The ability to predict and prepare for disruptions has become a key differentiator for businesses aiming to maintain continuity and growth. Supply chains are under constant pressure to adapt to changing demand and unforeseen interference. To stay competitive and resilient, companies must move beyond reactive strategies and embrace predictive analytics as a core tool for anticipating disruptions and making smarter, data-driven decisions.
Ilya Levtov, co-founder and CEO at Craft, says often when companies learn about disruption, one or more suppliers in their supplier network are already negatively impacted. This results in downstream impacts that can ultimately affect revenue and reputational damage.
"When we consider forms of risk, we look at the events happening around the world that can impact supply chains. AI can analyze unlimited data sources to flag risks, such as factory fires, social unrest, geopolitical conflict, severe weather events, changing regulations, trade disputes, forced labor violations, cyber-attacks and many other forms of supply chain risk," says Levtov.
I've underscored the use case of AI in mitigating disruption many times in this blog series. Why? Because the power of AI, specifically in predictive models, can lead to better decision making and quicker action. Success in this regard, Levtov explains, hinges on the quality and comprehensiveness of the underlying data.
"By bringing together supplier risk, operational and product-level data, and n-tier mapping, AI can interpret this information to monitor changes to supplier information and flag signs of risk, identify initial supply chain risks and map insights back to product lines to understand potential revenue at risk," says Levtov. "With better visibility, deeper insights and AI-driven action plans, supply chain risk management can become more strategic and proactive rather than reactive.”
Here are 5 steps towards leveraging predictive analytics:
- Collect high-quality, real-time data.
- Cleanse and preprocess data to ensure accuracy and reliability.
- Develop and test predictive models using AI, machine learning algorithms or statistical techniques.
- Once implemented, continuously monitor model performance and refine algorithms as needed.
- Share predictive insights with relevant stakeholders to promote collaboration and visibility.
Supply chain resiliency requires the ability to predict, prepare and act on insights before disruptions occur— this is no longer a luxury. With strong predictive analytics, businesses can transform uncertainty into opportunity, which leads to more agile supply chains for the future.
