In pharmaceutical manufacturing, quality control, and quality assurance, critical GMP decisions are often made with incomplete data, variable processes, and uncertain measurements. Batch release, process validation, continued process verification, analytical data interpretation, sampling decisions, and stability assessment all require more than a deterministic "pass/fail" view. This book introduces Monte Carlo simulation as a practical framework for converting variability, uncertainty, and risk into quantitative, decision-relevant information.

Unlike general texts on simulation or pharmaceutical quality systems, this book focuses specifically on GMP manufacturing and control. It connects statistical modeling with real pharmaceutical problems, showing how stochastic simulation, bootstrap methods, uncertainty propagation, and predictive risk analysis can complement traditional QA/QC tools and support transparent, scientifically defensible decisions.

Key features include:

• Practical Monte Carlo case studies including assay, dissolution, process validation, CPV, stability, sampling plans, microbiological counts, capability, and measurement uncertainty
• Reproducible examples in R, requiring no proprietary statistical software
• Clear distinction between variability and uncertainty, with direct GMP interpretations
• Coverage of OOS probability, confidence intervals, non-normal data, overdispersion, bootstrap capability, empirical OC curves, expected loss, and predictive control
• Quantitative support for comparability assessments, manufacturing changes, and post-approval lifecycle decisions
• Alignment with modern risk-based thinking, including ICH Q9/Q10/Q11/Q14, FDA process validation, and USP <1210>/<1220>

Written for QA/QC professionals, manufacturing and process engineers, regulators, and statisticians in pharmaceutical and chemical-pharmaceutical environments, the book is a practical companion for moving from qualitative risk descriptions to quantitative, reproducible decision support. Its central message is simple: better GMP decisions can be made when uncertainty is explicitly modeled rather than ignored.

Riccardo Bonfichi is an independent consultant specializing in the application of statistical methods to pharmaceutical manufacturing and control, with over 35 years of experience in GMP-regulated environments across Italy and abroad, including roles at Sandoz Pharma AG in Basel. Over his career he progressed from analytical scientist to senior quality roles - Head of Quality Control, Head of Quality Assurance, and Qualified Person - responsible for the batch release of sterile active ingredients and medicinal products. He now supports pharmaceutical companies in Italy and internationally as a freelance consultant and trainer, focusing on Monte Carlo simulation, resampling methods, and statistical decision-making in QA/QC, and he regularly delivers webinars and courses on these topics for industry associations and regulatory bodies.