Reading
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.
Section 01
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.
Section 02
Section 03
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.
Section 04
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.
Section 05
The project uses a complete data science toolchain:
This combination of tools covers the complete workflow from data cleaning, exploratory analysis to model training and result presentation.
Section 06
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.
Section 07
The project created a series of business-oriented new features to enhance the predictive power of the data:
These features transform raw data into predictive variables with more business meaning.
Section 08
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.
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