Pharmaceutical investment projects are subject to strict regulatory requirements. EU-GMP Annex 15 mandates a risk-based approach to qualification and validation. ICH Q9(R1) defines the principles of Quality Risk Management and requires systematic, documented risk assessments throughout the product lifecycle. ICH Q10 integrates risk management into the Pharmaceutical Quality System. For project managers, the question is: How do we implement these requirements efficiently – without getting lost in formalism?
This presentation provides the methodological toolkit for effective risk management in engineering and qualification projects. We start with a clear overview of the regulatory landscape: What do Annex 15, ICH Q9(R1) and ICH Q10 actually require? What level of documentation is appropriate?
A key element is the clear distinction between project risks and quality risks. Project risks concern cost, schedule and scope. Quality risks affect patient safety, product quality and data integrity. This distinction determines which methods and what depth of analysis are appropriate.
In the methodology section, we focus on two proven tools: FMEA and Risk Ranking & Filtering. FMEA receives particular attention as the most widely used tool in pharmaceutical projects. We demonstrate the step-by-step approach: scope definition, team composition, systematic identification of failure modes, assessment using objectified scales, action planning and re-evaluation. Risk Ranking & Filtering complements FMEA where rapid prioritisation is required.
The integration of FMEA into the project cycle determines its value. We show the interplay with User Requirement Specifications and qualification activities – ensuring that FMEA actually influences design decisions and test strategies.
A current focus is on AI-powered tools. These can support risk identification and enable data-driven prioritisation. It is essential to understand the regulatory boundaries: When do Annex 11 or the new Annex 22 apply? The presentation shows where these boundaries lie and how companies can use AI pragmatically – without unnecessary validation effort.
Typical pitfalls come from practice: subjective assessments, missing data and FMEAs that end up in the drawer. We present concrete countermeasures and best practices.
Participants will receive directly applicable methods, a clear regulatory framework and a realistic view of the benefits and limitations of AI in risk management.