Walmart Shopping Automation 

Developed and implemented an automation bot using Automation Anywhere 360 to streamline the product selection and checkout process for Walmart’s online platform. The bot was designed to read product codes from an Excel file and automatically search for and select the corresponding items on the Walmart website. This ensured precise product selection based on predefined product codes rather than subjective criteria like price, color, or ratings.

Once the products were selected, the bot proceeded to complete the checkout process by entering shipping details, applying any applicable promo codes, and handling the payment using demo credit card information. This end-to-end automation optimized the shopping process, reducing manual intervention and improving the overall efficiency of online purchases.

File Management Automation 

Developed a robust file management automation bot using Power Automate Desktop to streamline data consolidation and reporting processes. The bot was designed to read data from multiple Excel files, typically sourced from different departments or teams, and merge the information into a single, comprehensive report. This automated process eliminated the need for manual copying and pasting of data, reducing errors and saving significant time.

Once the data was consolidated, the bot generated pivot tables to provide key insights, such as summaries, trends, and comparisons, making it easier for stakeholders to analyze large datasets. The pivot tables were automatically formatted to ensure consistency and clarity, tailored to the specific reporting needs of the organization.

After creating the report, the bot then distributed the final version via email to predefined recipients, ensuring that all relevant parties received the information promptly and without manual intervention. The automated workflow improved efficiency in data handling, reporting, and communication, significantly enhancing overall productivity.

Automated Inventory Management System (AIMS) 

Developed AIMS to streamline inventory tracking for a Quick Service Restaurant (QSR) using Python libraries like Pyodbc, Tkinter, and Pandas, integrated with an SQL Server backend. The system efficiently managed real-time inventory levels, user activities, and stock updates, automatically adjusting quantities as materials were consumed or replenished.

AIMS featured a Tkinter-based interface for easy data input and monitoring, allowing staff to track stock, generate usage reports, and receive low-stock alerts. Using Pandas, it also generated analytical reports that provided insights into inventory trends and demand, helping optimize purchasing and reduce waste.

By automating inventory processes, AIMS improved operational efficiency, reduced manual errors, and ensured timely stock replenishment, ultimately enhancing the restaurant’s overall performance.