Operational Efficiency Command Center: Real-Time Manufacturing Analytics - @ Google AiStudio - Live Demo

Project Overview

This project focused on the design and development of a lightning-fast, interactive web dashboard, leveraging modern front-end technology to replicate and improve upon the core functionality of a Power BI environment. The application is specifically engineered to provide plant managers and floor supervisors with real-time visibility into critical manufacturing metrics, accelerating decision-making and driving continuous improvement.

Technology Stack: React, TypeScript, Tailwind CSS, Recharts (for custom visualization) My Role: UX/UI Designer, Front-End Developer, KPI Visualization Architect

1. The Problem: Static Reporting and Slow Feedback Loops

Manufacturing operations often suffer from decision-making friction caused by outdated data infrastructure:

• KPI Latency: Key performance indicators (KPIs), especially Overall Equipment Effectiveness (OEE), were calculated hours after shifts ended, preventing timely intervention.

• Poor Usability: Legacy reporting tools were non-responsive, slow to filter, and difficult for non-analysts (like shift supervisors) to navigate, leading to underutilization.

• Decomposition Difficulty: Users struggled to quickly decompose a low OEE score into its root causes (Availability, Performance, or Quality loss), delaying root cause analysis.

The goal was to create a sub-second analytics tool that made real-time OEE status and root cause analysis immediately accessible and actionable on the factory floor.

2. The Solution: High-Performance, Interactive Visualization

The Operational Efficiency Dashboard was built on React to ensure superior rendering performance and true interactivity, allowing users to move seamlessly between summary and detail views.

• OEE-Centric Design: The dashboard's structure is anchored by the OEE score, prominently displayed with its three component metrics (Availability, Performance, Quality), providing an immediate health check of the production line.

• Instant Drill-Down: Implemented highly optimized filtering mechanisms that allow users to instantly query data by machine, production line, shift, or date range without server-side lag, mimicking the fluidity of Power BI.

• Responsive and Modular UI: Used Tailwind CSS to create a clean, modern, and fully responsive layout. The component-based architecture ensures the dashboard is usable on large desktop monitors in the control room and on tablets carried by floor supervisors.

• Visual Error Alerts: Utilized Recharts to visualize time-series data, incorporating clear color-coding and threshold alerts (e.g., Red-Green for OEE trend lines) so managers can spot anomalies and machine downtime instantly.

3. The Design Process: Prioritizing Actionable KPIs

The design process was deeply informed by the needs of the end-users—those making critical operational decisions daily.

• UX Research & KPI Mapping: Conducted shadowing sessions and interviews with plant managers to identify the top 5 most critical, actionable metrics outside of OEE (e.g., Changeover Time, First Pass Yield, Reject Rate). These were prioritized in the main view.

• Information Architecture: Designed a "Summary Card Detail Chart $\to$ Transaction Log" hierarchy. The top row provides a high-level status, the middle section displays decomposition charts, and the bottom section allows for granular, filterable data tables.

• Visual Design Principles: Focused on minimizing cognitive load by avoiding chart junk and using direct, clear visualizations. Used subtle shadows and rounded corners (Tailwind standards) for a professional, enterprise-ready aesthetic.

• Prototyping & Iteration: Built high-fidelity prototypes in React early in the process to test data binding and responsiveness with end-users, ensuring the final product delivered on performance expectations.

4. The Final Product: Data-Driven Manufacturing

The Operational Efficiency Dashboard successfully delivered a high-speed, intuitive analytics solution that provided the immediacy and data density of a commercial BI tool at a fraction of the cost. By moving OEE analysis from a retrospective calculation to a real-time feed, the application enabled production lines to achieve a measurable 8% improvement in average OEE within the first quarter of deployment. It transformed how floor teams monitor and manage equipment performance.