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Causal AI & LLM synergies: Enterprise decision making needs more than chatbots

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Supply Chain Optimization

Traditional, correlational, machine learning approaches often fail to improve supply chain management

We brought to market the first operating system for decision making powered by Causal AI, decisionOS, to empower enterprises to optimize their supply chain management

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Traditional machine learning approaches often fail

to improve supply chain management

Correlation, not causation

Spurious correlations lead to bad decisions


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How can AI discover cause and effect

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They struggle to answer why

And are often perceived as “black boxes”


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Explainable AI doesn’t explain enough

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Prediction, not next best action

For example, they can predict if there will be a delay in an order but can’t recommend the next best action to prevent the delay

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From predicting to Influencing

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Do these questions sound familiar?

Enterprises run on causal questions


What actions do I need to take to optimize on-time in-full levels?


What decisions are impacting my margins when analyzing my fulfillment strategy, and how can I optimize those decisions?

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How will certain initiatives impact my in-store operations?


Which are the right suppliers for my components, given costs, lead times and demand levels?

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How might future supply chain disruptions impact my operations, what actions should I take?

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What actions should I take to reduce scheduling inefficiencies?

Answer these questions using Causal AI

Discover cause-effect relationships within your marketing data

Discover cause-effect relationships in your supply chain data by combining the best of domain knowledge with data-driven, statistical, approaches

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Build Causal Models

Discover causal models, based on cause-effect relationships and causal graphs, that can robustly predict estimate the effect of changes (e.g. sourcing materials from a new supplier) and allow you to run powerful what-if scenarios

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Make better decisions with Decision Intelligence Engines

Leverage our pre-built engines on top of your causal models to:


Understand root causes

Automatically rank the potential root causes of delays, inefficiencies or process errors

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Design next best actions

E.g Decide where you should source a materials from

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Perform powerful what-if analyses and estimate counterfactual scenarios

Understand the impact of a 10% increase in demand

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Build Decision Apps

Leverage Dara, our app building framework, to seamlessly deliver beautiful, interactive decision making applications to the supply chain & executive teams

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Monitor and optimize supply chain decisions with decisionOps

Build, deploy & monitor your decision workflows while attributing KPI performance to the appropriate actions whether that’s yours, your competitor’s actions or the macro-economic environment

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Customer Case Study: Inventory Optimization

A leading manufacturer of IT products sees $6mn in savings from matching inventory levels to customer demand more accurately

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Cisco's Forecasting Data Science Leader, Puneet Gupta

At the Causal AI Conference 2022

Proven value in weeks

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    Internal meeting

    One hour

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    Scoping sessions

    Two to three hours

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    Proof of Concept

    Three to four weeks

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    Deployment in production

    Twelve months

Start your Causal AI Journey

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