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Manufacturing Root Cause Analysis

Traditional, correlational, machine learning approaches often fail to improve manufacturing processes

We have brought to market the first operating system for decision making powered by Causal AI, decisionOS, to empower enterprises to optimise their manufacturing processes

Traditional machine learning approaches often fail

to discover root causes of anomalies or inefficiencies as well as recommend the next best action (intervention) to prevent those from happening

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Correlation, not causation

Spurious correlations lead to bad decisions

 

Read more on our blog:
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

E.g they can predict if a machine will break but they struggle to explain why and what the next best action is to prevent future issues

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From predicting to Influencing

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Do these questions sound familiar?

Enterprises run on causal questions

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What are the root causes of manufacturing process inefficiencies or defects?

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What are the best actions (interventions) to mitigate inefficiencies and/or defects?

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What actions can I take to maximize my Overall Equipment Effectiveness?

See the solution in practice

Interactive Example

License our platform to answer these questions using Causal AI

Discover cause-effect relationships within your Manufacturing data

Discover cause-effect relationships in your production line 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 anomalies, figure out the root cause & recommend next best actions to mitigate those

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Make better decisions with Decision Intelligence Engines

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

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Understand root causes

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

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Design next best actions

Prevent defects, inefficiencies or process errors ahead of time

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Perform powerful what-if analyses and estimate counterfactual scenarios

E.g understand the probability of defects if you increase electricity levels

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Build Decision Apps

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

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Monitor and optimize manufacturing decisions with decisionOps

Build, deploy & monitor your decision workflows to maximize Overall Equipment Effectiveness

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Related Case Studies

Customer Case Study: Early Fault Detection

$50bn Global Electronics Company chooses causaLens to revolutionize its early warning system for faulty parts with Causal AI

Customer Case Study: Manufacturing Downtime Reduction

$10bn Metals Enterprise sees an expected return of $4M from maximal throughput while reducing downtime

Customer Case Study: Scrap Parts Reduction

A global manufacturer sees 10x ROI from enhancing their manufacturing processes by comprehending the underlying reasons behind failures in their production lines

Causal AI at Bosch

Watch the talk from the Causal AI Conference 2023

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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    Platform Trial

    Three to four weeks

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    Deployment in production

    Twelve months

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