Data analytics for maritime safety management

By understanding the patterns and trends in data, maritime organizations not only can make better decisions about safety management but also create a data-driven culture that can proactively identify and address potential risks.

Safety is the most substantial and critical issue for all businesses in the world. Out of which, shipping is perhaps the most international and one of the most dangerous industries. A UK study has shown that it’s 5 times more likely to have a fatal accident on a ship than on construction sites. And on average, every 7 days a ship sinks.

The maritime industry has long relied on data analytics to help manage safety and mitigate risks. In recent years, maritime data analytics has become even more important as the industry has become more complex and the risks associated with maritime operations have increased.

To address potential safety issues, maintenance is taking place regularly. However, the current scheme is more of reacting to existing problems rather than being proactive on potential problems.

How can data analytics be used in a focused way and empower proactive actions?

The primary challenge associated with safety and maintenance analytics is that much of the data is dispersed among enterprise resource planning (ERP) systems, case management systems, legacy purpose-built safety applications, and various other secondary sources, such as equipment maintenance management systems.

Additionally, much of the data are stored in different formats and are subject to different data refresh schedules, making it very difficult to gain an ongoing holistic view of how the current maintenance system impacts the asset health condition.

To be able to analyze factors that might potentially contribute to deficiencies, companies need to firstly source and optimize a variety of data sources and structure them into a concise data set. This then allows companies to view raw data from distinct analytic perspectives. For example, to conduct root cause analysis, Kaiko Systems allows the technical team to create a timeline from normal operation to when the failure occurred. Variables like time, components, maintenance schedule, etc can be structured and correlated throughout the timeline. Then, the process helps to highlight the factors that are most likely to cause the change.

Roadmap for a data-driven safety management

While the critical aspect of safety analytics is to leverage data and act on findings in a timely manner. In Kaiko Systems’ experience, the safety operations in the maritime industry are a complex socio-technical system. To approach a data-driven safety system in a more focused way and at a faster velocity, stakeholders' initiatives and workflows need to be taken into account. 

Here is the roadmap that Kaiko Systems has broken down after implementing the solution on over 100 vessels: 

Align And Identify

Align safety scope: Quantifies the insights each stakeholder needs. This can keep everyone in sync and help create a focus on what needs to be done with the data. For example, instead of saying improving the vessel’s uptime, the team can suggest an increase in the lifetime of certain equipment on a ship.

Identify data sources: Inventory current data properly, and identify data sources and formats that are relevant to the safety initiatives. Leveraging a scalable method like Kaiko Systems for data collection can help reduce the time needed vastly while increasing the usability of the data. 

Optimize and Collect 

Optimize current inspection systems: Update checklists to include data required and allow data like pictures, ratings, and texts to be collected by the same platform under a tree structure. 

Collect standardized data: Strict specifications should be put in place to make all data follow a standard format, and make sure that each data type has the same content and format from the start. Leveraging a scalable method like Kaiko Systems for data collection can help reduce the time needed vastly while increasing the usability of the data. 

Verify and quick wins 

Verify data before analysis: Working with data from different sources often comes with the challenge of completeness, validity, and reliability. Especially when it comes to metadata that includes provenance, source, date, and location. These cumbersome details are easy to get corrupted in the process. Kaiko Systems is able to help the crew collect structured inspection data while verifying the plausibility. In this way, crews save 50% time that’s spent on inspection, and the data standard is ensured on the job. 

Focus on speed to value: Prioritize use cases that can give quick wins to test out the data models. After having effective insights, it’s also important to iterate workflows to lower the work burden and promote a better safety culture. This not only brings immediate result, but also improves the operational efficiency for each team.

Analyze and reporting

Conduct analysis: While key insights are quantified, to understand root causes, or what actions might be needed, the technical team needs to further group different data sources into the pre-set data models to be able to generate actionable insights. 

Auto-reporting: Auto-reporting is not only about sending customized reports to different stakeholders. Work orders can also be triggered by special events which can improve operational efficiency and allow fast reaction. 

Leveraging data analytics is a commendable stride toward proactive safety management. With verified and standardized data, safety data analysis will be the best assistant that tells you how your asset is doing and what actions need to be taken to prevent deficiencies. In the meanwhile, it's important to iterate the workflow with easy targets, so the team gets motivated by the benefits of the tool and the operational efficiency gets improved.

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