Supply Chain Root Cause Analysis with Causal AI

Presented by

Andre Franca, VP Research & Development, causaLens

About this talk

Prediction is at the core of running an efficient supply chain. Whether it’s forecasting demand or the weather; prediction enables our vastly complex global supply chains to operate in real-time and deliver on customer needs. However, the current approach to machine learning relies on past patterns and correlations to make predictions about the future – which makes it prone to fail in a constantly changing world. This talk introduces Causal AI and its impact on identifying the Root Cause of delays throughout the entire supply chain, providing recommendations to mitigate delays and risk, and empowering supply chain, operations, and manufacturing leaders to make decisions that will improve customer service while reducing costs. After watching this session, you’ll be able to: Explain how Causal AI will improve AI explainability, trust, and human interaction Understand how Causal AI overcomes the limitations of traditional AI and why over 85% of current AI projects fail. Leverage Causal AI to vastly improve capacity utilization, On-Time & In-Full (OTIF) service levels while reducing safety stock Explain how causaLens decisionApps can help teams run what-if scenarios at scale and use counterfactuals to re-imagine your supply chain

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causaLens is the pioneer of Causal AI—a giant leap in machine intelligence. Today’s machine learning algorithms extract correlations from data and predict outcomes based on patterns in past data. Correlations are useful for making predictions, but they’re of little use for decisions. Causal AI goes beyond predictions by understanding the actual causes behind an outcome and quantifying the impact of different interventions. It is the only form of machine intelligence that can answer “Why?”. causaLens builds Causal AI-powered products that empower all users to make superior decisions and drive business value. Leading organisations across a wide range of industries trust causaLens with their most important decisions.