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Manufacturing ERP Platform — Enterprise Production Management

ClientPT Astra International Tbk
Year2026
Stack9 technologies
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Results & Impact

Key Performance Metrics

10

Time Saved

50,000+

Scale

Business Challenge

The manufacturer operated 3 factories across Java with 2,000+ employees and a complex supply chain involving 300+ raw material suppliers. Each factory ran its own spreadsheet-based production schedule with no real-time visibility into material availability, work-in-progress status, or quality metrics. Procurement was reactive rather than planned, resulting in both stockouts stopping production lines and overstock tying up working capital. Financial consolidation required 5 accountants spending 10 days per month reconciling data from three separate systems.

## Solution Architecture

Solution Architecture

We designed a modular ERP platform with bounded contexts for Production Planning, Inventory Management, Purchasing, Warehouse Management, Quality Control, Finance, and Executive Analytics. Each module operates as an independent service communicating through Apache Kafka for event-driven data synchronization. The system uses CQRS with PostgreSQL as the write store and Redis-powered read projections for real-time dashboards. The architecture enforces eventual consistency across modules while maintaining ACID transactions within each bounded context.

### Key Architecture Decisions

**Event-Driven Integration:** All state changes are published as domain events (ProductionOrderCreated, InventoryReserved, QualityCheckPassed) to Kafka topics. Downstream services consume these events to maintain materialized views. This ensures loose coupling — the Quality Control module can be deployed independently without affecting Production Planning.

**CQRS Pattern:** Write operations go through command handlers that validate business rules against the PostgreSQL write model. Read operations query denormalized Redis projections optimized for specific UI views — the production dashboard reads from a pre-computed projection updated in real-time via Kafka consumers, not from raw transactional tables.

**Multi-Tenancy at Database Level:** Each factory operates within its own PostgreSQL schema, providing data isolation while sharing the same application infrastructure. Cross-factory reporting uses a dedicated analytics database populated through Kafka Connect.

## Key Features

Key Features

Production Planning with finite capacity scheduling considering machine availability, labor shifts, and material constraints. Inventory Management with real-time stock tracking across 8 warehouses using barcode scanning. Purchasing with automated purchase requisition generation based on reorder points and production schedules. Quality Control with inspection workflow, non-conformance tracking, and supplier quality scorecards. Finance with automated journal entries, multi-factory consolidation, and Indonesian tax compliance (e-Faktur integration). BI Dashboard with real-time OEE (Overall Equipment Effectiveness), production variance analysis, and cost-per-unit tracking.

## Technology Stack

Technology Stack

Frontend built with Next.js 15 App Router and React Server Components for island architecture — interactive dashboards render client-side while static reports use server components. Backend API layer built with NestJS following Clean Architecture with use cases, repositories, and domain entities. PostgreSQL 16 for write models with table partitioning for high-volume tables (production transactions, inventory movements). Redis Cluster for caching read projections and session management. Apache Kafka for event streaming between bounded contexts with exactly-once semantics. Docker containers orchestrated with Kubernetes on AWS EKS. Infrastructure as Code with Terraform managing VPC, RDS, ElastiCache, MSK, and EKS.

## Security Architecture

Results

Production planning cycle reduced from 3 days to 4 hours. Inventory accuracy improved from 72% to 99.2%. Financial close time reduced from 10 days to 2 days. Supplier lead time variability reduced by 35% through data-driven purchasing. Overall equipment effectiveness improved from 68% to 84% through real-time monitoring and predictive maintenance triggers. The system now handles 50,000+ daily production transactions across 3 factories.

## Lessons Learned

Lessons Learned

Event-driven architectures introduce eventual consistency that requires careful UI design — we implemented optimistic UI updates with WebSocket reconciliation for real-time dashboards. CQRS adds complexity in command-validation-read flows but pays off in query performance at scale. Multi-factory deployment requires rigorous tenant isolation testing — a schema migration that succeeds in Factory A may fail in Factory B due to data differences.

Technology Stack

Technologies Used

Next.jsNestJSPostgreSQLRedisApache KafkaDockerKubernetesAWSTerraform
More Work

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An AI-powered customer service platform built for a telecommunications company handling 500,000+ monthly customer interactions across WhatsApp, email, live chat, and phone. The legacy system relied on 200+ human agents using scripted responses with an average first-response time of 45 minutes and a resolution rate of only 62% on first contact. ## Business Challenge The telecom provider faced escalating support costs as their subscriber base grew to 10 million. Customer satisfaction scores were declining due to long wait times and inconsistent answers. Agents spent 60% of their time answering repetitive questions — billing inquiries, plan changes, network coverage checks — rather than solving complex problems. The knowledge base existed as 50+ PDF documents that agents searched manually. Multichannel support (WhatsApp, email, chat, phone) operated in silos with no unified customer context. ## Solution Architecture We built an AI Customer Service Platform using a Retrieval-Augmented Generation architecture. 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