Supply chain digitization

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Vision for digital maturity in the integration between biomanufacturers and partner organizations

Biopharmaceutical supply chains have evolved beyond single-source manufacturing to a complex network of collaborations and partnerships between sponsors and contract organizations. However, the expectations for deeper integration and visibility, automation and flexibility have increased and the lack of a standard framework has led to bespoke solutions which are unsustainable. This paper explains the current approaches to connecting biomanufacturing organizations, and the key benefits of improved digital integration. It articulates principles and patterns in the form of a simple maturity model that can be used to assess current state and plan for a more digitally aligned future.

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The future of the inbound supply chain: implementing electronic data exchange (EDE)

This paper proposes that shared and electronic data exchange (EDE) is a cornerstone of collaboration and innovation in the supply chain. The paper aims to demystify EDE, set out its importance for supply chain effectiveness, capture its benefits and help people understand the practical enablers.

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Electronic data exchange: a baseline survey summary

A benchmark survey which sets out to establish the baseline for electronic data exchange across participating members, to understand: How suppliers and biomanufacturers were working with EDE for raw materials across their inbound supply chain, whether they were using the ASTM standard and, if so, its application and value to their company and to provide Insights for improving EDE across the sector. The results cover utilization, prioritization, ASTM E3077-17 standard utilization and the benefits and challenges in implementing it, EDE maturity, benefits and challenges for EDE in the inbound supply chain, EDE accelerator and other conclusions.

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Regulatory: Feedback to DMKA questions to critical GXP augmented intelligence

BioPhorum response to work being conducted by The Danish Medicines Agency (DKMA) to define criteria for the application of artificial intelligence (AI) and machine learning (ML) across GxP-regulated areas. The questions  asked by the agency considered the requirements and quality of data used to build, test and validate an algorithm, and how it would respond to biases and deviations in results.

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