In today’s digital age, competition in the port, logistics and maritime sector has led companies to constantly invest in solutions that help them increase productivity and reduce overall costs, consequently, the demand for advanced solutions, such as maritime data analytics, is growing at an impressive rate among commercial shippers and other end users.
The port industry is a complex network of people, countries, and organizations, including shipowners, authorities, classification societies, cargo traders, oil companies, and other businesses, to name just a few. The need to track economic flows in this global supply chain has driven the industry to keep extensive data records.
Traditional technology already allowed logistic companies to obtain data for analysis, such as times, speeds, quantities and prices, among others, and use it to create reports and visualizations that help to better represent and evaluate the business model.
You may be interested in: “Geospatial data to optimize supply chains.”
However, with the advent of Big Data management techniques, everything has changed. Concepts such as weather, delays, unexpected repairs and, above all, a large amount of data obtained from sensors, GPS, RFID and other traffic management systems can now be incorporated.
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Big Data in port data analysis is divided into three groups:
Vessel Management
Using the data available in different registers, system parameters and bunkering statistics, this includes efficient bunkering, better vehicle maintenance, and improved crew management.
Port and Cargo Management Using Port Authority Data
Covers carriers and trading companies. This includes efficient cargo handling, tracking of goods, optimization of port facilities, among others.
Spatial Image Analysis from Position Tracking Systems Data
Such as AIS (automatic identification system) and LRIT (long-range identification and tracking of ships), ship imagery, coastal and space-based radar, optical sensors, include efficient routing, fleet tracking, traffic pattern analysis, anomaly detection, etc.
Also read: “Big Data to understand consumer mobility.”
From the development and implementation of new algorithms based on Big Data technology, data from different sources are collected and integrated, achieving:
- The design and implementation of a global productivity command control for logistics and port operations, identifying sources of inefficiency and increases the improvement in decision making.
- Design and implementation of an optimization algorithm for the traffic management system that allows the scheduling of cranes and equipment, improving the terminal’s performance and productivity.
- The adaptation and integration of a predictive maintenance system based on the measurement, tracking and monitoring of equipment sensors, achieving a minimization of downtime and breakdowns.
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