Current machine learning approaches are not fit to optimize the movement of people or goods around the globe. These systems fail to predict the future of an increasingly complex and volatile economy.
Causal AI separates signal from noise in dynamic, complex time-series data, enabling transportation and logistics companies to forecast and control the future.
Transport and logistics companies operate vast, geographically diverse networks. Inefficiencies can easily creep in, and opportunities can go unnoticed. Organizations can look to Machine Learning to reveal value in their data, but current AI techniques are often inadequate:
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.