Rachael Elliott is Director of Global Strategy and Innovation for DRI International. With nearly 20 years of experience leading commercial research in organizations, she has particular expertise in the technology side of resilience, and a keen interest in how artificial intelligence can help to transform the resilience of organizations. In the second of a two-part interview, we asked her how resilience professionals can navigate the integration of AI and supply chain logistics.

The Impact of AI on Supply Chain Disruptions:

Q: To what extent can AI mitigate the impact of unforeseen disruptions on supply chains, and what are the limitations of AI in these situations?

AI is now firmly established in helping organizations to manage supply chain disruptions. Current freight mapping tools can provide information about potential port delays or bottlenecks, for example, and both historical, real-time, and future forecasting information can help give an early heads-up of incoming disruptions. The same can be applied to land and air transport as well. Stock can be rerouted or, if disruption is predicted in the mid- to long-term, stockpiling could also be employed.

As discussed in the first question, real-time monitoring (using IoT devices) can provide insight into current stock levels, the status of goods being shipped, and provide localized updates on weather or geological changes.

Other uses might be automated decision making (e.g. automating some procurement processes, rerouting goods based on synergized data sources, or allocating inventory depending on stock levels). Such tools could even go a stage further by forecasting future demand based on historical patterns and monitoring of, for example, medium term weather forecasts.

However, with these transformational uses of AI comes risk. The omnipresent threat of cyber-attacks, particularly to devices which might not be updated regularly due to remote locations, means additional IT/cyber staff will have to incorporate these into their own threat mitigation strategies. Furthermore, as mentioned previously, an over-reliance on automation can lead to issues, particularly if professionals allow AI to make decisions without human verification. For example, an incorrectly announced weather warning could lead to automated systems kicking in and rerouting trucks on lengthy diversions. Also, in areas where data reception is scarce, powerful information could fail to be transmitted.

Q: How is AI used to optimize supply chain recovery strategies and minimize downtime following a disruption?

AI can scan news sources, incident reports and visual data from mapping sites/drones to provide a real-time impact assessment and potential reputational damage. This can not only help to quickly inform professionals on the nature and scope of recovery, but can also be valuable in providing situational awareness and mitigating potential stakeholder fallout.

If a first-tier supplier is taken offline as a result of an incident, AI could help to kickstart the engagement of alternative suppliers and evaluating the entire supply chain to ensure that back-up suppliers are provided with early warning of potential new production demands. The use of digital twins can also be used proactively to help simulate potential changes or shifts in production and/or sourcing strategies in order to provide alternative models for supply chain management.

Finally, AI can collate data from the incident, showing what did and didn’t work, which can assist in the After-Action Review (AAR) or Post-Incident Review (PIR), ultimately helping to change existing practices for the good. While human engagement should remain vital in the AAR/PIR, AI can help provide synergized data for better decision-making.

Click here to read part 1 of the Q&A, discussing AI’s impact on business continuity and its ethical implications.