This project aims to analyze supply chain data to identify inefficiencies, optimize logistics, and improve overall performance. The key objectives include:
- Understanding supply chain bottlenecks.
- Analyzing key performance metrics such as lead time, order fulfillment, and inventory levels.
- Visualizing trends and patterns in supply chain operations.
- Providing actionable insights for supply chain optimization.
- Python 3.11 for data processing and analysis.
- Pandas & NumPy for data manipulation.
- Matplotlib & Seaborn for data visualization.
- Jupyter Notebook for interactive analysis.
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Data Collection:
- The dataset is loaded from a CSV file (supply_chain_data.csv).
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Data Preprocessing:
- Handling missing values and data cleaning.
- Converting data types where necessary.
- Checking for duplicated values
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Exploratory Data Analysis (EDA):
- Summary statistics of key metrics.
- Visualizing data distributions and correlations.
- Identifying trends in supply chain performance.
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Visualization & Insights:
- Using Matplotlib and Seaborn for graphical representation.
- Identifying outliers and inefficiencies.
- Gaining insights into order processing times, supplier performance, and logistics bottlenecks.
- Certain suppliers have significantly higher lead times, impacting delivery schedules.
- Order fulfillment rates vary across different regions, suggesting optimization opportunities.
- Inventory levels fluctuate inconsistently, indicating the need for better demand forecasting.
- ogistics inefficiencies were identified in specific transportation routes.