The first standard shipping container was invented in 1956. Since then, containerization has revolutionized the efficiency of freight shipping. Now shipping is transporting approximately 90% of the world's cargo. It is fair to believe that this invention gave globalization one of the biggest pushes.
Since then, maritime has become a data-rich sector and is once again at the cusp of a new era. This time, it is operational intelligence that revolutionizes the efficiency of shipping operations - by leveraging the sheer mass of accumulated data.
The purpose of operational intelligence is to deliver visibility and insights into business operations. It works with a combination of in-memory computing and parallels real-time data analysis. This allows companies to access performance metrics in real-time, analyze problems, identify solutions, and show future predictions.
Operational Intelligence is a way to convert big data into smart data.
For example, when a worker inspects the condition of a cargo hold, machine vision can give direct feedback on the percentage of rust within a certain area. This feedback then informs the next course of action: Is there maintenance work required or not? Is it safe to load a certain type of cargo? When to schedule maintenance work during voyage plans and fleet availability?
Business priorities are transparent and integrated into day-to-day operations.
To get started with Operational Intelligence, it is required to have sufficient data and to understand what the data looks like - what is being generated, where it’s being stored, and how it is currently being analyzed.
After proper data cleanup and identifying relevant metrics, operational intelligence then is able to perform analysis based on the data pool without further modeling. For example, anomalies or trends can be displayed live with just a click.
Traditionally, Operational Intelligence depended on the use and aggregation of data stored in data warehouses to create analytics on business operations. The problem with this approach is that the information is based on past events, providing insights only in hindsight, not in the context of the actual state of the moment.
The advent of big data technologies like cloud infrastructure and modern cloud data platforms allowed the capture of real-time data. Through streams of information from various sources aggregated in distributed data platforms, it becomes possible to break data silos and analyze metrics based on actual real-time business operations. This allows proactive, on-the-job measures.
By processing real-time data fast, Operational Intelligence provides visibility & insights. Shipping companies need to make better decisions faster, managing the fleet in real-time. This opens up multiple use cases in fleet operation management:
Shipping companies are required to document vessel health, collect machinery data and ensure regulatory compliance. However, collecting this data is a manual, time-consuming & error-prone process. Information has always been scattered across multiple systems, becomes outdated quickly, and is unreliable.
Therefore, standardizing manual data collection is the first step to bring operational intelligence to the maritime industry.
Only then, the true potential of big data, cloud computing, and AI can be realized.
For Kaiko Systems, the objective of applying Operational Intelligence in shipping companies has three levels of insights: operational, tactical, and strategic.