rate. AI has the capability to decrease the return product rate by offering them personalized recommendations.

Convenient Custom Clearance

AI has the capability to streamline custom clearance processes by filling out all the required paperwork. KlearNow AI is a custom clearance software developed by Custom Engine; it allows businesses and companies to efficiently move products and goods and comply with ports and customs.

The automated document digitalization and data extraction capabilities would remove manual data entries and other relevant errors and mistakes. It develops Importer Security Filings and sends them to customs officials and authorities.

Low Equipment Downtime

AI would recognize the small problems and issues in the tools and equipment that are handling the material and other suppliers. For instance, RIP (Railcar Inspection Portal) is specialized software from Duos Technologies; it employs AI and machine learning technology to quickly analyze the fast-moving vehicles. RIP recognizes shock absorber cushion unit heads, whether they are missing or damaged; and notifies you to replace them before any type of incidence occurs.

Quality Control

AI software and tools evaluate and analyze the product quality throughout the supply chain process. For instance, Spinframe is a vehicle inspection software and it employs machine learning, AI, and computer vision technologies. It develops a digital twin for vehicles and recognizes problems and issues throughout the supply chain ranging from assembly lines to dealerships and end consumers.

Efficient AI tools and software would notify and alert you about the damaged products in the customer order fulfillment stage. Currently, Amazon has implemented the AI technology in two fulfillment centers and the company plans to implement it in 10 more locations. It allows you to isolate damaged and faulty products before shipping them to the customers.


AI-based warehouses employ robotic technology in the warehouses to efficiently perform operations like picking, packing, and shipping. For instance, Symbiotic is an AI-based robotic technology for SC processes; it has robotic picking capabilities to perform various retail functions.

Load Planning

AI helps businesses and companies to manage loads and develop balanced transportation. It allows them to work with preferred carriers and make sure sufficient storage space and labor availability across the facility. For instance, LevelLoad is software that conducts an analysis of shipping patterns and predicts an increase in demand for the next month.

Inbound Logistics

AI system guides LSPs (logistics service providers) to optimize material movement from suppliers to the production facility. For instance, RoboDispatch is an AI software for automobile manufacturers. It helps them in the inbound logistical operations for the flow of empty trailers from suppliers to the manufacturing facility or plant.

Conclusion: To 10 Examples of AI in Supply Chain 

After an in-depth study of the top 10 examples of AI in supply chain management; we have realized that AI plays a key role in the efficient supply chain processes. If you are learning about the uses of AI in supply chain management; then you should keep in mind the abovementioned examples.

error: Content is protected !!