Merge pull request #276978 from dominicbetts/aio-feedback-2

AIO: Address feedback

Author: prmerger-automator[bot]

articles/iot-operations/get-started/quickstart-get-insights.md

@@ -11,7 +11,7 @@ ms.date: 04/25/2024
 #CustomerIntent: As an OT user, I want to create a visual report for my processed OPC UA data that I can use to analyze and derive insights from it.
 ---
 
-# Quickstart: Get insights from Deploy Azure IoT Operations Preview to an Arc-enabled Kubernetes cluster
+# Quickstart: Get insights from your processed data
 
 [!INCLUDE [public-preview-note](../includes/public-preview-note.md)]
 

articles/iot-operations/view-analyze-data/tutorial-overall-equipment-effectiveness.md

@@ -793,11 +793,14 @@ In [Microsoft Fabric](https://msit.powerbi.com/groups/me/list?experience=power-b
 1. Select **DirectQuery** as the connection setting and then select **OK**.
 
 You can now create measurements and tiles to display OEE for your production lines by using formulae such as:
-- `OEE = Availability\*performance\*Quality`
-- `Performance = TotalUnitsProduced/10 (StandardProductionOutput)`
+
+- `OEE = Availability*Performance*Quality`
+- `Performance = TotalUnitsProduced/10`
 - `Availability = TotalOperatingTime/PlannedProductionTime`
 - `Quality = TotalGoodUnitsProduced/TotalUnitsProduced`
 
+The performance calculation above uses a factor of 10 in the calculation. This factor is specific to Contoso bakery and uses an estimate of the ideal cycle time for the production line. To learn more, see [Overall equipment effectiveness](https://wikipedia.org/wiki/Overall_equipment_effectiveness).
+
 Follow these steps to create some measures and use them to build a visualization dashboard.
 
 1. From the top navigation plane, select **New measure**. Paste the following data analysis expression into the main text field: