Order-Fulfillment-Analysis

Order Fulfillment Process Analysis Project Overview This project analyzes the order fulfillment process of a simulated e-commerce company to identify bottlenecks and propose improvements. It demonstrates business analyst skills in process mapping, data analysis, visualization, and actionable recommendations. Objective

• Map the order fulfillment process (Order Received ? Processing ? Shipping ? Delivery).
• Analyze processing, shipping, and delivery times to identify inefficiencies.
• Propose data-driven improvements to enhance efficiency and customer satisfaction. Dataset Simulated dataset (order_fulfillment.csv) with 1,000 orders, including:
• OrderID: Unique order identifier.
• OrderDate: Date of order placement.
• ProcessingTime: Hours to process order.
• ShippingTime: Hours to ship order.
• DeliveryTime: Total hours to deliver order.
• CustomerSatisfaction: Rating (1–5). Methodology

1. Data Creation: Generated simulated data using Python.
2. Process Mapping: Created a flowchart using draw.io.
3. Analysis: Calculated average times and identified bottlenecks (>48 hours).
4. Visualization: Plotted processing time distribution and satisfaction vs. delivery time.
5. Recommendations: Proposed automation, faster shipping, and better communication. Key Findings

• Bottlenecks: DeliveryTime (48 hours) exceed 48 hours.
• Customer Impact: Longer delivery times correlate with lower satisfaction.
• Variability: Processing time is consistent (~24 hours), but delivery time varies widely. Recommendations
• Automate order processing to reduce time to <20 hours.
• Partner with faster shipping providers to cut delivery time to <48 hours.
• Communicate expected delivery times to improve customer satisfaction. Repository Structure
• Order_Fulfillment_Analysis.ipynb: Jupyter Notebook with code and analysis.
• order_fulfillment.csv: Simulated dataset.
• process_map.png: Process flowchart.
• processing_time_dist.png: Distribution of processing times.
• satisfaction_vs_delivery.png: Satisfaction vs. delivery time scatter plot.
• process_summary.txt: Summary of findings and recommendations. How to Run

1. Install Python and required libraries:
2. pip install pandas numpy matplotlib seaborn
3. Clone this repository:
4. git clone https://github.com/your-username/Order-Fulfillment-Analysis.git
5. Open Order_Fulfillment_Analysis.ipynb in Jupyter Notebook and run all cells.
6. View outputs: process_map.png, processing_time_dist.png, satisfaction_vs_delivery.png, process_summary.txt. Tools Used

• Python Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Process Mapping: draw.io
• Environment: Jupyter Notebook Future Improvements
• Incorporate real-world data from an e-commerce company.
• Create a BPMN diagram for detailed process modeling.
• Simulate improvement scenarios (e.g., 20% reduction in delivery time). Contact For feedback or questions, reach out via [matthewkoeberg13@gmail.com] or GitHub Issues.