Demand Forecasting Best Practices is a unique step-by-step guide, ideal for demand planners, S&OP; managers, supply chain leaders and data scientists, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment.
"Data Science for Supply Chain Forecasting, Second Edition" emphasizes the importance of a scientific approach in demand forecasting, combining experimentation and observation. This edition includes over 45% new content, covering traditional statistical models, machine learning, and demand forecasting management, with practical Python and Excel implementations. Ideal for supply chain professionals.
In this book, Nicolas Vandeput explores quantitative supply chain optimizations, emphasizing the need for probabilistic simulations to enhance inventory management. It covers classical models, their limitations, and advanced techniques using Python. Inventory managers and planners will find practical examples and programs to improve their strategies.