Integrating Gen Ai For Predictive Maintenance And Process Optimization In Chemical Manufacturing
Abstract
This research is focused on the improvement of chemical production process in PT Mandiri Inovasi Bersama specifically in maintenance, quality check, and production schedules that are all still highly manual and often causes delays. To overcome these challenges, this thesis proposes an enterprise architecture design integrating Generative Artificial Intelligence (GenAI) to enable predictive maintenance and process optimization in the manufacturing process. DSR is used as the research method and The Open Group Architecture Framework is adopted as the architectural methodology and ArchiMate is used as the visualization medium. Architecture development takes place in the important phases of the TOGAF ADM like Architecture Vision, Business Architecture, Application Architecture and Technology Architecture. Validation by experts was performed via expert interview and measured using Content Validity Index (I-CVI and S-CVI/Ave). The results indicate that there is a strong perception of value, work efficiencies to be gained, and effectiveness with the model presented to address important production problems and performance, along with decreased downtime. This framework acts as a strategic plan for the adoption of GenAI into the industrial field to help a transformation from a reactive to proactive manufacturing environment.
Key words— generative AI, enterprise architecture, TOGAF, predictive maintenance, quality control, process optimization
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