Supply Chain Delivery Performance Analysis: Optimizing Logistics Decisions with Python and Machine Learning

This project is a Python-based application of data science and machine learning, aiming to analyze supply chain delivery delay issues, identify operational bottlenecks, assess profit risks, and predict late orders through a random forest classifier. It provides enterprises with actionable logistics optimization suggestions to improve operational efficiency and customer satisfaction.

In the globalized business environment, supply chain efficiency directly affects an enterprise's profitability and customer satisfaction. Statistics show that over half of orders experience delivery delays, leading to customer churn, additional costs, and brand damage. This project, built by Aprajita1729, uses Python ecosystem tools (Pandas, NumPy, Matplotlib, etc.) combined with machine learning to deeply mine supply chain data, identify bottlenecks, and warn of risks, providing data support for decision-making.

Data Processing Layer: Pandas handles data cleaning, transformation, and feature engineering; NumPy provides numerical computing support. Visualization Layer: Matplotlib generates statistical charts to help understand data distribution and anomalies. Machine Learning Layer: Uses a random forest classifier (high robustness) combined with SMOTE to handle class imbalance issues, improving the ability to identify late orders.

1. Delivery delay status: 54.71% of orders are delayed, far exceeding the industry threshold of 5-10%, and highly correlated with product category, delivery region, and supplier. 2. Profit risk: Approximately $2.1 million in profits are at risk (due to customer claims, expedited costs, etc.). 3. Model performance: The random forest classifier achieves an accuracy rate of 74%, which has practical application value.

Identify bottlenecks through multi-dimensional analysis: 1. Data cleaning and preprocessing (handling missing values, standardization, deduplication). 2. Exploratory data analysis (statistical summaries, visualizing correlations between variables and delays). 3. KPI dashboard (monitoring on-time delivery rate, average delay days, etc.). 4. Delay pattern analysis (time series to identify seasonal and periodic patterns).

Model construction process: 1. Feature engineering (extracting predictors such as order, logistics, time, and historical attributes). 2. Class imbalance handling (SMOTE to synthesize minority class samples). 3. Model training and validation (cross-validation to avoid overfitting, grid search to optimize hyperparameters). 4. Feature importance analysis (identifying key factors affecting delays).

Improvement suggestions based on analysis: 1. Supplier management optimization (strict evaluation of high-risk suppliers, alternative solutions). 2. Inventory strategy adjustment (increase safety stock for products with high delay rates, optimize distribution). 3. Predictive intervention (integrate the model into the order system to alert high-risk orders). 4. Continuous monitoring and iteration (regularly update the model, use A/B testing to verify effects).

The project demonstrates the value of data science in the digital transformation of traditional industries and proves that open-source tools can build practical analytical capabilities. It provides a complete practical case for practitioners and a data-driven optimization framework for enterprises. As supply chain complexity increases, predictive analysis will become the core of enterprise competitiveness, reshaping operational paradigms.