Late deliveries represent one of the biggest operational challenges in supply chain management. Delays impact customer satisfaction, increase logistics costs, reduce profitability, and disrupt planning across the entire value chain.
This project develops an end-to-end Machine Learning solution capable of predicting delivery delays before they occur while identifying the operational factors that contribute most to supply chain inefficiencies.
Beyond prediction, the project aims to translate data into actionable business recommendations that support better operational decision-making.
Supply chain managers often react to delivery delays after they have already affected customers.
The goal of this project is to proactively identify orders at high risk of delay so that operations teams can take preventive actions before shipment.
- Can we predict delivery delays before they happen?
- Which operational factors contribute the most to late deliveries?
- Which products, shipping methods, regions and markets present the highest operational risk?
- What actions should the business prioritize to improve delivery performance?
- Build an end-to-end Machine Learning pipeline.
- Predict delivery delays using historical operational data.
- Identify the key drivers behind late deliveries.
- Compare multiple machine learning models.
- Explain model predictions using feature importance techniques.
- Build an interactive Tableau dashboard for operational decision-making.
- Deliver actionable business recommendations based on data.
Business Understanding
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Data Collection
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Data Cleaning
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Exploratory Data Analysis
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Feature Engineering
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Preprocessing
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Machine Learning
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Model Evaluation
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Business Insights
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Interactive Dashboard
- Python
- Pandas
- NumPy
- Scikit-Learn
- SQL
- Tableau
- Git & GitHub
data/
notebooks/
src/
models/
sql/
dashboard/
reports/
presentation/
This project uses the DataCo Smart Supply Chain for Big Data Analysis dataset.
Source:
DataCo Smart Supply Chain for Big Data Analysis — Kaggle
The dataset contains operational information from a global supply chain, including orders, products, customers, shipping methods, sales, profit, geographic markets, and delivery performance.
The original dataset is not included in this repository due to its size. It can be downloaded directly from Kaggle using the link above.
🚧 In Progress