The market size for AI in the logistics and supply chain is expected to reach $12 billion by 2027. What systems are being used to drive this increase in productivity and efficiency?
5 min read
With traffic congestion causing 1.2 billion lost hours of lost productivity, Route optimisation aims to not only ensure faster delivery times, it also aims to reduce time and costs of logistics.
The system would be trained to evaluate and analyse all data on hand, including traffic reports, delivery data, past routes and map data.
It can look at the truck’s current geo-coordinates to determine precisely where the vehicle is along the route. This data can then be used for determining where changes could be made to optimise the route.
The best systems are self learning, so will improve over time as more data is fed into its automated self training engine, thus providing more efficiency over time.
With increased utilisation of IIoT (industrial internet of things) more data can be created around the movement, storage and organisation of stock in a warehouse. Using deep learning, a Ai can be used to automate and improve the internal logistics and communication processes that are needed to log, control and send vital information to users and customers.
This even reaches to the back office data, where RPA (Robotic Process automation) systems can be used to generate order forms, invoices and other documents; sending and managing them once generated.
Ai powered Data analytics can provide benefits to any industry especially those with large amounts of data. The difficulty is generating useful reports and visualisations around those data points. These analytics can inform next steps and business decisions.
Ai systems can be used in this space to automate generation of these reports and analytics. It can be used to intelligently flag changes to data or trends that are appearing.
In terms of decision management systems, human logic can be encoded into the Ai to automate data movement and documents depending on certain desired outcomes. These systems can drive and help decide the best decisions within logistics and supply chain organisations.
Ai enabled forecasting engines allow a higher level of accuracy when creating prediction. These models can also be updated as new data enters the system.
The models and engine is pointed towards the demand data that is generated, this intelligent mapping of future trends and movements can significantly improve financial planning, capacity planning, profit margins and risk management solutions
There are numbers ways in which ai powered systems can enhance logistics and supply chain customer service, they include:
In short, the accuracy and effectiveness of AI in logistics is growing quickly, each use case for ai systems is different and needs tallying to a companies needs, the best systems are built around the individual needs of the supply chain and process.
To better understand where AI can add efficiency and value to your company reach out to Pro Ai for an obligation free review of where ai can add value.