| Description |
1 online resource |
| Series |
Springer series in supply chain management, 2365-6409 ; volume 14 |
|
Springer series in supply chain management ; v. 14. 2365-6409
|
| Contents |
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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed January 10, 2022) |
| Subject |
Business logistics -- Statistical methods
|
|
Business logistics -- Management
|
|
Demand (Economic theory)
|
|
Business logistics -- Management
|
|
Demand (Economic theory)
|
| Form |
Electronic book
|
| Author |
Gras, Paul-Emile, author
|
|
Pentecoste, Arthur, author
|
|
Zhang, Renyu, author
|
| ISBN |
9783030858551 |
|
3030858553 |
|
