TY  - BOOK
AU  - Cohen,Maxime C.
AU  - Gras,Paul-Emile
AU  - Pentecoste,Arthur
AU  - Zhang,Renyu
TI  - Demand Prediction in Retail : A Practical Guide to Leverage Data and Predictive Analytics 
T2  - Springer Series in Supply Chain Management,
SN  - 9783030858551
U1  - 658.81 23
PY  - 2022///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Sales management
KW  - Business logistics
KW  - Production management
KW  - Quantitative research
KW  - Retail trade
KW  - Data mining
KW  - Sales and Distribution
KW  - Supply Chain Management
KW  - Operations Management
KW  - Data Analysis and Big Data
KW  - Trade and Retail
KW  - Data Mining and Knowledge Discovery
N1  - 1. Introduction -- 2. Data Pre-Processing and Modeling Factors -- 3. Common Demand Prediction Methods -- 4. Tree-Based Methods -- 5. Clustering Techniques -- 6. Evaluation and Visualization -- 7. More Advanced Methods -- 8. Conclusion and Advanced Topics
N2  - From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy
UR  - https://doi.org/10.1007/978-3-030-85855-1
ER  -