Case Study: PT. Pelayaran Bahtera Adhiguna (PLN Group) - PMS & IoT Implementation
How Hifshan Riesvicky's Planned Maintenance System and IoT solutions transformed fleet operations for 11 MV vessels at PT. Pelayaran Bahtera Adhiguna, a PLN Group company.
Case Study: PT. Pelayaran Bahtera Adhiguna (PLN Group)
Transforming Fleet Operations with Integrated PMS & IoT Solutions
Executive Summary
| Detail | Information |
|---|---|
| Client | PT. Pelayaran Bahtera Adhiguna (PLN Group) |
| Fleet Size | 11 Mother Vessels (MV) |
| Solution Deployed | Planned Maintenance System (PMS) + IoT Integration |
| Status | ✅ Fully Operational |
| Primary Goal | Digital Transformation & Operational Efficiency |
About the Client
PT. Pelayaran Bahtera Adhiguna is a crucial part of the PLN Group ecosystem, Indonesia's state-owned power utility. They manage a significant fleet that ensures the continuous supply and logistics required for national power generation.
Client Profile
- Industry: Maritime Transportation & Logistics (SOE)
- Fleet: 11 Mother Vessels (MV) providing nationwide maritime logistics.
- Crew Capacity: 100+ professional seafarers.
The Challenge: Navigating Complexity
Operating a fleet of 11 Mother Vessels involves high stakes. PT. Pelayaran Bahtera Adhiguna faced fragmented data and reactive processes:
- Manual Maintenance: Reliability depended on manual schedules and disparate records.
- Blind Spots: No real-time visibility into engine performance or fuel consumption.
- Compliance Fatigue: Manual ISM Code and SOLAS tracking was prone to human error.
- Operational Costs: High costs due to unplanned downtime and inefficient spare parts inventory.
The Solution: Integrated PMS & IoT Platform
We implemented a dual-layered technical solution that bridges onboard operations with shore-side management.
1. Planned Maintenance System (PMS)
The core of the digital transformation, providing a single source of truth for maintenance.
- Automated Scheduling: Running hours-based triggers for maintenance.
- Inventory Control: Real-time spare parts tracking with auto-alerts.
- Quality Control: Digital job cards with photo evidence requirements.
2. IoT Integration & Smart Monitoring
We installed industrial-grade sensors across all 11 vessels to capture high-frequency data from critical systems.
Sensors Deployed:
- Main Engines: RPM, Temperature, Vibration.
- Fuel Systems: Real-time consumption flow meters.
- Environment: Cargo hold and engine room ambient conditions.
- Navigation: GPS and AIS integration for optimized route tracking.
Technical Implementation: Deployment at Sea
The installation was carried out while the vessels were operational or during scheduled port stays to minimize disruption.
Implementation Roadmap
- Month 1: Needs assessment and inventory digitization.
- Months 2-3: Physical IoT installation and PMS server deployment on 11 vessels.
- Month 4: Intensive crew training and mobile app onboarding.
- Months 5-6: AI model calibration for predictive maintenance alerts.
Results & Quantifiable Impact
The impact was immediate, showing a clear shift from reactive to proactive management.
Operational KPIs
| Metric | Improvement |
|---|---|
| Unplanned Downtime | 📉 Reduced by 30% |
| Maintenance Costs | 📉 Reduced by 25% |
| Audit Preparation Time | ⚡ 90% Faster |
| Fuel Efficiency | 📈 15% Improvement |
| Failure Prediction Accuracy | 🎯 60% Success Rate |
Technology Stack
The Software Layer
- Architecture: Distributed Cloud-based SaaS with Offline-Sync.
- Mobile: Native iOS & Android apps for seamless crew interaction.
- AI Engine: Proprietary predictive maintenance algorithms.
The Hardware Layer
- Edge Devices: Ruggedized data collectors for maritime environments.
- Connectivity: Hybrid 4G/LTE and Satellite (LEO) for 100% uptime.
- Sensors: High-precision vibration, temperature, and flow sensors.
Client Testimonial
"The integrated PMS and IoT solution from Hifshan Riesvicky has transformed how we manage our fleet of 11 vessels. Real-time monitoring and predictive maintenance have significantly reduced our operational costs while improving safety and compliance. The system's ease of use and comprehensive features make it an invaluable tool for our operations."
— Management Team
PT. Pelayaran Bahtera Adhiguna (PLN Group)
Built for the Future
This implementation is not just about today's efficiency; it's about building a data foundation for the next decade of maritime operations.
Future Roadmap
- Advanced AI Models: Deep learning for even more accurate component lifespan prediction.
- Autonomous Reporting: Automated submission of regulatory compliance data.
- Digital Twins: Creating virtual replicas of each MV for stress testing in differing sea conditions.
About the Provider
Hifshan Riesvicky is a maritime software engineer & solutions architect with 10+ years of experience. He specializes in bridging the gap between traditional maritime operations and modern digital technologies.
- Specialties: PMS/CMMS, IoT Integration, Shipyard Management, AI Operations.
- Portfolio Highlights: ShipyardPro, PLN Group Fleet, 11+ Professional Calculators.
Interested in digitizing your fleet? Contact us for a consultation or explore our Maritime Services.
Keywords: PMS case study, maritime IoT, PLN Group maritime, ship management software, predictive maintenance ships, Indonesian maritime digital transformation.
Last Updated: January 2026
Client: PT. Pelayaran Bahtera Adhiguna (PLN Group)
Solution Provider: Hifshan Riesvicky