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”
Do these questions sound familiar?
Manufacturers run on causal questions
- 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?
- What are the root causes of poor OTIF levels?
- What are the best actions (interventions) to improve OTIF levels given my budget constraints?
- 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?
- 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?
- 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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the first operating system for decision making powered by Causal AI, to address all those causal questions
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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
$50bn Global Electronics Company chooses causaLens to revolutionize its early warning system for faulty parts with Causal AI
Textile manufacturer sees a 10% reduction in order delays by adopting Causal AI
$10bn Metals Enterprise sees an expected return of $4M from maximal throughput while reducing downtime
Proven value in weeks
Two to three hours
Three to four weeks