For those interested in shipping jobs, it has emerged as a major source of competitive advantage among industry participants. It represents an evolution of traditional ‘look back’ analysis as it is concerned with interpreting vast data sets to provide real-time insights and intelligent forecasting that address specific company objectives. In the business of shipping, these could include optimising transit times, anticipating vessel repairs, reducing downtime in ports, or switching routes to avoid potentially dangerous weather conditions.
Summary of practical applications
The value of it is transformative for the shipping industry. It means decision-makers not only being alerted instantly to immediate problems such as traffic delays, port strikes or maintenance faults, but it also enables forward planning and risk-mitigation in terms of predicting weather patterns for certain routes at different times of the year, using sensor feedback to anticipate repair incidents before they occur, calculating fuel consumption efficiency, or assessing inventory needs as part of a leaner, more intelligent and more reliable approach to supply chain management.
Key areas of competitive advantage:
- Route optimisation efficiency
It offers opportunities for significant fuel savings, timely deliveries and improved safety by avoiding hazardous conditions. This comes from drawing on historical and real-time information in respect of weather patterns, ocean currents, fuel consumption and vessel speeds to establish the fastest transit routes.
- Predictive maintenance
It can be gathered from ship sensors to identify potential equipment failures. This can come from monitoring unusual vibrations, temperature fluctuations or energy consumption spikes in the engine. Research has shown that this predictive approach to maintenance has reduced downtime by up to 50%.
- Security enhancement
Vessel sensors, navigation systems and ship-to-ship communications deliver big data that updates every 2 seconds to identify cyber-attacks or system vulnerabilities.
Challenges posed by big data:
- Data volume and complexity
A vast amount of data sets, in different formats, pulled from a range of sources is a challenge. This includes weather tracking, vessel monitoring, cargo handling, crew management, port statuses and sensor activity.
- Real-time data processing and analysis
Gathering, analysing and evaluating the huge amount of continuously updated big data to make timely decisions is difficult. Vessels provide constant alerts on location, speed, fuel consumption and weather conditions, which is information that must be interpreted quickly in order to achieve route optimisation.
- System integration
With all manner of data sources and data flows, there are challenges in consolidating all the information to help bring about easy decision-making. Older technology on certain ships has not evolved to accommodate the increase in data inputs, creating a need for interpretative middleware and bespoke system solutions.
- Data security and privacy
Greater data volumes and higher complexity of it bring challenges for managing internal privacy and preventing external security breaches. Data breaches bring significant financial and reputational risks, even national security risks. Research has shown that the average cost of a maritime cyber-attack has increased to USD 550,000. Potential mitigants include adopting advanced data encryption methods, initiating regular audits and maintaining strict access controls among authorised personnel.
- Skilled personnel
Such data volume and complexity requires talented personnel to interpret the data analysis meaningfully and approach decision-making skilfully. Missed opportunities to translate the data means a shortfall in potential efficiency and competitive advantage.
- Cost of big data infrastructure
Establishing big data systems involves significant investment, specifically in high-powered computing hardware equipment, sophisticated visualisation software and secure storage solutions. However, budget constraints make this a challenge, especially for smaller operators. Cloud-based services can meet this need by bringing scalability without upfront costs from building data centres.
