> 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/analysis-dashboards.md).

# Analysis Dashboards

## Overview

Analytical Dashboards provide a flexible environment for exploring historical manufacturing data through custom visualizations. 

<figure><img src="/files/CDIyBEqXeX8jla0q9SOG" alt=""><figcaption></figcaption></figure>

Users can create tailored visualizations from a exhaustive list of operational KPIs, manufacturing parameters and custom formulas, allowing analysis to be aligned with specific business and operational objectives.

## Creating Custom Visualizations

Analytical Dashboards support the creation of custom graphs using historical production, equipment, process, and quality data. Each visualization can be configured based on the specific operational question being investigated.

{% stepper %}
{% step %}

### Graph definition

<figure><img src="/files/gAZ7drkQyINcn6KJw5Sz" alt=""><figcaption></figcaption></figure>

Every graph begins with a descriptive name that provides context for the analysis

> *Clear naming helps teams quickly identify the purpose of each visualization within the dashboard*
> {% endstep %}

{% step %}

### Parameter selection

<figure><img src="/files/ObMLxgY4eblsIfiGXRq6" alt=""><figcaption></figcaption></figure>

Users can select from a broad range of manufacturing KPIs and operational parameters available within the platform. The selected parameter determines the operational behavior being analyzed.
{% endstep %}

{% step %}

### Entity selection

Analysis can be scoped to specific operational entities including:

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

> *This flexibility enables investigation at both line and asset levels depending on the scope of the analysis.*
> {% endstep %}

{% step %}

### Time-related configuration

<figure><img src="/files/edQkxVlQbfkURlCx6YFi" alt=""><figcaption></figcaption></figure>

Users can configure how historical data is evaluated by selecting:

* Date range
* Data resolution
* Aggregation method

> *These settings help align visualizations with the desired level of analysis, whether reviewing hourly production behavior or long-term operational trends.*
> {% endstep %}

{% step %}

### Visualization configuration

<figure><img src="/files/XBTYu3UyWMWw2rLLhBRg" alt=""><figcaption></figcaption></figure>

Analytical Dashboards support multiple chart formats, enabling users to present information in a manner best suited to the data being evaluated.

Additional visualization settings include:

* Date range filters
* Resolution adjustments
* Chart type selection
* Graph configuration options

Once configured, users can preview the visualization before saving it to the dashboard.
{% endstep %}

{% step %}

### Custom formula analysis

<figure><img src="/files/tr73vN4S151PARpvfaF1" alt=""><figcaption></figcaption></figure>

In addition to standard manufacturing KPIs, Analytics Dashboards support the creation of custom formulas which allow users to combine multiple parameters and mathematical operations to create organization-specific performance indicators.

Once created, formulas become available alongside standard parameters and can be used in any analytical visualization.

> *Custom formulas enable organizations to extend standard manufacturing analytics and align dashboard reporting with site-specific operational objectives.*
> {% endstep %}
> {% endstepper %}

## Managing Dashboard Visualizations

Once a graph has been created and saved, it becomes part of the Analytical Dashboard.

Visualizations can be updated as operational priorities evolve, ensuring dashboards remain aligned with current manufacturing objectives.

Users can modify existing graphs to:

* Update parameters
* Change visualization settings
* Adjust date ranges
* Refine analytical focus

> *This flexibility allows dashboards to evolve alongside production improvement initiatives.*

## Best Practices

### Focus on operational questions

Create dashboards around specific business questions rather than collecting unrelated metrics.

For example:

* What is driving throughput loss?
* Which assets experience the most downtime?
* How has quality performance changed over time?

### Compare related metrics

Combining complementary KPIs often reveals insights that individual metrics cannot.

Examples include:

* OEE and Downtime
* JPH and Quality
* Production Output and Availability

### Review trends, not individual events

Sustainable operational improvements are typically driven by recurring patterns rather than isolated incidents.

Use Analytical Dashboards to identify long-term trends and recurring operational behaviors.

## Operational Outcome

Analytical Dashboards transform manufacturing data into actionable operational insights.

By enabling users to build customized visualizations, analyze trends across multiple KPIs, and create organization-specific performance metrics, they support deeper investigation, more informed decision-making, and continuous operational improvement.