Predicting car shipment delays and estimating financial impact using machine learning and supply chain analytics.
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Live App: https://car-shipment-delay-prediction-8gxekgz52yqxspeqtu8svv.streamlit.app/
Automotive manufacturing depends heavily on timely delivery of critical components such as engines, transmissions, brakes, suspension systems, and infotainment modules.
Delays in any of these components can halt assembly lines and create significant financial losses.
This project analyzes supply chain shipment data to:
• Predict whether a shipment will be delayed
• Estimate how many days the delay will last
• Forecast the financial cost of the delay
These insights can help supply chain managers take proactive decisions and reduce operational risk.
The dataset contains 1010 car shipments with 65+ operational features, including:
Supplier Information
- Supplier region
- Supplier reliability score
- Supplier performance score
Logistics Data
- Transport mode
- Route type
- Distance
- Traffic severity
Operational Factors
- Production shift
- Order urgency
- Inventory buffer usage
- Strike notices
- Weather conditions
Part-Level Information
- Engine
- Transmission
- Brake System
- Suspension
- Infotainment
Target Variables
Car_Delayed(classification)Delay_Days(regression)Financial_Impact_USD(regression)
- Parsed date columns
- Handled categorical variables
- Feature type conversion
- Delay distribution analysis
- Financial impact distribution
- Supplier reliability analysis
- Seasonal trends
- Supplier reliability scores
- Delay flags per component
- Traffic severity index
- Supplier groups
- Reliability aggregations
Classification Model Predict if shipment will be delayed
Algorithms tested:
- Random Forest
- XGBoost
- CatBoost
Regression Model 1 Predict delay duration (days)
Regression Model 2 Predict financial loss due to delay
Evaluation Metrics
- Accuracy
- MAE
- RMSE
- R² Score
The project includes an interactive Power BI dashboard used to explore delay patterns and operational insights.
Dashboard Highlights
• Shipment delay rate
• Delay distribution by car component
• Financial impact of delays
• Seasonal shipment patterns
• Supplier reliability analysis
Major findings from the analysis:
• 62% of shipments experienced delays, indicating major supply chain inefficiencies.
• Engine reliability is the strongest driver of financial loss. Lower reliability scores lead to higher delays and costs.
• Q1 (Jan–Mar) shows the highest delay frequency, suggesting seasonal operational challenges.
• Urgent orders are delayed more often, indicating planning inefficiencies.
• Inventory buffers are rarely used and do not significantly reduce delays.
• Evening production shifts show higher delay frequency and cost spikes.
• Transmission and suspension components show lower reliability scores, suggesting supplier performance issues.
• Financial impact increases sharply with delay days, showing a near linear relationship.
Python
Pandas
Scikit-Learn
CatBoost
XGBoost
Matplotlib
Seaborn
Power BI
Jupyter Notebook
supply-chain-delay-cost-impact-analysis
│
├── app
│ ├── Delay_Days_Features_list.pkl
│ ├── app_final1.py
│ ├── best_model_rf.pkl
│ ├── financial_scaler.pkl
│ ├── random_forest_model_classification.pkl
│ ├── random_forest_top5_cost_features.pkl
│ ├── random_forest_top5_cost_model.pkl
│ └── top_features_classification.pkl
│
├── data
│ ├── Cleaned_car_automated_dataset.csv
│ ├── Financial_Impact_Regression_Final_Dataset (1).csv
│ ├── delay_days_regression_final.csv
│ ├── df_cleaned_classification_final.csv
│ └── raw_shipment_data.csv.csv
│
├── notebooks
│ ├── 01_data_cleaning.ipynb.ipynb
│ ├── 02_eda.ipynb.ipynb
│ ├── 03_feature_eng_classification.ipynb.ipynb
│ ├── 04_feature_eng_regression_days.ipynb.ipynb
│ ├── 05_feature_eng_regression_cost.ipynb.ipynb
│ ├── 06_model_classification.ipynb.ipynb
│ ├── 07_Delay_Days_Regression_Model.ipynb
│ └── 08_model_regression_cost.ipynb
│
├── Car Shipment Delay Prediction_ppt.pdf
├── Car_Shipment_Delay_Report.docx
├── Car_Shipment_Power_Bi_Dashboard.pbix
├── Insights.md
├── README.md
└── requirements.txt
Amneet Kaur
Data Analyst | Supply Chain Analytics | Machine Learning
LinkedIn
https://linkedin.com/in/amneetkaur24