> 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.md).

# Dashboards

## Overview

Dashboards serve as the operational intelligence layer of the Linecraft platform.

They consolidate production, quality, downtime, and other crucial information into analytical workspaces, enabling teams to monitor manufacturing performance and identify emerging issues without navigating across multiple modules.

<figure><img src="/files/JUEwtDhhHd21owvd4aap" alt=""><figcaption></figcaption></figure>

Typical use cases include:

* Monitoring production performance against targets
* Tracking OEE, throughput, and quality trends
* Identifying recurring operational losses
* Investigating downtime patterns
* Comparing performance across shifts, assets, or production lines
* Surfacing improvement opportunities through data visualization

## Why Dashboards Matter

Manufacturing performance is influenced by multiple interconnected factors:

* Equipment availability
* Production speed
* Quality losses
* Downtime events
* Process conditions

Without a consolidated analytical view, identifying the source of performance degradation becomes difficult and time-consuming.

Dashboards help operational teams:

* Detect abnormal production behavior early
* Monitor KPI trends over time
* Improve response times during production issues
* Create a common operational view across teams

## Dashboard Types

Linecraft dashboards are designed to support different layers of manufacturing analysis, from executive KPI monitoring to deep operational investigations and process-level monitoring.

Each dashboard type serves a distinct analytical purpose.

{% stepper %}
{% step %}

### Template Dashboards

Template Dashboards provide pre-configured analytical views built around common manufacturing use cases.

> *They accelerate dashboard creation by allowing users to deploy proven dashboard structures without manually configuring visualizations.*
> {% endstep %}

{% step %}

### Analysis Dashboards

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

> *This enables teams to investigate specific business questions, analyze performance trends, and uncover relationships between different operational variables.*
> {% endstep %}

{% step %}

### IoT & Quality Dashboards

IoT & Quality Dashboards extend operational visibility beyond production metrics and into process behavior.

> *These dashboards allow teams to visualize real-time and historical process parameters collected from machines of the manufacturing plan*
> {% endstep %}

{% step %}

### KPI Dashboards

KPI Dashboards provide real-time visibility into key manufacturing KPIs through configurable visualizations, spanning across 40+ KPIs

> *KPI Dashboards provide a live operational view of manufacturing performance, helping teams detect deviations, maintain production stability, and make timely decisions during active production.*
> {% endstep %}
> {% endstepper %}

## Best Practices

To maximize dashboard effectiveness:

### Focus on actionable metrics

Prioritize metrics that influence operational decisions rather than purely informational indicators

### Align dashboards with operational objectives

Different teams require different views:

| Team        | Primary Focus                                    |
| ----------- | ------------------------------------------------ |
| Production  | Throughput, OEE, Output                          |
| Maintenance | Availability, MTTR, MTBF                         |
| Quality     | Reject Trends, Process Stability                 |
| Leadership  | Performance Trends and Improvement Opportunities |

### Review trends, not single data points

Individual events rarely tell the full story. Focus on:

* Recurring patterns
* Trend direction
* Variability over time

### Periodically refine visualizations

As operational priorities evolve, dashboards should evolve with them. Remove unused visualizations and introduce new views that support current improvement initiatives.

## Operational Outcome

Dashboards transform production data into operational visibility.

By combining production, asset, quality, and process intelligence within unified analytical workspaces, teams can identify constraints faster, investigate performance issues with greater confidence, and make informed decisions that improve manufacturing outcomes.