# Supply Chain Logistics Analysis: Predicting Delivery Delay Risks with Machine Learning

> An end-to-end supply chain analysis project using Python, machine learning, and Power BI to analyze logistics performance, identify delivery delays, evaluate profitability, and build predictive models to anticipate delay risks.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-15T21:15:54.000Z
- 最近活动: 2026-06-15T21:19:15.096Z
- 热度: 163.9
- 关键词: 供应链分析, 物流优化, 机器学习, 配送延迟预测, 随机森林, Python, Pandas, Power BI, 数据驱动决策, 运营风险管理
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-theanujsaini-supply-chain-logistics-analytics
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-theanujsaini-supply-chain-logistics-analytics
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Supply Chain Logistics Analysis: Predicting Delivery Delay Risks with Machine Learning

An end-to-end supply chain analysis project using Python, machine learning, and Power BI to analyze logistics performance, identify delivery delays, evaluate profitability, and build predictive models to anticipate delay risks.

## Original Author and Source

- **Original Author**: TheAnujSaini
- **Source Platform**: GitHub
- **Original Project Title**: Supply-Chain-Logistics-Analytics
- **Original Link**: https://github.com/TheAnujSaini/Supply-Chain-Logistics-Analytics
- **Publication Time**: June 2026

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## Project Background and Business Challenges

Supply chain management is a core component of modern business operations, and the punctuality of logistics delivery directly impacts customer satisfaction and enterprise profitability. Supply chain organizations generally face multiple challenges such as delivery delays, rising transportation costs, declining customer satisfaction, low operational efficiency, and profit losses.

This project, developed by TheAnujSaini, aims to transform raw logistics data into actionable business insights through data analysis and machine learning technologies, supporting operational decisions and improving supply chain performance. The core goal of the project is to identify key factors affecting delivery performance and develop a predictive solution for delivery risk management.

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## Analysis Objectives and Scope

The project sets clear analysis objectives: analyze delivery performance across regions and transportation modes; identify key drivers of delayed deliveries; evaluate profit trends; study time patterns of delivery delays; build machine learning models to predict delay risks; generate business recommendations based on analysis findings.

The analysis covers multiple dimensions including orders, products, customers, transportation operations, regional logistics performance, and profitability indicators. Key attributes include order region, transportation mode, customer segment, department name, category name, profit per order, planned delivery days, and delay risk indicators, etc.

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## Technology Stack and Toolchain

The project uses a complete data science toolchain:

- **Programming Language**: Python
- **Data Processing**: Pandas, NumPy
- **Visualization**: Matplotlib, Seaborn
- **Machine Learning**: Scikit-Learn, Imbalanced-Learn (SMOTE)
- **Development Environment**: Jupyter Notebook, Google Colab
- **Business Intelligence**: Power BI (for reports and visualization)

This combination of tools covers the complete workflow from data cleaning, exploratory analysis to model training and result presentation.

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## Data Cleaning

The first step of the analysis is data quality assurance, including missing value analysis, duplicate record handling, feature selection, and data validation. These steps ensure the reliability of subsequent analysis.

## Feature Engineering

The project created a series of business-oriented new features to enhance the predictive power of the data:

- **Order Processing Time**: Time interval from order placement to shipment
- **Delivery Delay**: Difference between actual delivery days and planned days
- **Delay Indicator**: Binary label indicating whether a delay occurred
- **Profitability Flag**: Identifies whether an order is profitable
- **Time Features**: Time dimension information such as order month, date, hour, etc.

These features transform raw data into predictive variables with more business meaning.

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## Profitability Analysis

The analysis found that most orders generate positive profits, but a few orders result in losses. Delivery performance directly affects profitability, and delayed orders are often accompanied by profit losses.