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