PT Kereta Api Indonesiaās AIāPowered Predictive Maintenance Transformation
Organization: PT Kereta Api Indonesia (Persero)
Project Name: Smart Predictive Maintenance and Digital Transformation of Indonesiaās Rail Network
Location: Indonesia (7,000 kilometers of national rail network)
Project Phase: Completed (digital transformation in operational use)
Estimated Project Cost: Not specified (achieved up to 40% efficiency improvement and reduced maintenance time by up to 4 hours per segment)
Bentley Software: Bentley AssetWise Linear Analytics, iTwin Experience, OpenRail Designer, and ProjectWise
Additional Context: PT Kereta Api Indonesia (KAI) modernized its 7,000ākilometer national railway network using Bentleyās AssetWise Linear Analytics to power AIādriven predictive maintenance. The solution centralized data across IoT sensors, inspections, and maintenance systems, reducing track maintenance time by 2-4 hours per segment and improving efficiency by up to 40%. By integrating with SAP and GIS, KAI eliminated data silos, reduced emissions, and built a sustainable, scalable digital foundation for Indonesiaās railway future.
Project
PT Kereta Api Indonesia (KAI) operates and maintains a vast, 7,000-kilometer rail network that is the backbone of Indonesiaās transportation system. The organization faced significant challenges with its conventional asset management, which relied on inefficient manual processes and third-party solutions that created data silos. To modernize its operations, PT KAI launched a digital transformation project to implement a smart, data-driven, and predictive maintenance strategy. The goal was to centralize asset data, enable predictive maintenance with AI, and integrate seamlessly with existing enterprise systems to improve reliability, safety, and cost efficiency across its entire network.
Facts
- PT KAI used Bentleyās AssetWise Linear Analytics to digitize and monitor its entire rail infrastructure, including 7,000 kilometers of track, enabling real-time condition tracking, infrastructure modeling, and AI-powered predictive maintenance.
- They achieved up to 40% efficiency gains with maintenance time reduced by 2 to 4 hours per track segment, delivering 25 to 40% efficiency improvement and minimizing unplanned downtime and costs.
- The solution improved railway safety, reduced emissions, and supports long-term scalabilityāadvancing Indonesiaās vision for sustainable infrastructure and a digitally enabled railway maintenance system.
"By (using) Bentley's AssetWise Linear Analytics with AI, organizations can transform how they manage infrastructure, making it smarter, more efficient, and significantly more sustainable. This powerful synergy enables predictive maintenance, optimized resource use, lower emissions, and data-driven decisions that support long-term environmental and economic goals. As industries face increasing pressure to meet sustainability targets, leveraging these technologies is not just beneficial it's essential."
Solution
PT KAI selected Bentleyās AssetWise Linear Analytics as the core of its new smart infrastructure solution. It operates by integrating real-time and historical data from numerous sources, including IoT sensors, inspections, and maintenance records. Using built-in artificial intelligence, the system analyzes this data to detect anomalies, predict failures, and calculate asset risk levels. These insights are presented in interactive dashboards, enabling PT KAI to shift from a reactive to a predictive maintenance model. The solution’s ability to create a centralized connected data environment (CDE) and integrate with PT KAI’s existing SAP and GIS systems was crucial for breaking down data silos. Furthermore, iTwin Experience was used to create 3D models of railway assets, providing powerful visualization capabilities.
Outcome
The implementation of AssetWise Linear Analytics has significantly enhanced PT KAIās railway operations. Real-time monitoring and AI-powered failure prediction have reduced track maintenance time by 2-4 hours per segment, resulting in an efficiency improvement of up to 40%. These insights have helped reduce unplanned downtime, lower maintenance costs, and improve passenger safety. Designed to scale across PT KAIās 7,000-kilometer rail network, the solution supports long-term digital transformation and sustainability goals, including reduced emissions and optimized resource use.
Software
- AssetWise Linear Analytics: The core of the project, providing AI-driven predictive maintenance and linear analytics.
- iTwin Experience: Utilized for rail network data management and modeling.
- OpenRail Designer: Used to create and visualize 3D models of railway assets.
- ProjectWise: Used for its connected data environment capabilities.
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