AAS-Enabled Digital Data Exchange in Pharmaceutical Supply Chains: A Design Science Approach to Eliminating Manual Batch Data Handoffs Between CDMOs and Drug Product Manufacturers

This article has been Reviewed by the following groups

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Abstract

The pharmaceutical supply chain suffers from a persistent and costly inefficiency: critical batch data generated digitally by Contract Development and Manufacturing Organisations (CDMOs) is transmitted to drug product manufacturers as static PDF certificates or Excel spreadsheets, forcing skilled personnel to manually re-enter data into Quality Management Systems (QMS). This introduces transcription errors, delays batch release, undermines data integrity, and creates compliance risk under Good Practice (GxP) regulations. This paper presents a Design Science Research (DSR) artefactthe AAS Open Exchange Portalan interactive platform demonstrating how the Asset Administration Shell (AAS) standard, developed by the Industrial Digital Twin Association (IDTA), can eliminate this bottleneck through automated, machineto-machine batch data exchange between CDMOs and brand owners. The artefact was initially developed during the ISPE Emerging Leader Hackathon (April 2026, Copenhagen), sponsored by Roche, and subsequently extended into a universal MVP. The platform demonstrates bidirectional data exchange, Digital Calibration Certificate (DCC) integration, role-based access control using Attribute-Based Access Control (ABAC), AI-driven demand forecasting, a GxP-compliant audit trail, and a manual bridge mode for organisations not yet fully integrated. The AAS exchange is implemented as a browser-based simulation using client-side JavaScript; no live AAS server or production API was deployed, consistent with the scope of a DSR feasibility study. The paper evaluates the artefact against ALCOA+ data integrity principles, EU GMP Annex 11 requirements, and WHO traceability mandates, demonstrating that the AAS standard is not only technically feasible for pharmaceutical supply chain data exchange but natively satisfies regulatory requirements that current PDF-based workflows actively violate.

Article activity feed

  1. This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22674732.

    Does the introduction explain the objective of the research presented in the preprint? Yes The introduction clearly identifies the problem of manual batch-data handoffs between CDMOs and drug product manufacturers and explains the objective of developing and evaluating an AAS-based approach for automated, machine-to-machine data exchange. It also connects the objective to improving data integrity, reducing manual transcription, and supporting GxP compliance.
    Are the methods well-suited for this research? I don't know
    Are the conclusions supported by the data? I don't know
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear The figures and tables generally support understanding of the proposed architecture, data-exchange workflow, and evaluation results. However, additional presentation of quantitative or production-representative validation results would strengthen interpretation of the system's performance and applicability in regulated pharmaceutical environments
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly The authors provide a generally clear discussion of the findings and their potential implications for pharmaceutical data exchange. They also identify areas for further development. Additional discussion of validation in production-representative GxP environments, including operational controls and implementation challenges, would further strengthen the proposed next steps.
    Is the preprint likely to advance academic knowledge? Somewhat likely The paper contributes a concrete application of Asset Administration Shell (AAS)-enabled digital exchange to pharmaceutical batch-data handoffs, connecting it with data integrity, ALCOA+, and regulated pharmaceutical workflows. That gives it a potentially useful contribution.
    Would it benefit from language editing? No The manuscript is generally well written, with clear technical language and terminology appropriate for the subject. Minor stylistic improvements may be possible, but they do not significantly affect comprehension of the research objectives, methods, results, or discussion.
    Would you recommend this preprint to others? Yes, but it needs to be improved The preprint presents a promising approach to improving digital data exchange in pharmaceutical environments. However, the work would benefit from further evaluation in a production-representative GxP environment. In particular, the authors could strengthen the discussion of computerized system validation/assurance, audit-trail controls, user access and security, change and configuration management, and maintenance of data integrity throughout the data lifecycle. Additional evidence demonstrating how the proposed approach performs under actual regulated workflows would strengthen its practical applicability and compliance-related conclusions.
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes The manuscript would benefit from minor clarification of how the proposed approach would translate to regulated GxP environments. In particular, the discussion could more explicitly address computerized system validation/assurance, audit-trail and access controls, change and configuration management, and maintenance of data integrity throughout the data lifecycle. Clarifying these practical implementation considerations would strengthen the manuscript for a pharmaceutical audience.

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they used generative AI to come up with new ideas for their review.