SVM EDGE

Revolutionizing Analytics with Advanced Data Mining for the Maritime Industry

Today, the Maritime industry is facing several challenges due to slowdown in global trade volumes, low freight rates and overcapacity. This has led to the several challenges:

  • Declining revenues week after week
  • No possibility of increasing rates
  • Margins under constant pressure
  • Operational costs constantly increasing
  • Increase of risk associated with vessels

To remain competitive in such a challenging environment, the industry is looking for a technology driven process to identify areas for:

  • Maximizing revenue
  • Reducing risk associated with vessels
  • Improving inspection & vetting results
  • Increasing operational efficiency & safety
  • Reduction in costs

The maritime industry has a large quantum of data (and produces even more data every day). Most of this data is unstructured and making sense of this huge amount of data is extremely challenging.  However, identifying correlations from this data and confidently predicting outcomes can help gain a competitive edge.

With reduced investments towards IT projects, business users are unable to allocate budget for expensive enterprise level data mining tools. Therefore everyone is looking for the best, cost effective data mining tools to analyze and sort through this data and produce valid predictable outcomes.

This is where EDGE outshines the competition and plays a significant role.

EDGE is an acronym for Exploration of Data for KnowledGE. EDGE is a powerful Data Mining & Predictive tool that delivers insights quickly and helps positively impact the business. It is the perfect “Decision Enabling Software”.

With various inbuilt algorithms and machine learning capabilities, EDGE is excellent at identifying correlations between data that can influence outcomes. Business users are now able to take decisions with great confidence.

Few of the leading maritime companies have already used EDGE to predict outcomes in various business scenarios. These outcomes have been implemented and have already started yielding huge operational savings.

CASE STUDY

Vetting inspection results were already amongst the best in the industry for one of the largest marine energy transportation, storage and production companies. However, to continue to be a leader in this critical metric, an analytics project using EDGE was undertaken to mine the ship management vetting inspection data and identify attributes that influence inspection outcomes.

Result

  • EDGE identified key attributes and automatically mined the order of importance, and hierarchy of each attribute and their influence on vetting inspection outcomes
  • These findings were instrumental in decisive initiatives taken by the management to achieve future vetting inspections goals
  • These initiatives are already predicted to:
    • Reduce risk
    • Improve vetting inspection results
    • Contribute to huge operational savings (in several million dollars)