Emerging Technologies

How digital twins can help navigate supply chain disruption

A container ship is seen near Suez Canal Bridge which is known as the "Peace Bridge" following a Suez Canal tugboat sinking yesterday after colliding with a Hong Kong-flagged LPG tanker in the waterway of the Suez Canal, Egypt, August 6, 2023. Suez Canal Authority/Handout via REUTERS. digital twin

How can digital twinning help manage disruption to shipping routes and supply chains? Image: REUTERS

Mikael Lind
Senior Research Adviser, Research Institutes of Sweden
Wolfgang Lehmacher
Operating Partner, Industrial Innovation Partners, Anchor Group
Richard T. Watson
Research Director, Digital Frontier Partners
Margi Van Gogh
Head, Supply Chain and Transportation Industry, World Economic Forum
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Emerging Technologies

  • Global trade suffers from port congestion, hinterland (between the port and inland) bottlenecks and high-risk choke points.
  • The Bab-el-Mandeb Strait between Yemen, Djibouti and Eritrea is a high-risk zone and the Panama Canal has cut capacity.
  • Choosing an optimal route is critical and requires insight from data and analytics.
  • Digital twins are needed to navigate and inform manifold logistics decisions in interconnected, complex and dynamic trade environments.

From Covid-induced lockdowns, port congestion, container imbalances and chassis shortages; to the Suez Canal blockage, the Panama Canal drought and Houthi attacks on commercial ships, the challenges negatively affecting business operations know no limits. Countries and companies need to advance decision support systems to handle these disruptions effectively.

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Global shipping is a self-organizing system within which most actors, although intrinsically interdependent, largely take major decisions independently. Consequently, shipping is fundamentally a sub-optimal industry. Shippers might address the same problem in different ways. Some shipping lines, for example, might resume services via the Suez Canal; others could decide to continue transits via the Cape of Good Hope.

The global supply system is a maze and without a comprehensive, system-wide view, independent interventions can have unforeseen and undesirable consequences. The industry’s ability to act cohesively and the probability of positive outcomes can be enhanced with a high-fidelity digital model of the shipping industry, as part of the global supply system, continually recalibrated using dynamic, real-time flows of automatic identification system data (AIS data comes from automatic tracking systems on ships) and other data.

The impact of the Covid-19 pandemic in 2020 on supply chains
The impact of the Covid-19 pandemic in 2020 on supply chains Image: Statista

Large, self-organizing systems are highly complex and the effects of an individual action can be non-linear. Evolution did not equip humans to decipher such complexity. However, computer-based tools, systems dynamics, data analytics, visualization and machine learning, can enable us to predict and prescribe optimal solutions.

Latest supply chain disruptions: canals in crisis

The Suez Canal is a vital waterway for commercial shipping. The latest conflict in Gaza has resulted in targeting commercial ships with missiles and unmanned aerial vehicles (UAVs) in the Bab-el-Mandeb Strait. Despite Operation Prosperity Guardian, the US-led military operation responding to Houthi attacks, ships have been hit. The alternative route around Africa also comes with risks. Refueling in South Africa presents additional challenges, exacerbated by rising marine fuel prices.

Shipping via the Panama Canal, which allows goods to be transported swiftly between the Pacific and Atlantic Oceans, is disrupted owing to drought. Exceptionally low water levels are forcing canal officials to reduce vessel transits. Although the situation is not new, the latest canal disruptions are the worst on record. This hinders the flow of goods, deprives canal operating countries of billions of dollars of income and increases the cost of shipping and emissions, in both the short term and foreseeable future.

Digital twinning for disruption management

Building resilience and managing disruptions is about decision-making, the central activity of all organizations. Organizations act based on the anticipated effects of their interventions. The most rigorous approach to decision-making is to build a digital twin of the environment and simulate the impact of possible actions or events.

A digital twin is a dynamic digital representation of an object or a system, describing its characteristics and properties as a set of equations. Complex processes involving a multitude of actors are difficult decision-making environments that are best modelled digitally prior to action. A digital twin includes both the hardware to gather and process data and the software to represent and manipulate these data.

The components of a digital twin
The components of a digital twin Image: Mikael Lind and Richard T. Watson

Digital twins take advantage of digital data streams to bridge the barrier between the physical entity and its representation. Digital twin analytics rely on historical data and real-time, digital data streams (such as AIS and sensor-generated data) to analyse possible outcomes and provide answers to ‘what happens if’ or ‘what happens if not’ questions to support decision-making.

Simulating options

The digital twin needs to be fed with historical and real-time data on navigational constraints caused, for example, by weather conditions; fuel prices; bunkering possibilities (bunkering is the equivalent of fuelling in marine terms); the situation at canals, ports and terminals; and other choke points. Satellite data on the movements of pirates and terrorists can help to avoid attacks. Public and private data and analytics providers, such as private-sector players and national weather services, offer important feeds. Furthermore, data sharing between peers and with government agencies can close data gaps.

Digital twins allow real-time support for decision-making in dynamic environments. They are the basis for agile supply chain management, because they are digital replicas of global supply chain networks, providing a real-time visualization of operations including goods in transit and inventories. With the help of modelling tools, companies can instantly visualize the likely impact of disruptions, predict probable developments and anticipate future risks.

This allows for the simulation of different scenarios and the anticipation of disruptive events such as natural disasters or regional conflict. The insights from these simulations can be used to define alternative routes or fuel-sourcing options, considering all viable routing options and the resulting transit time and cost implications.

Importantly, digital tools facilitate communication and collaboration across supply chain networks. Supply chain integration enables alignment, corrective action and collaborative, cohesive crisis responses.

Image: Sandra Haraldson

The virtual watchtower (VWT) initiative is an example of human-machine interaction for disruption management. It approaches the management of dependencies between actors in the shipping and supply chain industry in a novel way. It takes a shipper-driven, terminal-centric distributed network approach combining community building with digital solution development where different actors in an system, in this case the supply chain, co-develop a digital solution to better deal with common challenges.

As the community grows, the benefits for every party and the community as a whole grow as well. Along the way continuous co-creation ensures that suitability of the solution is much more likely. The VWT complements existing visibility solutions, with more accurate data.

The need for more collaboration

Collaboration is a necessity. Effective responses to systemic challenges and complex crisis situations require that everyone contributes to global situational awareness, enabling a transparent view of an increasingly dynamic landscape.

Digital twinning can improve crew safety and reduce carbon dioxide emissions, delivering quantifiable social, environmental and economic value. In the maritime sector, the introduction of AIS data sharing, such as data on vessel position and speed, has created an essential real-time data stream. This data must be supplemented with real-time voyage intentions, port berthing and usage plans, bunker availability, the status of choke points and so forth, if we are to enable collaboration via an industry-wide, digital twin.

How does a self-organizing system build and maintain a real-time model of itself that supports independent competitive decision-making? Furthermore, how does it act collaboratively to build system-level resilience in the face of inevitable large-scale, uncontrollable disruptions? Finding the answer requires conscious, systematic collaboration – even cooperative competition or ‘co-opetition’ – between all relevant stakeholders across the global supply chain.

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Related topics:
Emerging TechnologiesSupply Chains and TransportationTrade and Investment
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