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Continuous pharmaceutical manufacturing has matured from a technical alternative to batch production into a strategically important manufacturing paradigm. Its promise lies in integrated material flow, smaller production footprints, improved process understanding, flexible scale-out, and the possibility of real-time quality assurance. Yet the quality systems used to govern continuous manufacturing often remain conceptually anchored in batch-era assumptions. The central problem addressed in this article is the mismatch between continuous manufacturing’s dynamic data environment and the inherited quality logic of discrete batch testing, fixed specifications, and retrospective release decisions. In batch quality logic, quality is often treated as a property confirmed after production. In continuous manufacturing, however, quality must be interpreted, predicted, and controlled while the process is still unfolding. This article proposes Lifecycle Intelligence as a theory-driven model for rethinking quality in continuous pharmaceutical manufacturing systems. Lifecycle Intelligence is defined as a quality paradigm in which process data, predictive models, control strategies, and lifecycle learning mechanisms are integrated to maintain quality dynamically across development, validation, commercial production, and post-approval improvement. The model shifts attention from isolated product release to continuous quality cognition. The argument advanced is that continuous manufacturing cannot reach its full quality and regulatory potential if it is governed primarily by batch quality logic. A lifecycle intelligence paradigm is needed to convert continuous data streams into validated quality decisions. This shift will require not only technical advances, but also new validation practices, model governance structures, regulatory dialogue, and workforce capabilities.

Digital batch records have commonly been implemented as electronic substitutes for paper documentation, preserving the logic of retrospective compliance rather than transforming the logic of pharmaceutical knowledge. This narrow implementation view treats the batch record as a repository of completed events, signatures, deviations, and release evidence. Such a view is increasingly insufficient for manufacturing environments shaped by automation, continuous processing, process analytical technology, and real-time quality expectations. This article reconceptualises digital batch records as pharmaceutical knowledge systems rather than electronic documentation artefacts. The central argument is that a digital batch record should not merely record what occurred during manufacturing, but should structure why it occurred, how it relates to process understanding, and how it informs quality decisions. This reframing is especially important for continuous manufacturing, where batch boundaries, material histories, and quality evidence are dynamic rather than fixed. The objective of the article is to develop a conceptual systems perspective on digital batch records for continuous manufacturing and regulatory inspection. The article synthesises peer-reviewed literature on pharmaceutical digitalisation, continuous manufacturing, data integrity, knowledge management, process control, real-time release testing, and regulatory science. It does not present new empirical data, but constructs a conceptual model from existing evidence and emerging regulatory trends. The article defines the system boundary of digital batch records, explains their knowledge logic, connects them to pharmaceutical knowledge management, and positions them as infrastructure for continuous manufacturing and inspection transformation. It argues that digital batch records can integrate process data, material traceability, critical quality attributes, audit trails, deviation logic, and lifecycle knowledge into a structured manufacturing intelligence layer. Four tables are used to clarify the proposed record logic, continuous manufacturing integration, inspection transformation, and knowledge-system architecture. The article concludes that digital batch records should be designed as the cognitive infrastructure of pharmaceutical quality. When architected as knowledge systems, they can support real-time quality assurance, predictive process oversight, continuous improvement, and more transparent regulatory interaction. The shift from record-keeping to knowledge-driven assurance is therefore not a technical upgrade alone, but a transformation in how pharmaceutical manufacturing knows, governs, and demonstrates quality.