> For the complete documentation index, see [llms.txt](https://linecraft.gitbook.io/ikshana-pro-knowledgebase/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://linecraft.gitbook.io/ikshana-pro-knowledgebase/product-guides/dashboards/iot-and-quality-dashboards.md).

# IoT & Quality Dashboards

## Overview

The IoT & Quality Dashboard provides real-time visibility into manufacturing process conditions, machine signals and quality-related parameters.

While production KPIs such as OEE and throughput help measure operational outcomes, IoT and Quality parameters help teams understand the process conditions that influence those outcomes.

By visualizing live process data, the dashboard enables users to monitor critical operating conditions, detect abnormalities early, and investigate potential causes of production and quality issues before they impact performance.

## Creating Custom Visualizations

The IoT & Quality Dashboard supports the creation of customized visualizations tailored to specific process monitoring requirements.

Each graph can be configured using a combination of process parameters, quality measurements, and operational context.

{% stepper %}
{% step %}

### Graph definition

<figure><img src="/files/Y4lCBEvRM5MrhwELTqIH" alt=""><figcaption></figcaption></figure>

Every visualization begins with a descriptive graph name that clearly communicates its analytical purpose.

Examples include:

* Paint Booth Temperature Monitoring
* Weld Quality Tracking
* Process Stability Monitoring
* Compressed Air Consumption Analysis
  {% endstep %}

{% step %}

### Parameter selection

<figure><img src="/files/VO3vRrCVUgziE94NeWd2" alt=""><figcaption></figcaption></figure>

Users can select from available:

* Quality Parameters
* IoT Parameters
* Custom Formulas

The selected parameter determines the process behavior or quality characteristic being monitored.&#x20;
{% endstep %}

{% step %}

### Entity selection

Visualizations can be scoped to specific operational entities, including:

* Machines
* Cells
* Process Inputs/Outputs
* Machine States

> *This flexibility allows teams to focus analysis on the assets most relevant to their investigation.*
> {% endstep %}

{% step %}

### Visualization configuration

Users can configure:

* Date range
* Chart type
* Visualization settings

> *These options allow process data to be displayed in the format most appropriate for operational monitoring.*
> {% endstep %}
> {% endstepper %}

## Managing Dashboard Visualizations

Once a graph has been created and saved, it becomes part of the IoT & Quality Dashboard and is immediately available for ongoing monitoring.

As operational requirements evolve, existing visualizations can be modified to:

* Update monitored parameters
* Change chart configurations
* Refine process monitoring objectives
* Adjust asset scope

> *This ensures the dashboard remains aligned with changing production and quality priorities.*

## Best Practices

### Monitor leading indicators

Focus on process variables that influence production performance rather than waiting for performance metrics to indicate a problem.

Examples include:

* Process temperatures
* Equipment states
* Sensor measurements
* Critical quality characteristics

### Align visualizations with process risks

Prioritize monitoring parameters associated with:

* Quality losses
* Equipment failures
* Production instability
* Process variation

This improves the likelihood of detecting issues before they impact operational performance.

### Investigate process trends alongside production KPIs

Process data becomes most valuable when viewed in the context of operational outcomes. Compare process conditions with other KPIs to identify relationships between process behavior and production performance.

## Operational Outcome

The IoT & Quality Dashboard provides real-time visibility into the conditions that drive manufacturing performance.

By enabling continuous monitoring of process parameters, machine signals, and quality characteristics, it helps teams detect abnormalities earlier, maintain process stability, improve quality control, and make faster operational decisions based on live production conditions.