Extreme weather is an ever-present hurdle for supply chains, exposing them to severe operational disruptions.

These include changes in buying patterns, shifts in product demand, and the need for greater agility in delivery capabilities. Extreme conditions can also strain resources such as electricity for heating or cooling, requiring reactive processes and resiliency plans, and ultimately leading to materials shortages for manufacturers.

Technology issues, including inefficiencies and vulnerabilities, frequently exacerbate these challenges. In fact, 89% of organizations report experiencing operational disruptions or failures due to technology-related problems.

To ensure that their processes are resilient and capable of handling disruptions, organizations must take proactive measures supported by artificial intelligence. In the process, they can adjust workflows as needed to ensure smooth execution, maintain customer satisfaction, and reduce operational risks in response to adverse weather conditions.

To proactively mitigate disruptions, organizations should start by building a resilient operating model from the ground up. This means breaking down end-to-end supply chains into individual processes and capabilities. With a strong foundation in place, plans for operational resilience, including disaster recovery, disruption response, and handling conditional variance, will naturally follow. Such measures ensure that businesses can continue to deliver at scale, regardless of external conditions.

Alongside these processes, leaders must prioritize operational transparency, encompassing visibility and measurement, ownership and accountability, and chain-of-authority for each process.

Transparency is the cornerstone of operational resilience. It involves both design transparency (detailing what an organization is supposed to do, scenario planning with AI-driven information retrieval, and providing just-in-time knowledge management) and process transparency (tracking real-time actions and performance through AI-powered process mining and root-cause analysis).

Process mining, in particular, is a crucial technique in enhancing transparency. It’s a practice rooted in laying the foundation for optimization by analyzing existing processes to identify inefficiencies, bottlenecks and deviations. By capturing and translating this data, process mining offers a clearer understanding of operational shortcomings and the insights that can nudge organizations toward the proper improvements.

Vendors should also use AI to configure systems monitoring, integrate complex data sets, and ensure that the right key performance indicators are prioritized. AI can also help to capture best practices, drawing from collective wisdom (in the form of historical data) while harmonizing it with industry standards.

Lastly, by establishing proactive and reactive communication channels and mechanisms, supply chains can cut through the disruption noise. This enables them to execute resilience plans, coordinate with handoff owners, and ultimately respond to difficult conditions with ease.

From Insights to Action

AI offers a range of applications in process intelligence. It’s highly useful in boosting operational resilience and managing disruptions caused by external conditions. However, to maximize the technology’s benefits, the insights that it generates must be actionable. This is where a business’s primary lever —process transformation — comes into play.

AI-driven process modeling allows businesses to conceive of their entire workflows in a visual, graphical format, capturing and relating key activities across a swath of departments. This helps to predict the ripple effects of disruptions and develop mitigation strategies, drawing on weaknesses identified during the process-mining stage, and showing where re-engineering can make a material difference or provide value.

By showcasing successes and challenges experienced throughout an organization’s transformation journey, leaders can apply data-driven insights to keep up with the landscape little by little (rather than in larger, less frequent leaps and bounds). While this might not seem like classic disaster preparedness, regular maintenance hones the operational resilience “muscle” that needs to shoulder a larger burden in times of crisis.

By combining AI insights with human domain knowledge, organizations can create a blueprint for change management, transforming their operational models into more resilient and adaptable frameworks.

AI's Path From Theory to Practice

The future of operational resilience will evolve from the theoretical to the practical applications as AI technology improves. Currently, this evolution sits firmly in the “democratizing” phase. Large numbers of stakeholders are investing in the promise of AI-driven process intelligence as the hype surrounding it swells.

Looking ahead, as the conversation shifts from potential to practicality, supply chains will benefit more from “augmented” business process management, in which AI introduces automation capabilities to enhance worker performance, especially when systems are under high stress. The resulting solutions will be driven by the human-machine interface.

Once this practice is standardized and validated, the industry will move toward an “autonomous” phase with self-correcting processes that require minimal human intervention. Here, systems will independently use AI to identify weaknesses, implement solutions, and run processes autonomously.

Extreme weather events like this summer’s hurricanes and heat waves are neither rare nor isolated events. They are recurring challenges whose consequences may manifest in any number of ways. As such, organizations must anticipate and proactively address the threats they pose.

This mindset shouldn’t be limited to just weather events. Process intelligence is pivotal for navigating any sort of disruption, such as fluctuating demand and resource strains. By utilizing AI in this capacity, businesses can visualize workflows, predict impacts and develop effective mitigation strategies to become less sensitive to the whims of external conditions.

J-M Erlendson is transformation engineering lead at Software AG.