I enjoyed how the simulation reflected real-world project management scenarios. It provided practical experience in analysing KPIs, evaluating financial performance, identifying project risks, and preparing a stakeholder report. The tasks were engaging, well-structured, and helped me better understand the responsibilities of a Commercial Project Manager.
- Job Simulations
- Commercial Project Manager
Introduction from Siemens Mobility
Tasks
Intro & Scenario
Background context and your project team
Your role
- Oversee the commercial aspects of a major light rail project.
- Monitor KPIs and financial performance throughout the project lifecycle.
- Perform effective contract and claim management.
- Collaborate across functions to address risks and opportunities.
- Provide clear updates and recommendations to leadership.
Your goal
- Apply project planning tools (WBS, KPIs) to assess project health.
- Analyze financial and operational data to forecast outcomes.
- Identify risks and propose mitigation strategies.
- Deliver professional reports that inform key decisions.
Intro & Scenario
Background context and your project team
Your role
- Oversee the commercial aspects of a major light rail project.
- Monitor KPIs and financial performance throughout the project lifecycle.
- Perform effective contract and claim management.
- Collaborate across functions to address risks and opportunities.
- Provide clear updates and recommendations to leadership.
Your goal
- Apply project planning tools (WBS, KPIs) to assess project health.
- Analyze financial and operational data to forecast outcomes.
- Identify risks and propose mitigation strategies.
- Deliver professional reports that inform key decisions.
Reviews
The simulation was well structured and realistic. It helped me understand the responsibilities of a Commercial Project Manager, including KPI analysis, EAC forecasting, risk management, and stakeholder communication. I enjoyed working on practical project scenarios that improved my analytical and reporting skills.
Learning data analysis concepts through realistic examples. Testing hypotheses before investing time in real data collection. Exploring “what-if” scenarios and predicting possible outcomes. Practicing statistical techniques and data visualization. Finding patterns that might otherwise be difficult to notice.

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