Heerts, Tanya

English

The critical understanding, on which all else hinges in the biopharmaceutical industry, is that value is only added when a drug or a biologic is produced as a safe, efficacious first-pass product, in an efficient and effective manner. Every other activity is an expense and a cost to the company. In a tightly regulated industry that is facing turbulent times, the need to reconcile rising costs while still maintaining quality within the processes and products is imperative within the biopharma manufacturing sector. Current literature suggests that data science will have major implications in the future of biopharmaceutical manufacturing, however this emerging field still faces a long road for development and refinement for systematic use within manufacturing. Nonetheless, the strategies behind lean and data science essentially embody the same purpose: reduce waste and inefficiencies within the production flow and processes. The purpose of this paper is to demonstrate the feasibility of using big data analytics as an advanced lean manufacturing tool, as well as propose a possible model for integration with respect to constraints and challenges faced with utilizing big data analytics within the biopharma manufacturing sector

University of Northern Iowa

A Feasibility Study on Utilizing Data Science as an Advanced Lean Manufacturing Tool in the Biopharma Manufacturing Sector

https://scholarworks.uni.edu/cgi/viewcontent.cgi?article=4897&context=grp

A Feasibility Study on Utilizing Data Science as an Advanced Lean Manufacturing Tool in the Biopharma Manufacturing Sector

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University of Northern Iowa