International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Automating Data Entry and Capture: A Custom Build Robotic Process Automation Development Approach

Author(s) Mr. Teody Arboneda Banagan
Country Philippines
Abstract This research paper, titled "Automating Data Entry and Capture: A Custom Build Robotic Process Automation Development Approach," addresses the inefficiencies and inaccuracies inherent in manual data entry and capture processes within organizations, particularly when dealing with Excel, SharePoint, and diverse web data sources. These manual methods are identified as time-consuming, error-prone, and detrimental to productivity and scalability. Existing generic automation tools often fall short in providing tailored solutions for specific organizational needs, leading to increased operational costs, data redundancy, and potential security risks.
The primary objective of this study is to evaluate the effectiveness of a custom-built Robotic Process Automation (RPA) approach to automate these processes. This involves developing RPA bots specifically designed for organizational workflows, focusing on data extraction from Excel, SharePoint, and various web sources. The research aims to demonstrate how custom RPA solutions can improve data accuracy, enhance efficiency, and offer better scalability compared to traditional manual methods and generic automation tools. Additionally, the study seeks to identify best practices for RPA implementation and explore future advancements in the field. The findings are expected to provide practical guidance for businesses seeking to streamline data processes, reduce operational costs, and improve data quality, contributing to the broader knowledge of RPA in data entry and capture
Field Computer Applications
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-08-28
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.54479
Short DOI https://doi.org/g9z27b
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