The ability to predict and prepare for disruption has become a key differentiator for businesses that want to maintain continuity and growth. Supply chains are under constant pressure to adapt to changing demands and unforeseen interference. To remain competitive and resilient, businesses must move beyond reactive strategies and embrace predictive analytics as an essential tool for anticipating disruption and making smarter, data-driven decisions.
Ilya Levtov, co-founder and CEO of Craftsmanship, says that often when businesses learn of a disruption, one or more suppliers in their supplier network are already negatively affected. This leads to downstream impacts that can ultimately affect revenue and damage reputation.
“When we consider forms of risk, we look at events happening around the world that can impact supply chains. AI can analyze unlimited data sources to report risks, such as factory fires, social unrest, geopolitical conflicts, extreme weather events, regulatory changes. , trade conflicts, forced labor violations, cyberattacks and many other forms of supply chain risks,” says Levtov.
I have repeatedly highlighted the use case for AI to mitigate disruption in this blog series. For what? Because the power of AI, especially in predictive models, can lead to better decision-making and faster action. Success in this regard, Levtov explains, depends on the quality and completeness of the underlying data.
“By bringing together supplier risk, operational and product-level data, and n-level mapping, AI can interpret this information to monitor changes to supplier information and flag signs of risk , identify initial supply chain risks, and map product line information 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 to take advantage of predictive analytics:
- Collect high-quality data in real time.
- Clean 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 necessary.
- Share predictive insights with relevant stakeholders to promote collaboration and visibility.
Supply chain resilience requires the ability to predict, prepare and act on information before disruptions occur.— it is no longer a luxury. With strong predictive analytics, businesses can turn uncertainty into opportunity, leading to more agile supply chains for the future.
