Design-for-Six-Sigma To Develop a Bioprocess Knowledge Management Framework.

PDA J Pharm Sci Technol

Biologics Discovery, Bioprocess Development, Biologics Manufacturing Science & Commercialization, Merck Research Laboratories Information Technology, Merck and Co, Inc.

Published: April 2016

AI Article Synopsis

  • The biopharmaceutical industry faces pressure to improve productivity due to high development costs, leading to the need for a streamlined approach using knowledge management (KM).
  • A KM strategy was developed applying Design-for-Six-Sigma (DFSS) to enhance efficiency, minimize resource use, and ensure quality in product development through a focus on customer needs.
  • The strategy consisted of nine workstreams designed to align with best practices, address gaps, and use existing resources effectively, while tracking progress with specific performance metrics.

Article Abstract

Unlabelled: Owing to the high costs associated with biopharmaceutical development, considerable pressure has developed for the biopharmaceutical industry to increase productivity by becoming more lean and flexible. The ability to reuse knowledge was identified as one key advantage to streamline productivity, efficiently use resources, and ultimately perform better than the competition. A knowledge management (KM) strategy was assembled for bioprocess-related information using the technique of Design-for-Six-Sigma (DFSS). This strategy supported quality-by-design and process validation efforts for pipeline as well as licensed products. The DFSS technique was selected because it was both streamlined and efficient. These characteristics permitted development of a KM strategy with minimized team leader and team member resources. DFSS also placed a high emphasis on the voice of the customer, information considered crucial to the selection of solutions most appropriate for the current knowledge-based challenges of the organization. The KM strategy developed was comprised of nine workstreams, constructed from related solution buckets which in turn were assembled from the individual solution tasks that were identified. Each workstream's detailed design was evaluated against published and established best practices, as well as the KM strategy project charter and design inputs. Gaps and risks were identified and mitigated as necessary to improve the robustness of the proposed strategy. Aggregated resources (specifically expense/capital funds and staff) and timing were estimated to obtain vital management sponsorship for implementation. Where possible, existing governance and divisional/corporate information technology efforts were leveraged to minimize the additional bioprocess resources required for implementation. Finally, leading and lagging indicator metrics were selected to track the success of pilots and eventual implementation.

Lay Abstract: A knowledge management framework was assembled for bioprocess-related information using a streamlined and efficient technique that minimized team leader and member resources. The technique also highly emphasized input from the staff, who generated and used the knowledge, information considered crucial to selection of solutions most appropriate for the current knowledge-based challenges in the organization. The framework developed was comprised of nine workstreams, constructed from related solution buckets which were assembled from individual solution tasks that were identified. Each workstream's detailed design was evaluated against published and established best practices, as well as the project charter and design inputs. Gaps and risks were identified and mitigated to improve robustness of the proposed framework. Aggregated resources (specifically expense/capital funds and staff) and timing were estimated to obtain vital management sponsorship for implementation. Where possible, existing governance and information technology efforts were leveraged to minimize additional bioprocess resources required for implementation. Finally, metrics were selected to track the success of pilots and eventual implementation.

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