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Manufacturing

Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the most crucial questions across process engineering, quality control, supply chain, procurement, complexity management, customer experience as well as centralised planning & strategy

Traditional machine learning approaches often fail to address critical business questions

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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”

 

Read more on our blog:
Explainable AI (XAI) doesn’t explain enough

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Prediction, not next best action

For example, they can predict if a machine will break down but can’t recommend the next best action to prevent the problem

Read more on our blog:
From predicting to Influencing

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Do these questions sound familiar?

Manufacturers run on causal questions

Process Engineering & Quality Control
  • What are the root causes of process inefficiencies or defects?
  • What are the best actions (interventions) to mitigate those inefficiencies and/or defects?
  • What are the best actions (interventions) to optimize throughput while keeping energy costs, defects ratio, quality, & maintenance costs under control?
Supply Chain resilience
  • What are the root causes of poor OTIF levels?
  • What are the best actions (interventions) to improve OTIF levels given my budget constraints?
Procurement & Complexity Management
  • What are the root causes of delays?
  • How do more product configurations impact our profitability and delays? What is the optimal balance between product options, profitability, & delays?
  • What is the optimal choice of vendors that balance quality & delays?
Customer Experience
  • What are the root causes of churn and/or returns? 
  • Which customer support tickets need to be prioritized?
  • What are the optimal discount levels that balance margins and customer acquisition?
Centralized planning & strategy
  • What investments will help me achieve my sales and profit targets while improving customer experience?
  • How will the competitive, market, and macro dynamics impact my PnL?
  • How will decisions in pricing, marketing or supply chain impact the rest of the business? (e.g will pricing decisions disrupt the supply chain?)

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Leverage decisionOS

the first operating system for decision making powered by Causal AI, to address all those causal questions

Causal AI

To move beyond traditional ML and into a world where you can provide actionable recommendations by leveraging state of the Causal AI tools and methods.

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DecisionApp Building

Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.

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DecisionOps

For the deployment and monitoring of decision workflow, trusting those workflows in production and measuring the causal impact of your decision-making.

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Trusted by leading organisations

[ML experts] don’t deploy [predictive] models in real products because these are safe critical, they don’t trust them… Putting our knowledge on the table together with engineers so that we can discuss cause-effect relationships in an intiuitve language was so much success.

Karim Said Barsim, Research Scientist

cAI Conference 2023

Understanding the causal drivers behind demand is critical, causaLens enhances our supply chain visibility and empowers our domain experts to run powerful what-if analyses.

Takashi Hiramatsu, Senior Manager, MLCC Planning Department

 

 

 

Causal systems are focused on modelling variable interactions. This makes it clear what’s going on under the hood and can also help us deliver solutions with a greater degree of confidence.

Alexandre Trilla, Senior Data Scientist

cAI Conference 2023

 

 

 

Causal AI at Bosch

Watch the talk from the Causal AI Conference 2023

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: Order Delays Reduction

Textile manufacturer sees a 10% reduction in order delays by adopting Causal AI

Customer Case Study: Manufacturing Downtime Reduction

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

Proven value in weeks

  • 1 Icon
    Internal meeting

    One hour

  • 2 Icon
    Scoping sessions

    Two to three hours

  • 3 Icon
    Platform Trial

    Three to four weeks

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    Production

    Twelve months

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