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
Spurious correlations lead to bad decisions
And are often perceived as “black boxes”
Read more on our blog:
Explainable AI (XAI) doesn’t explain enough
For example, they can predict if a machine will break down but can’t recommend the next best action to prevent the problem
Do these questions sound familiar?
Manufacturers run on causal questions
See Our Solutions in Practice
Visit Demo HubLeverage 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.
Learn moreDecisionApp Building
Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.
Learn moreDecisionOps
For the deployment and monitoring of decision workflow, trusting those workflows in production and measuring the causal impact of your decision-making.
Learn moreTrusted 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
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1Internal meeting
One hour
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2Scoping sessions
Two to three hours
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3Platform Trial
Three to four weeks
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4Production
Twelve months