Supply Chain Analytics
Supply Chain Analytics
This innovative new core textbook, written by an experienced professor and practitioner in supply chain management, offers a business-focused overview of the applications of data analytics and machine learning to supply chain management. Accessible yet rigorous, this text introduces students to the relevant concepts and techniques needed for data analysis and decision making in modern supply chains and enables them to develop proficiency in a popular and powerful programming software. Suitable for use on upper-level undergraduate, postgraduate and MBA courses in supply chain management, it covers all of the major supply chain processes, including managing supply and demand, warehousing and inventory control, transportation and route optimization. Each chapter comes with practical real-world examples drawn from a range of business contexts, including Amazon and Starbucks, case study discussion questions, computer-assisted exercises and programming projects.
Chapter 1: Introduction.- Chapter 2: Data-drive Supply Chains & Intro to Python.- Chapter 3: Data Manipulation.- Chapter 4: Data Visualization.- Chapter 5: Customer Management.- Chapter 6: Supply Management.- Chapter 7: Warehouse & Inventory Management.- Chapter 8: Demand Management.- Chapter 9: Logistics Management.
Liu, Kurt Y.
| ISBN | 978-3-030-92223-8 |
|---|---|
| Article number | 9783030922238 |
| Media type | Book |
| Edition number | 1st ed. 2022 |
| Copyright year | 2022 |
| Publisher | Springer, Berlin |
| Length | XIX, 377 pages |
| Illustrations | XIX, 377 p. 165 illus., 158 illus. in color. |
| Language | English |
