Managing Engineering Documentation Knowledge at Scale: Adobe Experience Manager Architecture and Artificial Intelligence Automation for Technical Content in Manufacturing Enterprises

Abstract

Manufacturing enterprises generate large volumes of technical documentation, including product specifications, work instructions, and engineering change notices, across multiple plants and business units, and this documentation is frequently fragmented across disconnected file shares, legacy document systems, and plant-specific repositories. Existing engineering knowledge management research has developed ontology-based and knowledge-graph-based approaches to structuring this content, but these approaches typically require dedicated ontology engineering effort and are not always compatible with the content platforms manufacturing organizations already operate for other purposes. This paper proposes an architecture that combines a structured web content and digital asset management platform, Adobe Experience Manager, with artificial-intelligence-assisted automation, including auto-tagging, metadata extraction, and duplicate detection, to manage engineering documentation as structured, reusable knowledge units at enterprise scale. The architecture is illustrated through a multi-plant scenario involving specifications, work instructions, and engineering change notices, and is discussed in relation to knowledge-graph-based alternatives from the recent literature. The paper argues that a content-platform-centric approach offers a lower-barrier entry point for organizations that have not yet invested in formal ontology engineering, while identifying the specific knowledge-representation capabilities such an approach cannot substitute for. Limitations and directions for empirical validation are discussed.