Truly adaptive supply chains aren’t built overnight. But in today’s increasingly volatile environment, they are more crucial than ever.
To thrive, businesses must go beyond reaction and recovery, leveraging an expanding arsenal of powerful tools and technology to forecast, anticipate, and respond to rapid changes. This shift from reaction to prediction is no longer optional – it’s essential for success.
Whether navigating geopolitical conflicts or climate-related weather events, supply chain managers must be ready to manage the ripple effect on their operations. By leveraging AI, predictive, and prescriptive analytics, supply chain teams can gain newfound control to accurately forecast demand and optimize decision-making.
A persistent state of disruption
It seems every day brings news of another supply chain disruption. Catastrophic weather events, international disputes, trade tensions, and more continue to drive instability at almost every level of logistics.
2024 data from Resilinc reveals a nearly 40% increase in global supply chain disruptions compared to 2023, a year that hinted at gradual stabilization following major upheaval. Specifically, political unrest saw a 285% YOY increase in 2024, the largest increase of all risk events. Extreme weather also more than doubled, growing by 119% YoY, including floods, forest fires, and hurricanes/typhoons, all of which significantly impacted the supply chain.
These disruptions are challenging, but they present an opportunity for operators. At every level of the supply chain, there is data – structured and unstructured – that can be harnessed, analyzed, and utilized by AI to improve decision-making. Sources like logistics providers, suppliers, shop floor systems, financial information, weather forecasts, and even social media traffic can provide important insights that could benefit supply chain planning. How companies use this data will make a difference in the long run.
Forecast, anticipate, respond 2.0
For decades, supply chain managers have operated with a “forecast, anticipate, respond” philosophy to prepare for potential disruptions and react swiftly to remain resilient.
This approach has proven effective, even without the use of the latest technology. Successful supply chain teams have used moving averages and market research to plan for future demand and built a diverse supplier networks to mitigate potential risks in certain regions around the world. These measures inform how they manage production schedules or route shipments in response to changes throughout the supply chain.
In recent years, AI and machine learning platforms have supercharged how companies “forecast, anticipate, and respond.” Real-time insight into inventory, sales trends, location data, and more are helping reduce costs and making supply chain planning more prescriptive and predictive. Going forward, teams can act with even more confidence when preparing for what’s to come.
Equipped with these tools, the era of “forecast, anticipate, respond” may be set for the next stage in its evolution.
The vision for AI in supply chain operations
The implementation of AI within supply chains has been swift. But the complexity of modern-day supply chains requires significant time and effort across departments, including procurement, QA, production and more. Without a direct business benefit, the investment required to properly integrate AI may outweigh the end-result.
That’s why 63% of businesses “have an AI strategy linked to business objectives” to improve operational efficiency, business resilience, and increase employee productivity, according to an IDC 2024 Supply Chain Survey. Using AI to analyze patterns, optimize processes, and provide new insights can ultimately help them make a direct business impact.
Looking ahead, AI will find uses at nearly every stage of operation:
- Route optimization
o From sourcing raw materials to manufacturing and delivery, AI is helping companies transport items more efficiently. AI trip planning analyzes traffic, weather, and mapping data to reduce fuel consumption. With specialized AI solutions, companies can see a holistic view of their supply chain that a non-AI system could provide. By accessing more efficient routes, companies can also streamline their inventory.
- Inventory management
o AI-powered predictive analytics are helping retailers transport the right goods to the right places at the right time. They’re also providing important insights that can influence the initial manufacturing of these goods. Optimized routes can improve overall sustainability by reducing overproduction, minimizing excess inventory, and preventing unnecessary waste.
- Equipment maintenance
o The machinery moving goods from Point A to Point B is just as important as the goods themselves. Informed by sensor data on equipment like trucks and drills, AI can offer predictive maintenance alerts when equipment needs servicing and can even adjust production schedules to plan for downtime, minimizing unplanned maintenance outages.
As companies analyze a stream of data such as delivery locations, traffic patterns, equipment status, and weather conditions, they can help predict future lead times more accurately.
Together, these insights and analytics empower businesses to make faster, more informed decisions in response to evolving market conditions and world events – bringing us closer and closer to this new vision for supply chain management.
