https://github.com/sohanoor/procuresmart

ProcureSmart: Automated Sales Data and Order Management Script through Python & Selenium
https://github.com/sohanoor/procuresmart

automation order-management python sales selenium selenium-python selenium-webdriver

Last synced: 5 months ago
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ProcureSmart: Automated Sales Data and Order Management Script through Python & Selenium

• Host: GitHub
• URL: https://github.com/sohanoor/procuresmart
• Owner: sohanoor
• Created: 2024-09-14T08:29:27.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-14T08:49:34.000Z (almost 2 years ago)
• Last Synced: 2025-04-06T09:18:53.278Z (about 1 year ago)
• Topics: automation, order-management, python, sales, selenium, selenium-python, selenium-webdriver
• Language: Python
• Homepage:
• Size: 8.61 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# ProcureSmart

ProcureSmart: Automated Sales Data and Order Management Script through Python & Selenium

# Project Documentation

## Aim

The aim of this project is to automate the process of collecting sales data, logging into vendor websites, retrieving vendor offers and product information, comparing prices, and managing orders. The project leverages Microsoft Excel, Python, Selenium, and Power BI to streamline these tasks, predict future product quantities, and create an efficient ordering system.

## Motivation

The primary motivation for this project is to enhance efficiency and accuracy in the procurement process. By automating data collection and analysis, the project aims to reduce manual effort, minimize errors, and optimize purchasing decisions. This will lead to cost savings and better inventory management.

## Process Followed

1. **Data Collection:**

   - **Sales Data:** Collect sales data and store it in Microsoft Excel.

   - **Vendor Offers:** Use Selenium to log into vendor websites with authentication and retrieve the latest vendor offers.

   - **Product List and Prices:** Extract product lists and prices from the vendor websites.

2. **Data Analysis:**

   - **Price Comparison:** Compare the prices of products to identify the lowest price options.

   - **Stock and Sales Report Comparison:** Compare product data from sales reports and stock reports to identify discrepancies and trends.

3. **Prediction:**

   - **Quantity Prediction:** Use Python to predict the quantity of products needed for the next week based on historical sales data.

4. **Order Management:**

   - **Create Product List:** Generate a product list for the next order based on the prediction and analysis.

   - **Place Order:** Automate the process of placing orders with vendors.

5. **Reporting and Visualization:**

   - **Power BI:** Use Power BI to create dashboards and visualizations for reporting and monitoring key metrics.

## Technical Details

- **Data Collection and Automation:**

  - **Microsoft Excel:** For storing and organizing sales data and analysis results.

  - **Python:** For data processing, analysis, and prediction.

  - **Selenium:** For web scraping and automating interactions with vendor websites.

- **Analysis and Prediction:**

  - **Python Libraries:** Pandas, NumPy, Scikit-learn for data manipulation, analysis, and predictive modeling.

- **Reporting:**

  - **Power BI:** For creating interactive dashboards and visualizations of sales data and predictions.

## Business Benefits

- **Increased Efficiency:** Automation reduces manual data entry and processing time.

- **Cost Savings:** Comparison of prices ensures the best deals are selected, and accurate ordering reduces overstocking and stockouts.

- **Better Decision Making:** Predictive analytics provides insights for informed purchasing decisions.

- **Enhanced Visibility:** Power BI dashboards offer clear visibility into sales performance and inventory status.

## Knowledge Acquired

- **Automation Techniques:** Experience in automating data collection and web interactions using Selenium and Python.

- **Data Analysis:** Skills in data manipulation and analysis using Python and Excel.

- **Predictive Modeling:** Ability to develop and apply predictive models for inventory management.

- **Reporting Tools:** Proficiency in using Power BI for creating visualizations and dashboards.

## Future Plan

- **Integration:** Integrate additional data sources for more comprehensive analysis.

- **Enhancements:** Improve predictive models with more advanced algorithms and larger datasets.

- **Scalability:** Adapt the system to handle larger volumes of data and more complex scenarios.

- **User Interface:** Develop a user-friendly interface for easier interaction with the system and reporting tools.

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This documentation provides a comprehensive overview of the project, detailing the aim, motivation, steps involved, technical details, business benefits, knowledge gained, and future plans.