Maritime
GOALS
- Reduce unproductive movement in a large port maritime yard without deterioring the performance of the terminal
- Increase ressource utilization rate (cranes, forklift automatic guided vehicles, and other container handling vehicles)
Data coming from TOS
- upstream data: port of departure, shipping company, shipping line
- downstream data: receiving company, logistics companies involved
- inside of a container
- external data: weather and seasonality data
Analytics
- Machine learning and statistical modelling algorithms
- Big Data technologies for creating analytical boards and models
- Python
Business KPIs
- Unproductive moves ratio
- Container dwell time
- Vessel dwell time
- Congestion at gate entrance
- Ressources useful utilization rate
Results
- Improved container stacking on terminal site alongside the found customer categories
- Reduction of the unproductive moves ratio by 25%
- Our model predicted unproductive moves with 71% accuracy
- Main cost drivers were savings in machine utilization and respective maintenance leading to a yearly savings potential of multi million EUR