Voyage Data Recorder (VDR) supplier Danelec Marine, announced its Bridge Operational Quality Assurance (BOQA) safety and risk management support tool at the Nor-shipping conference in Oslo, in June.
BOQA, described as a decision-support tool for officers on the bridge, is designed to enhance maritime safety through monitoring of ship deviations and provision of shore feedback before accidents occur.
The system provides a structured methodology for automatic electronic data monitoring, providing real-time trigger alerts and post-event analysis and feedback to correct potentially dangerous behaviours. It also records six event categories; navigation, AIS, compliance, weather motion, and customized safety events. The system can be set up for daily reports and/or instant alerts for each event type.
Danelec describes BOQA “as a scalable cloud-based event reporting, analysis and feedback solution” that is integrated with the DanelecConnect shipboard Internet of Things (IoT) platform. It provides automatic recording and transmission of data from ship navigation systems and sensors through the DanelecConnect hub to shore offices and uses analytical tools to identify deviations from operational parameters. Danelec says that this will allow shipping companies to create a formal proactive approach to operational risk management in ship navigation practices and procedures.
The development of BOQA was inspired by a 2013 paper submitted to the IMO by the Oil Companies International Marine Forum entitled The Proactive Use of Voyage Data Recorder Information.
The paper called for transmission of data from VDRs to shore for analysis against the shipping company’s established standard operating procedures. The OCIMF, proposed system would identify non-conformities and give feedback to the ship’s master and crew as a learning experience to avoid future mistakes and enhance safety.
“BOQA is designed to be future-proof with a built-in capacity to learn,” said Hans Ottosen, CEO of Danelec, “new unforeseen event types can be developed by applying machine learning and artificial intelligence methods.”
The system is currently undergoing initial testing onboard five vessels of different shipping companies.