Machine Learning and Data Analysis for Optimal Productivity of Pharmaceutical Water Systems
Publication
Article
BioPharm InternationalBioPharm International, Manufacturing and Facilities November 2022
Volume 2022 eBook
Issue 4
Pages: 12–17
Leveraging smart technology and SMEs for significant operational and financial benefits.
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Today’s work environment presents unique challenges, ranging from managing huge quantities of data to recruiting and maintaining employees with the critical skills needed for manufacturing operations. Through these challenges, how can companies ensure that a business maintains regulatory compliance, ensures quality, retains process knowledge, and safeguards business continuity? This can be done by leveraging machine learning and data analysis from original equipment manufacturers (OEMs) with the expertise to provide system operational feedback.
Effective use of data represents one way that manufacturers can address these issues.Through the examination of an example, the authors of this article will show how the use of machine learning, artificial intelligence (AI), and subject matter experts (SMEs) enabled one major pharmaceutical customer to better manage their water systems. Digitalization empowered SMEs from the equipment supplier to use operational history, process acumen, troubleshooting knowledge, key performance indicator (KPI) analysis, and data transparency for critical insights into every facet of system operation. This also provided financial benefits to the end user by managing production cycles, capital circumvention, quality assurance, increased reliability, alarm avoidance, decreased labor cost for troubleshooting, and many more. The use of data management can also drive increased automation so that plant labor can focus on more high-value activities with the confidence their water systems are running optimally.
Read the article in the Manufacturing and Facilities 2022 eBook
Article details
BioPharm International
eBook: Manufacturing and Facilities 2022
November 2022
Pages: 12–17
Citation
When referring to this article, please cite it as C. Robinson and J. Robes, “Machine Learning and Data Analysis for Optimal Productivity of Pharmaceutical Water Systems,” BioPharm International, Manufacturing and Facilities eBook 2022 (November 2022).
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