01 / Executive Summary

Engineered a highly responsive enterprise resource planning system for stock allocation, accounting audits, and auto-dispatched dealer approvals. Cut down inventory sync errors to near-zero and automated orders workflow.

02 / The Challenge

An agro-industrial firm managing 100+ dealers relied on manual Excel ledgers and paper signature systems. This resulted in an average 24% stock discrepancy between warehouses and a 4-day latency to process orders. Financial reports took minutes to generate because raw SQL queries scanned millions of unindexed records under heavy lock contention.

03 / Implementation & Solution

Built a rule-based state machine in Node.js that routes approval notifications in parallel based on transaction sizes. Implemented compound B-tree indexing in PostgreSQL and partitioned transaction tables by financial quarters. Developed a fast, glassmorphic Next.js dashboard featuring offline service worker synchronization.

04 / Architecture & Strategy

Next.js App Router for frontend UI, Node.js microservices for state-routing engine, and partitioned PostgreSQL database layer. Push alerts are broadcasted via real-time WebSocket connections.

05 / Diagnostics & Challenges

Resolving database lock contentions on transaction ledger tables during peak order dispatch periods. Optimizing Next.js client-side memory footprint for dashboards displaying real-time data feeds.

06 / Key System Features

• Parallel rule-based state approval machine
• Quarterly database partitioning on PostgreSQL transaction logs
• Aggregated ledger views and compound B-tree database indexing
• Real-time dealer push notifications (via WebSockets)
• Service worker-driven offline dashboard sync

07 / Outcome & Result

Stock discrepancies reduced from 24% to under 0.8%. Order processing cycle dropped from 4 days to under 30 minutes. Sales velocity increased by 18% through the streamlined dealer portal.

08 / Future Improvements

• Implement Redis caching layer for quick access to aggregated financial reports
• Add machine-learning predictive algorithms to automatically forecast dealer stock replenishment cycles

09 / Technology Pipeline Stack

10 / Project Integrations & Repos