GRP (Gunung Raja Paksi) — Manufacturing Modernization & Industry 4.0

Key Contributions

• Cross-functional program leadership: managed engineering, OT, IT and analytics teams to deliver plant modernization roadmaps.
• Process digitization & automation: replaced manual handoffs with automated data capture, sequence control and alarm analytics.
• Real-time operations analytics: designed and rolled out dashboards and alerts for production, quality and maintenance KPIs.
• Data platform & integrations: built ingestion pipelines from PLCs/SCADA to a central lake, enabling near-real-time analytics and reporting.
• Change management & capability building: trained plant teams on new dashboards, SOPs and decision workflows to ensure adoption.

Context & Challenges

GRP operated multiple brownfield facilities with heterogeneous control systems and fragmented reporting. Key challenges included:

• Legacy SCADA/PLC systems with inconsistent data models across lines.
• Lack of near-real-time visibility into yield losses and scrap drivers.
• Manual quality reconciliation and slow root-cause analysis cycles.
• Pressure to increase throughput while maintaining product quality and safety.

Approach

The program used a phased, risk-managed approach:

• Phase 1 — Rapid assessment and quick wins (data collection, pilot dashboards).
• Phase 2 — Stabilize OT integrations (protocol adapters, unified tags, edge buffering).
• Phase 3 — Central data platform (ingest, storage, near-real-time transforms) and enterprise reporting.
• Phase 4 — Optimization loops: predictive maintenance, scrap reduction models and operator decision support.

Solution & Technology

Delivered an integrated stack combining OT adapters, streaming ingestion, curated serving layer and BI/visualization:

• Edge adapters & buffering (MQTT / Kafka edge bridges) for reliable PLC/SCADA telemetry.
• Streaming and batch pipelines (Kafka + Spark/Flink) to produce operational data streams and aggregates.
• Serving layer (ClickHouse / Postgres) for sub-second KPI queries consumed by dashboards.
• Model operationalization for scrap prediction and yield optimization (Python ML pipelines).
• BI & dashboards (Power BI / QuickSight) with governed semantic models for leadership & operations.

Business Impact

• Reduced scrap rates by identifying process drivers — measurable material cost savings.
• Improved first-time yield and throughput through focused operator interventions and predictive alerts.
• Shortened incident resolution and RCA cycles via consolidated telemetry and historical analysis.
• Created a repeatable blueprint for rolling out Industry 4.0 capabilities across other plants and regions.

Selected Visuals

Click any image to enlarge.

GRP plant overview — site layout and process flow

Process modernization & automation diagrams

Operations analytics dashboard — real-time KPIs