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Causal AI for Supply Chain

Current machine learning approaches are not fit to optimize the movement of people or goods around the globe. These systems make untrustworthy predictions in an increasingly complex and volatile economy. Utilising Causal AI in Transport & Logistics enables companies to elevate their decision-making processes.

Key challenges for transportation and logistics clients

  1. Reducing inefficiencies across vast networks
  2. Analysing big datasets in real time
  3. Unforgiving standards of operational precision
  4. Unreliable machine learning technology


Forecasts based on standard machine learning techniques only extrapolate from historical trends, making them fragile to new market entrants, product line changes and macroeconomic shocks. causaLens uncovers the true causal structure behind the supply chain to create predictive causal models for any supply chain metrics. These models autonomously adapt up to three times faster than current state-of-the-art machine learning to disruptions in the marketplace. CausalNet, causaLens’ proprietary inference engine, delivers actionable insights that include but also go beyond just SKU predictions and sales forecasts, directly solving business problems by, for example, providing dynamically updated optimal warehouse layouts.

Volatility in the freight industry, driven by financial and trade turbulence, implies an enormous premium for accurate changepoint detection. Current predictive methods rely on historical statistics and are therefore often caught out by new behaviours. causaLens uses cutting-edge AI Causal Discovery to uncover the true structure behind freight rates, taking into account the evolving relationships between shipping cycles, consumer sentiment and macroeconomic changes. CausalNet, causaLens’ proprietary inference engine, delivers actionable insights to directly optimise business KPIs, such as how to simultaneously satisfy all bookings whilst maximising margins.

Last mile and on-demand delivery present challenges as well as opportunities. The secular trend towards e-commerce implies significant growth potential, but consumers have high standards and competition is fierce. AI promises logistics companies an edge, but current machine learning solutions are business blind, with results that are often not directly actionable. causaLens’ solution includes, but goes beyond, dynamic demand forecasting. CausalNet, our proprietary inference engine, directly solves delivery problems. For example, it can autonomously determine the optimal allocation of orders across the fleet in real time.

The State of AI in Supply Chains

Learn how Causal AI can be used to enhance Supply Chain modelling by listening to ‘The State of AI in Supply Chains’ panel at the Ai4 Supply Chain Conference.