Supply Chain Resilience Optimization

Managing Uncertainty

Global supply chains are becoming increasingly complex, and are subject to multiple sources of uncertainty: unpredictable customer demand, varying availability of supply, and disruptions caused by political events, natural disasters and infrastructure/IT failures.

To address these issues, MJC² has collaborated with major shippers and logistics companies such as Dell, Procter & Gamble, DHL, Kuehne+Nagel and DPD, developing innovative software algorithms that reduce the impact of these uncertainties on the supply chain.

The result is a suite of resilience-aware logistics and supply chain optimization algorithms:

  • Level 1 - Resilience: optimize contingency strategies and alternative routings.
  • Level 2 - De-stressing: increase reliability by de-risking lanes and schedules.
  • Level 3 - Agility: real-time optimization capability to automatically react to disruption.

1. Resilience Optimization

A multimodal network of routes provides resilience to the supply chain by offering alternative routes when major events cause disruption in the trade lanes. However:

  • The size and complexity of modern logistics networks (please see here) means that developing contingency plans is currently a extremely time-consuming task.
  • Furthermore, most modern supply chains cannot be managed and optimized based on a simple "minimise cost" basis - other factors such as customer service, reliability and environmental impact are increasingly important.

The core of the MJC² solution is an advanced AI-driven optimization engine which can route 1000s of shipments through the multimodal network in seconds. An integrated inventory model allows the corresponding impact of schedules on forecast stock levels to be determined.

Integrated disaster modelling tools allow the user to apply different disaster scenarios to the supply chain, enabling a rapid simulation of the impact on the supply chain.

The result is a set of resilient logistics plans for the overall network, including trusted/preferred lanes for normal activity, along with integrated fall-back and contingency plans that are known to be feasible and cost-effective.

2. De-stressing the Supply Chain

The increasingly "on demand" nature of supply chain operations places great pressure on the logistics operations. Shippers and forwarders search for the balance between fastest and cheapest, but are faced by a huge challenge due to the number and diversity of options.

In this environment it is extremely challenging to also assess the robustness of the solution to disruption. Major disruptions such as disasters will have been analysed in point 1 above, but more "everyday" events such as delays, bad weather, labour disputes, etc. still have a major impact.

The second stage of the optimization is therefore to incorporate "de-stressing" strategies into the logistics planning process, intelligently using any available flexibility in the operations to de-risk the logistics schedule.

Artificial intelligence algorithms can optimize the trade-off between "raw speed" vs cost vs robustness. In this way the resulting logistics operation is more resilient to delays and other disruptions, and leads to fewer bottlenecks at key nodes and terminals in the supply chain.

3. Agility & Real-time Optimization

The final component is a real-time optimization engine that gives the operation the agility to respond effectively to disruptions.

The de-stressing optimization undertaken in the previous step reduces sensitivity to relatively minor disruptions and delays. However, inevitably there will be cases where even a reasonably robust shipment plan fails due to a significant problem on route.

Furthermore, if a major disaster does occur, it is important that the operation has the right tools in place to quickly adopt the appropriate contingency strategy developed in Step 1 above.

MJC²'s LARG+O platform is a real-time supply chain optimization environment, combining live dashboards of shipments as they progress through the global logistics network with dynamic AI-driven optimisation of routes and flows. For example:

  • "Minor Disruption" e.g. a container has been unexpectedly detained at a port for security inspection, leading to a delay of two days.

    => LARG+O automatically re-routes the container onto a faster onward service, perhaps switching from barge to rail.

  • "Major Disruption" e.g. a labour dispute at a port leads to severe congestion and very long delays for all vessels and consignments.

    => LARG+O re-routes new shipments via different ports. For shipments already committed to the affected port the system replans onward movements accordingly.

  • "Disaster Scenario" e.g. an explosion occurs at a major inland terminal, making it unusable and effectively destroying all goods in the terminal.

    => LARG+O identifies the consignments likely to have been destroyed and schedules replacement shipments. Consignments on route to the terminal and future consignments are re-routed.



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