Maritime Logistics Optimization for a Global Mining & Metals Company
We have helped an international company specializing in the production of aluminum and supplying it to customers around the world in reducing the costs of logistics and manual work. We have created a maritime logistics optimization system that enables process automation, accurate forecasts, and supports effective decision making.
Reducing the logistic expenses
The company produces aluminum and supplies to its clients around the world. Maritime and land transportation costs are substantial expenditure side of the company. The order handling process is very long. Several departments are engaged in the delivery service. The process of sending materials consumes a lot of time and effort itself.
Automation of manual order analysis
Managers need to analyze hundreds of orders (in terms of Incoterms, delivery zones, the sailing time between ports, etc.), after that form ship lots, find available vessels and calculate costs (transportation costs, stock costs, transshipment costs). Due to the lack of time, managers use the most primitive approaches for ship formation in order to deliver all orders on time. This approach was not optimal and the company spent a lot of money on transportation.
Creating an optimization system
That’s why the company decided to create an optimization system. It will support the decision-making process of the freight department. As a result, it would significantly reduce the company’s logistics expenses and save time thanks to the automation of manual work.
We developed a comprehensive AI solution for maritime logistics optimization. The solution takes into account various specific business factors: loading rate, the distance between ports, average vehicle speed, the sailing time between ports, oil consumption, MGO, order volume, material type, etc.
AI optimization engine
Implemented algorithms minimize all-important transportation costs: demurrage cost, dead freight, fuel consumption, overall ship route, and many others. The system automatically finds the right type of vessel, blocks the available pier, assigning LAY-CAN.
Predictive modeling using machine learning
The optimization system has been additionally enriched with predictive modeling. Algorithms for forecasting ship prices based on macroeconomic factors and seasonality have been implemented. In addition, a trained model for predicting ship delays based on marine AIS (ship tracking system). The created models enriched the optimization engine and increased the quality of order planning for the following months.
A user interface has been created on top of the AI system. The user interface allows users to make fast and proper business decisions by analyzing several logistic optimization scenarios. The system can also be used to run ad-hoc simulations with manually entered parameters, ship allocation on the platform, transport analysis, and stock costs.
The AI solution has been fully automated and integrated with the company’s main ERP system (SAP). The system uses SAP as a data source and also returns the results directly to SAP. The optimization results were sent daily to the SAP system.
AI optimization systems give the user possibility for multidimensional analysis of the most valuable logistics KPI’s: customer orders carried out on time, transport costs (demurrage, dead freight, cost per ton, oil consumption), inventory, transshipment costs, revenues and more.
The implemented solution supports maritime logistics optimization and provides a possibility to forecast ship prices through predictive modeling. It enables analyzing several logistic optimization scenarios and making effective business decisions through the user interface. The solution has been fully automated and integrated with the company’s main ERP system (SAP). The new optimization system gives the user the possibility of a multidimensional analysis of key performance indicators.
Increased customer orders completed on time
Reduce transportation and stocks costs
Save hours of manual work thanks to Automation