Authors:
Minh Khoi Nguyen
1
;
Hanh Tran
1
;
2
;
Ileana Ober
1
and
Razan Abualsaud
1
Affiliations:
1
Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse, France
;
2
Military Academy of Saint-Cyr Coëtquidan (AMSCC), CReC Saint-Cyr, France
Keyword(s):
Artificial Intelligence, Process Management System, Process Monitoring, Collaborative Process.
Abstract:
This paper presents an AI-augmented framework for automated and intelligent process monitoring, addressing the inefficiencies of manual progress reporting in Process Management Systems (PMS), which leads to potential inaccuracies and consumes valuable user time. Our research proposes a novel solution that bridges users’ workspaces and PMS, enabling automatic progress reporting based on users’ actions within their preferred tools. The core innovation of our framework pMage lies in employing Artificial Intelligence (AI) techniques to analyze and interpret sequences of user actions, translating them into accurate task progress updates, which significantly reduce manual input and enhance the accuracy of the reporting, thus making the integration of a PMS smoother and more effective. We demonstrate our framework’s applicability through a case study that uses pMage to monitor a brake system manufacturing process with our prototype. As a smart interface, pMage provides a no-code solution to
connect a wide range of user applications to various PMS via their respective APIs. This versatility ensures broad applicability across different organizational contexts and toolsets. Our AI-augmented framework offers a more reliable, efficient, and user-friendly approach than existing monitoring methods.
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