By Luca Urciuoli, ZLC Adjunct Professor.
Since men first went ‘down to the sea in ships’, ocean transport has been a risky business. Storm, tempest and shipwreck are ever-present, but there is a whole range of more human failings that while they do not threaten life and limb, add incalculable cost and inefficiency to supply chains. Often, these costs are barely recognised – delay and disruption are normal and accepted in the maritime trade.
Historically, of course, those on land would have no awareness of any problem until their vessel failed to turn up, so there was little or no scope for mitigation. With modern technology that is no longer the case, but the list of parties who may need to be co-ordinated to mitigate the effects of accident or delay is very long, and manual communication methods are slow and error-prone.
It might therefore seem obvious that digital ecosystems to automate communication and support decision-making would make a significant contribution to reducing the disruption and cost of unplanned events, by reducing managers’ response times and enabling supply chains to recover in a cost-efficient manner. But surprisingly, there is little methodology available to evaluate the benefits of digital platforms in operational risk. Especially since the pandemic, global supply chain risk, from viruses to geopolitics, the need for resilience, and the strategic implications for sourcing strategies, inventory policies and so forth, have been much discussed: operational risk (from late arrival to containers overboard to vessels trying to transit the Suez Canal sideways) less so.
Demonstrating the benefits matters because creating such digital platforms is neither trivial nor cheap. There are a very great number of different entities involved. There are the product manufacturers or owners, and the intended recipients, shippers, land carriers, port authorities and handlers, ocean carriers, customs administrators and others. A range of intermediaries may be involved – freight forwarders, ship brokers, customs agents, perhaps NVOCCs (non-vessel operating common carriers) to whom risk and responsibility may be outsourced to varying degrees. The immediate impact of a supply chain failure because, for example, a vessel arrives late in port, is on the company waiting for its goods, in for example lost sales, or penalty charges from their customers – but where the costs finally land is anyone’s guess. And a digital network for a single trade is of little use: a reasonably large container carrier may be an integral part of 10,000 separate supply chains! It is therefore less than obvious that investment in digitisation has a cost benefit for many of the individual players, even if the gain to the whole supply chain is clear.
We have attempted a mathematical model to evaluate the benefits of digital platforms, specifically around their contribution to mitigating operational risks in sea transport. To do this we looked at four trade lanes for containers moving from the Far East to the Netherlands, examining available historic data and filling out the picture through interviews and workshops with relevant shippers, freight forwarders and others.
All four lanes used the digital platform to some extent, but none could be described as fully developed. The relevant data around a shipment exists in many forms. Almost all could be handled electronically and automatically, but not all of it is. Some, for example a vessel’s location, can be and is reported continuously and automatically as it changes. Other documents, for example Bills of Lading, are invariant during the shipment, and may or may not be electronic. Discrete events – a vessel’s sailing time, or a container being loaded – may be recorded manually or electronically, and reported manually (sometimes with significant delay) or automatically. Customs documentation is usually electronic these days, but may still be dependent on manual (and error-prone) input. And of course, truly exceptional events may not have an established digital reporting format. So, the ecosystem we assessed deployed a range of real time tracking services, exception alert services (emails) and real time or scheduled milestone and KPI reporting services, but supplemented by manual data uploads.
Also, the ecosystem didn’t feature some of the most recent advances, in areas such as AI or predictive analytics, that could in the near future make digital platforms even more effective in not just reporting, but advising on and even executing remedial actions.
We needed to categorise the types of operational risk involved in ocean transport. There are many. There are delays at sea, perhaps due to equipment failure, or the need to route around weather. There are strikes, queues for port terminals, piracy, security or compliance issues and many others.
Risks tend to evolve or ‘snowball’. A late arrival in port may mean the vessel misses its allotted berth slot, extending the delay. Goods to be transhipped to coastwise or inland shipping (which is common) may have missed their sailing, so further delay occurs while alternatives are sought (such spot market booking are likely to be more expensive, while the cost of the original booking may not be recoverable). A vessel running late may omit some calls, so goods for those destinations may face an unplanned transhipment and onward passage, and every extra handling event is another opportunity for cargo damage. To generalise, we identified five main scenarios where digital platforms might be beneficial in reducing instances or aiding recovery:
- Container detention
- Export inspection
- Unplanned transhipments
- Container release
- Cargo damage (during any of the above, or while in transit).
With these in mind we constructed tree diagrams to display the evolution of risks and their operational implications, and validated these in focus groups, considering ‘normal’ scenarios and what deviations from normal look like.
We then developed state equations, incorporating the main probabilities and corresponding costs, using historical statistics where available, supplemented by judgemental input from our experts. For each scenario there are a number of possibilities, with different costs and probabilities attached. To take cargo condition as an example: the cargo could be undamaged; or it may be damaged and unusable; or it may be damaged but saleable at a lower price; or it may be damaged and saleable if money is invested in repairs.
We then asked our experts to assess the impact that the digital platforms were having, and how this was affecting the probabilities and costs of different risks.
The headline finding is that we demonstrated quantitatively that the improved visibility engendered by the digital ecosystem really does bring benefits, but the findings also underscore the importance of understanding the most important operational risk scenarios, and the potential for reducing the time to manage disruptions (improved visibility is not particularly helpful if timely action isn’t taken).
An interesting finding is that what really drives the business case is not the straight ability of the system to reduce a given risk, but rather the cumulative impact on the costs arising. For example, the ecosystem is not particularly effective at reducing the costs of cargo damage in percentage terms (less than 10%, perhaps unsurprising as damage tends to be accidental rather than the result of a systemic failure), but the value of that reduction is very high. By contrast, risks at export inspection can be almost eliminated (because, for example, all the documentation is present and correct), but this, though well worth achieving, doesn’t save particularly large sums.
We have also validated our decision tree approach and in the future, we believe this modelling could be developed in combination with greater use of real time data to enhance the predictive capabilities of digital ecosystems and also to predict the costs and benefits of new and more advanced ecosystem services.
For more information, the paper:
Urciuoli, L., Hintsa, J. Can digital ecosystems mitigate risks in sea transport operations? Estimating benefits for supply chain stakeholders. Marit Econ Logist (2020). https://doi.org/10.1057/s41278-020-00163-6
is available at https://link.springer.com/article/10.1057/s41278-020-00163-6 , or contact [email protected]