Container fleet growth and improved sailing speeds mean productivity is transforming for the better: Baltic and International Maritime Council (BiMCO) predicted supply to grow by 11.3% in 2023. However, this significantly outpaces 1-2% growth expectations in head haul and regional trade demand for the same period. With so much supply capacity and demand lagging, it doesn’t look good for shipping lines' profit and loss (P&L).

Nevertheless, with the increasing adoption of new-generation hardware and software, container shipping lines have seen an explosion of information high in volume, velocity, and variety. These vast data sets offer a rich source of insights, which can help carriers monitor efficiency, forecast demand, and optimize their operations.

Let's look at how shipping lines can leverage big data analytics and get the best return on investment (ROI) from data-driven container fleet management.

Robust data collection puts shipping companies at the helm

Container shipping lines have little control over port congestion and other inland transportation legs, such as truckers. However, customers are increasingly less loyal if services are delayed: In 2022, a FarEye survey found 88% of consumers may abandon their online shopping cart if delivery speed could be better. And retailers are less inclined to work with shipping lines again if delivery is slow or unreliable.

From ports like Tanjung Pelepas (PTP) implementing AI-powered port management information systems (PMIS) to smart containers and reefers with Internet of Things (IoT) devices, the industry is ripe for real-time data collection. This is good news for shipping companies and customers as the data can be used to create live maps to access a container's location and observe if it is coming on time or delayed because of disruptions.

Today it’s essential to continuously monitor the container's location and environment, such as temperature, humidity, and standstill time, particularly if perishable goods are on board. While reefers usually have a power source for refrigerating that can power IoT devices, it’s increasingly common to install sensors in dry containers too.

This helps industry professionals track their assets in real-time, helping them to make quicker decisions during delays and optimize performance in line with customers' expectations.

Mitigate risks with operational and financial data visibility

Lacking clear visibility into operations and finances hampers shipping lines’ capacity to optimize resource allocation, anticipate market fluctuations, and proactively address potential issues. From port congestion and perishing goods to natural disasters and political unrest, this lack of visibility can impede growth, hinder profitability, and leave them vulnerable to unexpected setbacks.

Take the Covid-19 pandemic, for example. A lack of visibility into the global supply chain caused major disruptions to international shipping, such as port closures, delays in shipments, and shortages of containers. Although this was a black swan event, in less severe cases, shipping lines may be better prepared to mitigate the impacts of disruptions with better data governance—proper data collection, visibility, and analytics.

Shipping lines collect data across multiple platforms, including container trackers, enterprise resource planning (ERP) or transport management systems (TMS), historical planning, and financial systems that, when siloed, lead to a shortfall in operational visibility. By integrating the systems, shipping professionals can access a reliable view of everything from container utilisation and turn times to detention and demurrage, equipment stocks, and cost reporting.

In the face of increasingly selective customers, shipping lines must start monitoring live data centrally, if they are not already, to organize themselves amidst the havoc. This strategic coordination is where big data analytics comes into play.

Muster your forces with big data analytics

A 2021 McKinsey report found early adopters of AI-enabled supply-chain management have successfully reduced logistics costs by 15% and improved inventory levels by 35%. AI provides the right toolset to analyze large volumes of data, helping carriers identify trends among importers and exporters worldwide, and ensure sufficient stocks in locations with the highest demand.

However, shipping lines can use big data analytics further to improve operational efficiency.

Let’s say you're building a container location forecast for eight weeks from now, using historical orders, bookings, and container events data from the past few years. However, you can enrich this data with external factors such as public holidays, industrial seasonality, forex, and economic indicators to better capture demand dynamics. Commercial forecasts can also be used to extract value, but because of the traditional overestimation, they could not be your source of truth. By feeding all this data into predictive analytics, shipping companies can forecast container demand across every location in the network to allocate the optimal stock and decrease operational costs.

Where predictive analytics forecast performance and demand, scenario planning helps shipping lines run simulations to anticipate future events. This gives carriers time to build what-if scenarios and disaster recovery plans in cases with blockages, like the Suez Canal, or a particular port closure. This can help them make more informed resource allocation and utilization decisions.

Big data analytics helps shipping lines gain a competitive advantage by enhancing operational efficiency, slashing costs, and delivering better customer service. Armed with centralized access to data sources, shipping lines can navigate uncharted waters and harness predictive analytics and scenario planning models. They can foresee shipment volumes, strategically allocate optimal capacity, and prepare contingencies.