The largest operator of a mid-sized tanker vessel company has vast amounts of data, a tangle of information about seafarers, incidents, inspections, and maintenance activity. Through data analytics, this data could reveal insights that would help unlock untold benefits for the business.
There are two problems that fall squarely on the shoulders of the marine human resources (HR) professional and the fleet manager that this extensive data hopes to solve:
Firstly, fleet managers need to foresee where and when a fault would happen that could lead to either personal injury or an operational incident. This could compromise the performance of the ship, which would then be placed in the red zone/danger zone if the fault is not addressed or resolved swiftly.
Secondly, the marine HR professional needs to find a perfect combination of officers to be placed in the suitable vessels at the right time to stay in a safer zone throughout their voyage and minimize any risk.
These two problems are solved using SEDGE predictive analytics.
CHALLENGES
The problem can only be solved after many measures are added to the existing data and normalization has taken place. Though machine learning (ML) will perform a pattern analysis and recognition from the historical data, the shape fed to the algorithms must be well defined. This is where most of the challenges were observed.
The data has been fed from the seafarer’s personal information, audit information, as well as inspection and incident details. Due to this diversity of data from multiple entities, we created over 80 features from the data. Some are standalone features while others have combined effects, thus enabling SEDGE to learn the pattern smoothly and predict more accurately.
Creating risk buckets for officers was the most challenging part as the weight distribution for each incident was different based on location, risk actuals, and risk severity. Manipulating maintenance activities data was another challenge in order to record all the officers performing their duty without much deviation.
The greater challenge lay in statistically determining a score that should represent an officer’s historical work. A perfect unbiased score defines an officer’s activity throughout his/her tenure in the company. The score was used to predict the officer’s and vessel’s risk profile.
READ MORE at our Machine Learning tool SEDGE Resource Page