PE Dashboard: Production Engineering & Maintenance Intelligence Platform
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PE Dashboard: Production Engineering & Maintenance Intelligence Platform

Next.jsTypeScriptPrisma ORMPostgreSQLTailwind CSSAI InsightsTelegram Notifications
projectDetail.role

Full Stack Production Engineering Developer

projectDetail.year

2025 - 2026

projectDetail.duration

Ongoing

projectDetail.type

projectDetail.industrialAutomation

projectDetail.theChallenge

Factory maintenance operations were fragmented across spreadsheets, isolated forms, and manual follow-ups. Machine history, repair requests, preventive maintenance schedules, and spare-part usage were not connected in one workflow, making it difficult to prioritize risk, trace recurring failures, or give management a real-time view of plant reliability.

projectDetail.theSolution

I built PE Dashboard, a full-stack maintenance intelligence platform that unifies machine asset management, repair request workflows, preventive maintenance, spare-parts inventory, and AI-assisted analytics in a single Next.js application. The system combines role-based access control, QR/PDF operational flows, Telegram notifications, and structured risk insights so technicians, leaders, and requesters can work from one source of truth.

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After
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Operational Visibility
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projectDetail.systemDemo

PE Dashboard: Production Engineering & Maintenance Intelligence Platform demo preview

projectDetail.systemArchitecture

projects.systemArchitectureLive

Frontend

Next.js + TypeScript

Backend

Prisma + PostgreSQL

Operations

Maintenance + Spare + PM

Intelligence

AI Insights + Alerts
projects.latency: 12ms
projects.powerStable

projectDetail.lessonsLearned

- Workflow Coupling: Maintenance, machine history, and spare-part reservations cannot be designed as isolated modules. I linked these workflows so teams can trace each request from machine issue to repair action and part consumption without losing context. - Permission Design: Real factory users do not fit into a simple admin/user split. I implemented a finer-grained RBAC model so requesters, technicians, leaders, and managers each see the right actions without exposing the entire system. - AI Reliability: AI summaries are useful only when paired with deterministic fallbacks. I designed the analytics flow so dashboards can still return structured operational summaries even when the external AI provider is unavailable or misconfigured.

projectDetail.businessImpact

Centralized Maintenance Workflows | Real-Time Asset Visibility | AI-Assisted Risk Prioritization | QR/PDF Operational Tracking

projects.detailHeading

PE Dashboard is a factory-focused production engineering platform designed to connect maintenance execution, machine history, and spare-parts control into one operational system. Project Scope: - Built a centralized web application for machine assets, maintenance requests, preventive maintenance, work orders, and spare inventory. - Connected operational workflows with role-based permissions for requesters, technicians, leaders, and managers. - Added analytics and AI-assisted summaries to help teams identify high-risk machines, overdue PM tasks, and inventory pressure earlier. Key Features: - Machine Management: Tracks machine master data, criticality, warranty, downtime history, and repair linkage. - Maintenance Workflow: Handles request intake, approval flow, comments, attachments, and status progression. - Preventive Maintenance: Supports PM scheduling, task execution, and overdue visibility. - Spare Parts Control: Manages part master data, stock transactions, reservations, suppliers, and reorder logic. - Digital Operations: Includes QR-based tracking, PDF document generation, import/export utilities, and audit-friendly records. - AI & Alerts: Generates dashboard insights, predictive summaries, and notification flows for faster maintenance response. Technical Highlights: 1. Frontend: Built with Next.js App Router, TypeScript, Tailwind CSS, and reusable UI components. 2. Backend: Uses Prisma and PostgreSQL for strongly structured operational data and workflow relationships. 3. Intelligence Layer: Implements risk scoring, structured AI summaries, and cached analytics endpoints. 4. Notification Layer: Integrates Telegram-based alerts for maintenance and operational events. Business Value: - Replaced fragmented maintenance tracking with a single source of truth. - Improved traceability between machine failures, repair execution, and spare-part usage. - Gave production engineering teams faster visibility into risk, backlog, and reliability priorities.

projects.technologies

  • Next.js
  • TypeScript
  • Prisma ORM
  • PostgreSQL
  • Tailwind CSS
  • AI Insights
  • Telegram Notifications

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projects.totalAssets: 4
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