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Framework for Automating Multi-Team Workflows to Maximize Operational Efficiency and Minimize Redundant Data Handling
Subject area: Science,Engineering and Technology · Area of research: Data Handling
Abstract
The increasing complexity of multi-team workflows in organizations necessitates efficient automation frameworks to maximize operational efficiency and minimize redundant data handling. This study proposes a comprehensive framework for automating multi-team workflows, building on existing research in workflow automation and integrating demonstrated methods for reducing operational burdens and increasing time savings. By leveraging advanced data synchronization, task prioritization, and real-time communication technologies, the framework ensures seamless collaboration across teams while eliminating inefficiencies caused by data redundancy and manual processes. The proposed framework incorporates machine learning algorithms for intelligent task allocation and adaptive process management, ensuring optimal utilization of resources. Key innovations include centralized data pipelines that prevent duplication, automated triggers for task execution, and real-time performance analytics to monitor and refine workflows dynamically. The framework was validated through implementation in diverse organizational contexts, demonstrating a measurable reduction in task completion time, improved data accuracy, and enhanced cross-team coordination. This work emphasizes the importance of stakeholder alignment during automation adoption, offering insights into strategies for managing resistance and ensuring user buy-in. Additionally, the study highlights the role of automation in fostering organizational agility by enabling teams to focus on high-value tasks rather than repetitive manual efforts. The integration of cybersecurity measures further ensures data integrity and compliance, addressing concerns over potential vulnerabilities in automated workflows. The findings contribute to the broader discourse on operational efficiency by providing actionable methodologies for organizations aiming to streamline processes and enhance productivity. Key benefits of the framework include improved decision-making speed, reduced operational costs, and a scalable model adaptable to various industries. Future directions include exploring integration with emerging technologies such as blockchain for enhanced data security and augmented reality for immersive team collaboration. The study concludes by advocating for a paradigm shift towards holistic workflow automation to address evolving organizational challenges.
Keywords
Workflow Automation, Operational Efficiency, Data Redundancy, Machine Learning, Real-Time Analytics, Cross-Team Collaboration, Cybersecurity, Stakeholder Alignment, Organizational Agility
How to cite this paper
@article{1703282,
author = {Adebusayo Hassanat Adepoju, Blessing Austin-Gabriel, Adeoluwa Eweje, Anuoluwapo Collins},
title = {Framework for Automating Multi-Team Workflows to Maximize Operational Efficiency and Minimize Redundant Data Handling},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {9},
pages = {663-679},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703282.pdf},
abstract = {The increasing complexity of multi-team workflows in organizations necessitates efficient automation frameworks to maximize operational efficiency and minimize redundant data handling. This study proposes a comprehensive framework for automating multi-team workflows, building on existing research in workflow automation and integrating demonstrated methods for reducing operational burdens and increasing time savings. By leveraging advanced data synchronization, task prioritization, and real-time communication technologies, the framework ensures seamless collaboration across teams while eliminating inefficiencies caused by data redundancy and manual processes. The proposed framework incorporates machine learning algorithms for intelligent task allocation and adaptive process management, ensuring optimal utilization of resources. Key innovations include centralized data pipelines that prevent duplication, automated triggers for task execution, and real-time performance analytics to monitor and refine workflows dynamically. The framework was validated through implementation in diverse organizational contexts, demonstrating a measurable reduction in task completion time, improved data accuracy, and enhanced cross-team coordination. This work emphasizes the importance of stakeholder alignment during automation adoption, offering insights into strategies for managing resistance and ensuring user buy-in. Additionally, the study highlights the role of automation in fostering organizational agility by enabling teams to focus on high-value tasks rather than repetitive manual efforts. The integration of cybersecurity measures further ensures data integrity and compliance, addressing concerns over potential vulnerabilities in automated workflows. The findings contribute to the broader discourse on operational efficiency by providing actionable methodologies for organizations aiming to streamline processes and enhance productivity. Key benefits of the framework include improved decision-making speed, reduced operational costs, and a scalable model adaptable to various industries. Future directions include exploring integration with emerging technologies such as blockchain for enhanced data security and augmented reality for immersive team collaboration. The study concludes by advocating for a paradigm shift towards holistic workflow automation to address evolving organizational challenges.},
keywords = {Workflow Automation, Operational Efficiency, Data Redundancy, Machine Learning, Real-Time Analytics, Cross-Team Collaboration, Cybersecurity, Stakeholder Alignment, Organizational Agility},
month = {March},
}