Description
Open source statistical toolkits have progressed tremendously over the last decade. In this book Nicolas Vandeput demonstrates that these toolkits are more than enough to address real-world forecasting challenges as found in supply chains. Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting contends that a true scientific method that includes experimentation, observation and constant questioning must be applied to supply chain as well. The first part of the book is focused on statistical “traditional” models and the second on machine learning. The various chapters are focused either on forecast models or on new concepts (overfit, underfit, kpi, outliers). The book is full of python examples to show the reader how to apply these models him/herself. This is a book for practitioners focusing on data science and machine learning and demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. Through its hands-on approach, it is accessible to a large audience of supply chain practitioners. Nicolas is a Supply Chain Data Scientist specialized in Demand Forecasting & Inventory Optimization. He always enjoys discussing new quantitative models and how to apply them to business reality. Passionate about education, Nicolas is both an avid learner and enjoys teaching at universities including the University of Brussels; he teaches forecast and inventory optimization to master students since 2014. He founded SupChains in 2016 and co-founded SKU Science-a smart online platform for supply chain management-in 2018. I Statistical Forecast Moving Average Forecast Error Exponential Smoothing Underfitting Double Exponential Smoothing Model Optimization Double Smoothing with Damped Trend Overfitting Triple Exponential Smoothing Outliers Triple Additive Exponential smoothing II Machine Learning Machine Learning Tree Parameter Optimization Forest Feature Importance Extremely Randomized Trees Feature Optimization Adaptive Boosting Exogenous Information & Leading Indicators Extreme Gradient Boosting Categories Clustering Glossary
