How Data Science Transforms Intelligent Supply Chains: Theory and Real‑World Cases

01 Book Introduction

In today’s data‑driven business environment, data science is the core engine for intelligent supply‑chain transformation. It extracts value from massive data, enables precise decisions, builds smart models, and optimizes end‑to‑end operations, enhancing digital collaboration across the supply chain.

02 Origin of the Book

The book, the third in the “Intelligent Supply Chain” series, focuses on practical data‑science applications in supply‑chain scenarios, presenting data science as a “microscope” that turns vague business pain points into clear mathematical language and data‑driven wisdom.

03 Content Features

It offers hands‑on guidance: rapid Python data cleaning, A/B testing to prove algorithm value, and Pareto‑front optimization for multi‑objective trade‑offs, providing essential skills for professional growth.

04 Typical Cases

Chapter 11 addresses “last‑mile” delivery efficiency, using synthetic control or propensity‑score matching to evaluate incentive mechanisms under small‑sample or heterogeneous conditions, achieving a 1% improvement in delivery speed without harming satisfaction.

05 Information Flow – Customer Service Call Volume Analysis

Chapter 16 builds a data‑driven forecasting framework that integrates multi‑source data, applies causal discovery (FGES) and lag analysis (PCMCI), and combines deep learning (TFT) with XGBoost, raising prediction accuracy to 91.76% and balancing cost and experience.

06 Target Audience and Benefits

Designed for supply‑chain data analysts, algorithm engineers, data scientists, operations managers, and logistics educators, the book teaches data‑preprocessing, feature engineering, model building, and validation through 12 real cases, each with reusable code, helping readers master intelligent pricing, demand forecasting, inventory optimization, and more.