Brief description: 

The Port & Terminal productivity cockpit is a web-based tool for supporting a common set of shared and specific metrics to evaluate the logistic process of a port terminal. This logistic process could be depicted as a hub process where the clients are inputs/outputs from different transportation models: vessel, train, and truck. One of the main performance indicators for a terminal is to provide the shortest possible time to leave or pick up containers at the lowest possible cost. This time-cost ratio is an overall indicator to measure performance that is transversal for the domain. The most widely used Key Performance Indicator (KPI) is the Truck/Train/Vessel Turnaround Time (T/VTT): the time a specific means of transport spends in the terminal to fulfil an order. At this point turnaround time is known in isolation, i.e. in a specific terminal, and calculated after the order is processed. Our solution integrates Big Data sources from several stakeholders involved into the containers management and provides better insights and metrics about the overall efficiency at both port and terminal levels. The defined KPIs provide benchmarking capabilities (e.g. related to costs and performance) that may indicate different levels of competitiveness. A key feature of the solution is to achieve an improvement in KPIs as the Truck Turnaround Time by predicting its trend using analytical models. These models have been developed using machine learning techniques and the whole set of data available. Therefore, these models provide insights about current operations, thus helping stakeholders to understand how to improve the related KPIs.

Main Features: 
  • Real-time dashboards to display relevant information for decision makers and, hence, improve current decisions regarding resources planning. These dashboards are supported by a user-friendly interface
  • Improvement of the planning and execution of operations as the previous expertise and the whole set of historical data is the main source of knowledge.
  • Optimization of the asset utilization, specifically reducing maintenance work. A maintenance request implies a minimum crane downtime of 30 minutes. Knowing beforehand such scenario will reduce costs and assign another resource before failure.
  • Increment of productivity and efficiency, translated to moves per hour increase and cost per move reduction.
Areas of Application: 
  • Maritime Ports and Terminal operators
Workflow: 
Published
Component / Service / App

Owner

Instituto Tecnológico de Informática
Valencia
Spain
Type: 
BDVA member
Contact: 
BDV Reference categories: 
Data Analytics
Data Management
Data Visualisation and Interaction
Markets: 
Transport, storage and logistics
Readiness Level: