ML and AI Implementation Insights for Bio/Pharma Manufacturing.
This survey conducted by the authors examined factors of Roger's diffusion of innovation theory related to adopting machine learning (ML) and artificial intelligence (AI) in the bio/pharmaceutical manufacturing industry. The sample included industry leaders and bio/pharmaceutical professionals with...
| Publicado en: | BioPharm International Vol. 36; no. 10; pp. 24 - 29 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MJH Life Sciences
Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172859647&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172859647 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1542166X 5NE jtl: BioPharm International issn: 1542166X maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 10 pid: 54670 pub: MJH Life Sciences place: Cranbury, New Jersey artinfo: ui: 172859647 172859647 172859647 172859647 ppf: 24 ppct: 5 formats: fmt: @attributes: type: P tig: atl: ML and AI Implementation Insights for Bio/Pharma Manufacturing. aug: au: PAZHAYATTIL, AJAY BABU KONYU-FOGEL, GYONGYI affil: Pharmaceutical management consultant for cGMP World sug: subj: Machine Learning Artificial Intelligence Pharmaceutical Companies Manufacturing Industry Decision Making United States Food and Drug Administration Diffusion of Innovation Human Questionnaires Surveys Profits Evaluation Investments Evaluation Collaboration Natural Language Processing Education Professional Competence Data Science Utilization Product Development Evaluation ab: This survey conducted by the authors examined factors of Roger's diffusion of innovation theory related to adopting machine learning (ML) and artificial intelligence (AI) in the bio/pharmaceutical manufacturing industry. The sample included industry leaders and bio/pharmaceutical professionals with experience in regulatory affairs, product development, and commercial operations at FDA-regulated bio/pharmaceutical companies. Participants responded to a survey administered with a direct link for confidentiality. Respondents were asked 44 questions on the adoption challenges of ML and AI practices in bio/pharma manufacturing. According to the results, creating ML and AI solutions catering to manufacturing segments can quickly generate a customer base. The survey also found that the first areas to be affected by AI are likely to be those related to process efficiency because decision making related to manufacturing efficiency tends to be less regulated compared to quality decision making. In addition, the greatest opportunity for return on investment (ROI) in ML and AI implementation in bio/pharma manufacturing maybe waste reduction, batch yields, and faster batch completion, which are well-aligned with existing lean manufacturing principles. In the current scenario, it is essential to provide training courses for bio/pharma professionals to collaborate in identifying opportunities and developing and implementing ML and AI projects. This will help professionals adapt to the digital revolution and stay ahead in the rapidly changing bio/pharma manufacturing industry. pubtype: Trade Publication doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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