Description
This job focuses on identifying and quantifying critical spare parts for Battery Energy Storage Systems (BESS) projects. The goal is to develop and validate a process for spare part planning and inventory strategy using conventional Reliability-Centered Maintenance (RCM) methods and AI-based approaches.
Key Tasks:
- Investigate current RCM processes in the industry and evaluate existing scientific literature and best practices.
- Assess and improve existing RCM processes for BESS at Rolls-Royce, identifying potential areas for enhancement.
- Analyze interactions between sales, service, procurement, and quality in the context of RCM.
- Develop a method to scientifically identify and plan critical spare parts and required quantities over the system lifespan, assuming constant failure rates.
- Determine the optimal inventory strategy for spare parts, considering local versus global storage.
- Generate and validate results for spare part and inventory strategies using conventional and AI-based approaches.
- Optional: Methodologically describe and analyze failure chain analysis and error code mapping.
- Optional: Compare traditional and AI-based methods for pattern recognition and risk assessment.
Requirements:
- Master's student in Engineering, Data Science, or a related field.
- Knowledge of reliability engineering, maintenance strategies, and risk modeling.
- Experience with data analysis and machine learning/KI methods.
- Strong analytical and conceptual skills.
- Independent and structured work style.
- Excellent English language skills.
- Interest in digitalization and process optimization in the energy sector.
What We Offer:
- Healthy and fair working conditions.
- Attractive compensation.
- Flexible working hours and overtime compensation.
- On-site restaurant.
- Occupational medical care provided by our in-house company doctor.