This Python script uses a BigQuery SQL query to analyze warehouse order fulfillment data and identify areas for improvement in warehouse operations. By calculating the number of orders fulfilled, a business can assess which warehouses are meeting fulfillment goals and which ones are falling short. This information can help increase customer satisfaction and revenue.

Warehouse order fulfillment is a critical component of a business's success. Customers expect their orders to be delivered on time and in full. Failure to meet these expectations can result in dissatisfied customers and lost revenue. Inefficient warehouse operations can lead to longer fulfillment times and higher costs. It is essential to analyze warehouse order fulfillment data regularly to identify areas for improvement and optimize operations.

To use this script, follow these steps:

1. Install the required packages by running pip install -r requirements.txt.
2. Set up authentication for your Google Cloud account by following the instructions here.
3. Update the project_id and dataset_id variables in fulfillment_analysis.py with your project and dataset IDs.
4. Run the script by running python fulfillment_analysis.py.

Note: If the Colab notebook is not being displayed, please copy the URL and paste it on nbviewer so you can see the code.

Please note that this code is intended for educational and non-commercial use only.

Contributions to this repository are welcome. If you find a bug or have suggestions for improvement, please open an issue or submit a pull request.

This project was created by Santiago Moreno Velasquez as part of the Google Analytics Certification courses.