Recent case studies suggest that process understanding and optimization offer significant cost-saving potential. However, understanding complex processes requires deep knowledge of both the machinery and the overall system, which cannot be found in user manuals. Skilled and experienced personnel are often required for each process decision. Traditional process optimization methods, such as Lean Six Sigma, rely on continuous improvement strategies supported by statistical analysis of process data. While effective, these methods struggle with the complexity of large datasets, as modern processes are influenced by hundreds of variables. To address these challenges, digital twins offer a transformative solution. Digital twins provide a comprehensive, real-time view of processes, enabling better understanding and optimization of production systems. This chapter explores how digital twins are used to tackle the limitations of traditional methods, offering a deeper understanding of real-life manufacturing processes, and facilitating real-time optimization.