For decades, manufacturers have relied on traditional analytics; correlations, trendlines, and dashboards to make operational decisions.

But there's a limit:

𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 ≠ 𝐂𝐚𝐮𝐬𝐚𝐭𝐢𝐨𝐧

Just because two variables move together doesn’t mean one causes the other.

This blind spot can lead to poor decisions and surface-level fixes that don’t solve the real issue.

For example, a machine’s temperature spikes often coincide with defects. Traditional analytics might alert you when it happens—but not why. Is it the temperature? A faulty sensor? Operator error?

Causal Inference flips the script. Instead of just observing data patterns, it asks:“What actually caused this outcome?”

‍

Watch/Listen below:
‍