## 2 Digitalization in Maritime Industry  
Digitalization using BDA and ML has been utilized in the shipping industry to improve operational efficiency, productivity and to enhance fuel efficiency. BDA and ML are used to reorganize huge amounts of unstructured data and analyze these data to establish the correlations between diverse aspects that are difficult for human analysts to identify. ML helps to accelerate the process of BDA and is used to uncover trends and patterns of the data. Research conducted across industries has shown significant improvements in industries that are in pursuit of digitalization where these improvements have enabled industries to experience better economic performance [15]. Although digitalization in the shipping industry has been relatively slow compared with other industries, big players such as Rolls Royce and Wartsila have set up research and development centres to explore remote and autonomous shipping [16; 17].  
Machinery that is built for ships usually does not live up to its expected lifespan. This might be due to the lack of maintenance or the incapability of detecting faults sooner to prevent catastrophic damages. To reduce such risks, preventive maintenance that involves the evaluation of equipment condition via periodic check (i.e., BDA) and continuous equipment condition monitoring (i.e., IoT) enables the maintenance process to be much more efficient as remote diagnostics of ships' machinery will be made available [18]. When faults are discovered immediately, further engine damage can be prevented, thereby reducing the amount of fuel consumed. This in turn reduces the amount of GHG emissions and also results in 10-35 per cent more cost-effective operations [19]. According to a report in [20], predictive maintenance can reduce unexpected failures by 55% and maintenance costs are expected to reduce by an estimated 25% to 30%. Companies are also utilizing advanced technology such as weather routing, allowing ships adequate time to avoid bad weather [21; 22]. In addition to that, the technology ensures that its seagoing assets' gas emissions and cargo temperatures are monitored from shore, thereby reducing maintenance costs and the risk of failure due to negligence [23]. Another type of technology that helps to reduce fuel consumption is the Marine Growth Prevention System (MGPS). The MGPS aims to combat marine organism growth and prevent the organism from depositing on the ship's systems, thus helping to eliminate corrosion. The MGPS aids in the efficient  
Figure 1: Examples of electric vessels: (**a**) Ellen Ferry; (**b**) Brim Explorer cruise ship; (**c**) Grimaldi GGSG ro-ro freighter.  
operation of the seawater-supplied system and machinery [24], and reduces the ship's resistance. This in turns increases the energy savings and reduces the fuel consumption of a ship [24].  
To stay ahead of an ever-evolving environment, there is a growing emphasis on the adoption of digital technologies in the maritime industry, employing BDA and ML [25; 26; 27]. It enables a large amount of data to be collected, stored and processed where many aspects of marine operations could be conducted via digital platforms, efficiently and effectively. BDA and ML to achieve fuel efficiency in ships are widely implemented in commercial vessels such as container ships, oil tankers and cruise liners. However, little work has been carried out on HCV such as tugboats, patrol vessels and ferries. The carbon emission from HCV should not be overlooked; therefore, countries such as Singapore have allocated a substantial amount of funding in encouraging the harbour craft sectors to invest in digital solutions [28]. In the next section, the BDA and ML frameworks applied to HCV are presented.