---
name: operational-dashboard-generator-3ddbb575e47a
description: Real-time operational performance dashboard skill with KPI visualization and alerting
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
metadata:
  specialization: operations
  domain: business
  category: operational-analytics
graph:
  domains: [domain:operations]
  skillAreas: [skill-area:data-analytics, skill-area:data-visualization, skill-area:business-analysis]
  workflows: [workflow:vendor-onboarding, workflow:vendor-evaluation]
  roles: [role:operations-analyst, role:bi-developer, role:business-analyst]
---

# Operational Dashboard Generator

## Overview

The Operational Dashboard Generator skill provides comprehensive capabilities for creating real-time operational performance dashboards. It supports KPI definition, visual management displays, trend analysis, and alert configuration.

## Capabilities

- KPI definition and calculation
- Real-time data integration
- Visual management displays
- Trend analysis
- Alert threshold configuration
- Drill-down reporting
- Mobile dashboard access
- Executive summary generation

## Used By Processes

- CI-001: Operational Excellence Program Design
- SIX-002: Statistical Process Control Implementation
- QMS-003: Quality Audit Management

## Tools and Libraries

- Power BI
- Tableau
- Grafana
- Real-time data platforms

## Usage

```yaml
skill: operational-dashboard-generator
inputs:
  dashboard_type: "operations_center"  # executive | operations_center | shop_floor
  kpis:
    - name: "OEE"
      target: 85
      warning_threshold: 75
      critical_threshold: 65
      calculation: "(availability * performance * quality)"
    - name: "On-Time Delivery"
      target: 98
      warning_threshold: 95
      critical_threshold: 90
      calculation: "(on_time_orders / total_orders) * 100"
  data_sources:
    - type: "mes"
      connection: "mes_api"
    - type: "erp"
      connection: "sap_bapi"
  refresh_rate: 60  # seconds
outputs:
  - dashboard_definition
  - visualization_specs
  - alert_configuration
  - data_model
  - mobile_view
```

## KPI Categories

### Production KPIs
| KPI | Definition | Target |
|-----|------------|--------|
| OEE | Equipment effectiveness | >85% |
| Throughput | Units per hour | Per plan |
| Yield | Good units / Total units | >99% |
| Cycle Time | Time per unit | At takt |

### Quality KPIs
| KPI | Definition | Target |
|-----|------------|--------|
| First Pass Yield | Pass first time | >98% |
| Defect Rate | Defects per million | <1000 |
| Scrap Rate | Scrap cost % | <1% |
| Customer Complaints | Per period | Trending down |

### Delivery KPIs
| KPI | Definition | Target |
|-----|------------|--------|
| On-Time Delivery | % shipped on time | >98% |
| Lead Time | Order to ship | Per commitment |
| Schedule Attainment | Actual vs. plan | >95% |
| Backlog | Past due orders | Zero |

## Dashboard Design Principles

### Information Hierarchy
1. Key metrics (big numbers)
2. Trends (charts)
3. Details (tables)
4. Drill-down (links)

### Visual Best Practices
- Use color meaningfully (RAG status)
- Minimize clutter
- Consistent layout
- Clear labels
- Real-time updates

## Alert Configuration

| Level | Threshold | Action |
|-------|-----------|--------|
| Info | Near target | Monitor |
| Warning | Below target | Investigate |
| Critical | Significantly below | Immediate action |

## Dashboard Layers

### Executive Dashboard
- High-level summary
- Company-wide view
- Weekly/monthly trends
- Strategic KPIs

### Operations Center
- Site-level view
- Daily/hourly trends
- Operational KPIs
- Resource status

### Shop Floor Display
- Real-time status
- Line-level view
- Visual management
- Immediate feedback

## Integration Points

- Manufacturing Execution Systems
- ERP systems
- Quality Management Systems
- IoT/sensor data


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Operations, Product Management
Roles: Business Process Analyst, Data Analyst, Growth Product Manager, Operations Manager, Product Manager
Specialties: API, Database
Tools: Grafana, Power Bi, Tableau
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 7f8d80494363a16fb159c2be9458e92e58fff1caeb521b5a1c40bdca6732f36c