Transport & Logistics
Current machine learning approaches are not fit to optimise 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.
Transportation looks to pick up the pace
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:
- Non-causal ML techniques are likely to include spurious correlations, making it difficult to make confident decisions based on their insights
- Macroeconomic drivers can change quickly with significant knock-on consequences to demand, but traditional ML fails to adapt fast enough to regime changes
Solutions
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.
Why causaLens
Which level is your organisation in?
No Machine Learning
- Do you think it would take you years to become an AI-driven Enterprise?
- Is the value of your enterprise’s data asset largely unrealised?
- Are the predictions, insights and business decisions compromised due to human error?
- Are resources wasted improvising solutions to problems that could be better solved with ML?
Manual Machine Learning
- Is it hard to build and retain data science teams?
- Does it take months or years to achieve real business impact?
- Is the team mostly reactive, responding to requests from business functions for analytics?
- Does your team spend their time implementing repetitive & expensive processes?
Automated Machine Learning
- Do you find that generic Data Science Platforms overfit & underperform?
- Do you have trouble translating predictive models & predictions to real value for the business?
- Are generic platforms not flexible or transparent enough while failing to incorporate your domain expertise?
Reach the top levels of Enterprise AI for Transport & Logistics
Causal AI
- Goes beyond modelling correlations in historical data and understands true causal drivers
- Autonomous, from raw data directly to causal signals, predictive models & insights
- Inherently explainable
- Adaptable and works in regime shifts
- Intuitively incorporate domain expertise
- Works in low-signal-to-noise environments
Strategy Optimisation AI
- Automated KPI Optimisation.
- Includes all benefits of Automated Causal AI.
- Goes beyond predictive models and integrates the business context and the domain knowledge in a meaningful way.
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.
