Problem

Manufacturing production and assembly lines are how most modern products are made, from silicon computer chips to vaccines. They are inherently complex systems with a large number of moving parts and interdependencies, which makes it difficult to tune them to get the most out of them. Traditional methods of optimising production lines are often based on trial and error or bespoke digital twins, both of which are time-consuming and expensive in their own ways. Standard AI approaches also fail because they cannot capture the cause and effect relationships between components in a production line, making their optimisations poor or even harmful.

Solution

Causal models accurately capture the relationships between components in production line systems. This makes it simple and inexpensive to simulate how factors like yield and byproduct production will change when machine settings are varied. CausaDB's optimisation functionality also makes it easy to find the optimal machine settings needed to achieve a desired outcome, like maximising yield while minimising byproduct production. These objectives can also be weighted, depending on business needs (for example if the primary goal is to maximise yield, but a less important secondary goal is to minimise byproduct, our algorithm can take care of it).

CausaDB can be easily integrated with existing technology stacks through a variety of interfaces to enable truly data-driven optimisation without heavy integration work.

Value

• Improve margins by increasing yield without increasing material costs
• Cut costs by reducing waste and byproduct production
• Improve carbon emissions by reducing wasted energy

Demo

You can find a technical example of how to optimise production lines in CausaDB on our documentation site.

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