Tag Archives: cargo modeling

Cracking the Cargo Conundrum

The smoke from nearby forest fires drifting across the entrance to the Port of Singapore wasn’t unduly worrying the captain of Titan, arriving from Shanghai with 10,000 containers on board. He had clocked the oil tanker off his starboard side and was content that, after obviously having a few navigational hiccups, the pilot of that vessel was now holding a course and speed safely out of Titan’s way. It was as he relaxed back in his chair and looked out across the bow that the smoke thinned out and he saw it. Another tanker. Huge. Q­max class carrying liquefied natural gas.

This is not the plot of a blockbuster book or the climactic scene of a Hollywood disaster movie. It is one of a number of plausible scenarios in the new RMS report on the challenges facing insurers because of the huge growth in marine cargo.

The report “Marine Cargo Catastrophe Modeling: Navigating the Challenges, Charting the Opportunities” examines the outdated techniques and incomplete data that marine insurers have had to make do with in order to estimate their cargo cat risk and port accumulations. Put simply, for too long knowing how much exposure they’ve built up in enormous international ports has been a guessing game. And two recent CAT events, which caused multibillion dollar losses because of huge concentrations of cargo, have exposed this weakness to an uncomfortable scrutiny.

The risks of global trade

Whereas the risks for land or property are essentially static, cargos are constantly moving and so the risk variables might seem unfathomably complicated. It’s not just the number of ports the vessel will go through and the CAT­ risks in those locations: hurricanes, storm surges, earthquakes, and terrorist attacks. Consideration needs to be given to the geology in that region, the construction of the ports in that country, and the level of disaster­ preparedness that exists.

As we saw during Superstorm Sandy, loss outcomes can be influenced by factors such as the exact location of cargo storage (In containers? In warehouses? Stacked?). Equally important is the vulnerability of the products. Are they fragile like electronics (ruined by water) or more resilient like jewelry (which can more easily be salvaged)?

The new RMS report in combination with the soon-to-be launched RMS Marine Cargo Model will bring clarity to these issues.

A purpose-built model for the industry

The RMS Marine Cargo and Specie model will be generally available this May, with the launch of RiskLink version 16.

To develop the model, the RMS geospatial team analyzed thousands of square kilometers of satellite imagery of top global ports and created a proprietary technique for allocating risk exposure across port terminals and storage structures. The port Industry Exposure Databases (IEDs) included in the RMS Marine Cargo Model, also incorporate important information on “dwell time,” or how long cargo spends at a given location. This variable, which is critical in determining port accumulations, can be highly influenced by variables such as weather, port automation, import/export ratios, and labor relations.

Covering almost 80 countries and three perils (wind, storm surge, and earthquake), the new marine model will provide 11 high-resolution and 150 medium resolution port industry exposure database, enabling the best insight on cargo vulnerability and global port accumulation currently available to the industry.

Tianjin Is A Wake-Up Call For The Marine Industry

“Unacceptable”  “Poor”  “Failed”

Such was the assessment of Ed Noonan, Chairman and CEO of Validus Holdings, on the state of marine cargo modeling, according to a recent report in Insurance Day.

China Stringer Network/Reuters

The pointed criticism came in the wake of the August 12, 2015 explosions at the Port of Tianjin, which caused an estimated $1.6 – $3.3 billion in cargo damages. It was the second time in three years that the cargo industry had been “surprised”—Superstorm Sandy being the other occasion, delivering a hefty $3 billion in marine loss. Noonan was unequivocal on the cargo market’s need to markedly increase its investment in understanding lines of risk in ports.

Noonan has a point. Catastrophe modeling has traditionally focused on stationary buildings, and marine cargo has been treated as somewhat of an afterthought. Accumulation management for cargo usually involves coding the exposure as warehouse contents, positioning it at a single coordinate (often the port centroid), and running it though a model designed to estimate damages to commercial and residential structures.

This approach is inaccurate for several reasons: first, ports are large and often fragmented. Tianjin, for example, consists of nine separate areas spanning more than 30 kilometers along the coast of Bohai Bay. Proper cargo modeling must correctly account for the geographic distribution of exposure. For storm surge models, whose output is highly sensitive to exposure positioning, this is particularly important.

Second, modeling cargo as “contents” fails to distinguish between vulnerable and resistive cargo. The same wind speed that destroys a cargo container full of electronics might barely make a dent in a concrete silo full of barley.

Finally, cargo tends to be more salvageable than general contents. Since cargo often consists of homogenous products that are carefully packaged for individual sale, more effort is undertaken to salvage it after being subjected to damaging forces.

The RMS Marine Cargo Model, scheduled for release in 2016, will address this modeling problem. The model will provide a cargo vulnerability scheme for 80 countries, cargo industry exposure databases (IEDs) for ten key global ports, and shape files outlining important points of exposure accumulation including free ports and auto storage lots.

The Tianjin port explosions killed 173 and injured almost 800. They left thousands homeless, burned 8,000 cars, and left a giant crater where dozens of prosperous businesses had previously been. The cargo industry should use the event as a catalyst to achieve a more robust understanding of its exposure, how it accumulates, and how vulnerable it might be to future losses.