This open access book provides a cutting-edge framework for leveraging data-driven predictions to solve complex operational problems in platform-based supply chains. It moves beyond traditional models by integrating advanced machine learning with optimization techniques, enabling managers to make smarter, more adaptive decisions in dynamic digital environments.<BR>The approach bridges the gap between ...Full description
This open access book provides a cutting-edge framework for leveraging data-driven predictions to solve complex operational problems in platform-based supply chains. It moves beyond traditional models by integrating advanced machine learning with optimization techniques, enabling managers to make smarter, more adaptive decisions in dynamic digital environments.<BR>The approach bridges the gap between predictive analytics and operational decision-making, introducing a structured “predict-then-optimize” methodology tailored for platform ecosystems. This dual focus allows for more robust and realistic solutions than purely deterministic or intuition-based approaches.<BR>Key features and benefits include:<BR>A unified framework that integrates prediction and optimization models for end-to-end supply chain decision-making;<BR>Real-world case studies and examples that illustrate the application of the methodology in platform contexts;<BR>Practical guidance on implementing predictive and optimization techniques using modern computational tools.