Connected operations – automated data collection

Companies in the connected phase want to benefit from better visibility and transparency into what is happening onboard. In practice this means collecting data more frequently and from a greater number of sources in order to better understand equipment efficiency and operation. Often onboard instruments are integrated into a data-collection platform that gathers and sends data to onshore office where it can be viewed and analysed.

Typical for connected operations:

  • Onboard data-collection platform is required 
  • Data is stored in a cloud service and is available for different use cases
  • Simple KPIs can be automatically calculated from the data
  • Automated data-collection removes inaccuracies in reporting, but also dramatically increases the workload for onshore personnel
  • Additional competences and resources needed to analyze the collected data

Automated data collection with integrated systems

Because of the limitations of traditional operation many companies have invested in systems to automate data collection. Instruments are integrated into a data-collection platform that gathers and sends data via satellite link to the onshore office where it can be viewed and analyzed by personnel on a computer or mobile device. In some cases, onboard analysis also occurs to enable faster decision-making, but typically the information is also shared with the onshore office. Data is typically stored in a cloud service and is available for different use cases. Often the cloud solution includes an analytics platform that enables reports and dashboards to be generated.

Increased workload due to manual data analysis

While the connected mode of operation removes inaccuracies in reporting, it also dramatically increases the workload for onshore personnel. Instead of a single noon report multiplied by the number of ships in a fleet, there is a constant stream of data for every vessel. Information overload is becoming a real issue as the deluge of data threatens to overwhelm the very people it is supposed to help. Sifting through this data in a timely fashion to uncover reliable insights is often difficult, if not impossible, especially given the amount of time that is needed just to prepare the data for viewing.

The data by itself answers the question of what has happened. However, understanding why or how something happened requires further analysis. Because the data is provided as is, at the very least a professional analyst is required to painstakingly comb through it.

Issues with data quality

The quality of automatically collected data is, at least in theory, superior compared to data collected manually. That said, there are still several factors that hinder its quality. Most of these issues are caused by sensors having intrinsic inaccuracies, calibration issues, or malfunctions. For example, a badly calibrated sensor or drift of sensor calibration can lead to significant misinterpretations of performance. In addition, the data collected onboard needs to be aggregated in order to transfer it onshore. If performed incorrectly, aggregation can significantly reduce the accuracy of the data.

Data quality example

Accurate speed-through-water data is a prerequisite for any decisions related to evaluating vessel performance or speed-related onboard operations. According to a study of 300 vessels conducted by Eniram in 2017, a large proportion (65% of cruise vessels) suffer significantly from poor speed log quality, with performance being over or underestimated by more than 5%. Such a large performance deviation is caused by a speed log inaccuracy of only 1.6%.

Organizational aspects

As mentioned before, the connected operations mode increases the stream of data to be analyzed and processed, requiring investment in professional analysts. Successful companies adopting this mode of operations develop plans with HR functions to attract new competences to the organization and create new roles.

How the data is used

Connected operations increase visibility and create insight into what has happened in the past. When data is available in real time both onboard and onshore, transparency is increased compared with traditional operations. For example, there is no need for the officer onboard to take a screenshot, send it, and then have a lengthy phone call. Instead, all data is instantly available to all relevant parties. Or if a vessel is heading into rough weather, the relevant personnel onshore would receive immediate notification as well as information about any follow up carried out onboard such as steering the vessel towards calmer waters. Operational transparency saves time and the need for follow up.

How to advance from connected operations?

To move towards smart data-driven decision-making mode, the following elements are key:

  • Consider investing in cloud-based technologies and machine learning to automatically analyze data in real time.
  • Combine information from what’s happening onboard with information about the vessel’s sailing environment, such as weather data. Then apply intelligent analytics based on machine learning methodologies to move beyond answering the question “What happened in the past?” or even “What’s happening now?” to “What will most likely happen in the future?”
  • Be prepared for a shift in mindset – previously the onboard crew were mostly responsible for vessel performance follow-up and optimization, but with more advanced analytics and modeling techniques available, onshore staff have a greater role in helping and supervising.
  • Create a plan for getting onboard crew engaged and committed to using the new technology, backed by strong support from top management.
  • Ensure that the human role is not underestimated – smart operation functions best when humans and computers are making decisions together, complementing each other’s strengths and compensating for each other’s weaknesses.

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