Kereta Api Indonesia (KAI) — AI-generated image by Google Gemini.

A business intelligence case study that designs an OLTP-to-OLAP data warehouse and analyzes railway performance using revenue and operational KPIs.

Railway transportation operators must balance revenue growth with operational efficiency and service reliability. This project simulates a real-world railway analytics scenario to help management evaluate financial performance, maintenance effectiveness, capacity utilization, and punctuality.

The management of a railway operator faces several challenges:

• Fluctuating ticket revenue across routes and time periods
• Long repair times that disrupt operations
• Inefficient seat capacity utilization
• Service delays affecting customer satisfaction

The following KPIs are used in this project:

1. Total Ticket Revenue
   Measures overall income generated from ticket sales.

2. Mean Time to Repair (MTTR)
   Evaluates maintenance efficiency by measuring average repair duration.

3. Passenger Load Factor
   Indicates how effectively seat capacity is utilized.

4. On-Time Performance (OTP)
   Measures the percentage of train trips arriving on schedule.

The system follows a traditional BI architecture:

flowchart LR
    subgraph OLTP["OLTP Layer"]
        A[Operational Database]
    end

    subgraph ETL["ETL Layer"]
        B[Extract]
        C[Transform]
        D[Load]
        B --> C --> D
    end

    subgraph OLAP["OLAP Layer"]
        E["Data Warehouse
(Facts & Dimensions)"]
    end

    subgraph BI["BI Layer"]
        F[Dashboards]
        G[Reports]
    end

    A --> B
    D --> E
    E --> F
    E --> G

The data warehouse uses a star schema optimized for analytical queries. For more detail how data warehouse design, you can open design directory.

1. Fact Tables

• FACT_TICKET_SALES (transactional)
• FACT_MAINTENANCE (accumulating snapshot)
• FACT_PASSENGER_LOAD (periodic snapshot)
• FACT_OPERATIONAL_PERFORMANCE (accumulating snapshot)

2. Dimension Tables

• DIM_DATE
• DIM_TRAIN
• DIM_ROUTE
• DIM_STATION
• DIM_COMPONENT
• DIM_PASSENGER
• DIM_TECHNICIAN
• DIM_PAYMENT_METHOD
• DIM_MAINTENANCE_TYPE

ETL can be succeed with help tool by Pentaho. Analytical dashboards are created using Power BI to visualize KPI trends, comparisons, and performance indicators.

This project is a conceptual and educational case study. Data used in the warehouse may include synthetic or dummy data to demonstrate BI workflows.