Pharmaceutical Outsourcing Q2 2024 - 23

CONTRACT RESEARCH
SECTION TITLE
Transforming Operational
Potential with AI and IoT
Artificial intelligence and the Internet of
Things technologies are transforming
the very framework of how equipment
maintenance is perceived and implemented.
This shift is critical in today's fast-paced,
data-driven world where downtime not only
means lost productivity but also potentially
hampers critical research and development
activities. By leveraging AI to predict
equipment failures before they happen,
the AI-Predicted Health Score ensures
that laboratory operations can proceed
uninterrupted, thereby safeguarding
the integrity of scientific research and
manufacturing outputs.7
Furthermore, the incorporation of
IoT technologies into this predictive
framework allows for the seamless
gathering and analysis of vast amounts
of operational data. This real-time data
collection and analysis are pivotal in
identifying not just potential equipment
failures,
but
also
in
optimizing
equipment performance and energy
usage, contributing to more sustainable
laboratory and manufacturing practices.
The AI-Predicted Health Score, therefore,
goes beyond maintenance, touching upon
the broader goals of operational efficiency,
sustainability, and the facilitation of highquality,
uninterrupted scientific inquiry
and production.9
By continuing to extend the AI-Predicted
Health Score technology across a wider
array of equipment, Elemental Machines
hopes to empower researchers and manufacturers
with the tools they need to achieve
breakthroughs, setting new standards in
equipment management, and ensuring that
laboratories and manufacturing facilities can
operate at the pinnacle of efficiency, reliability,
and sustainability.
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References
1. Achouch, M., et al. On Predictive Maintenance in
Industry 4.0: Overview, Models, and Challenges. Applied
Sciences. Published 2022 August 12.
2. Karuppiah, K., et al. On sustainable predictive
maintenance: Exploration of key barriers using an
integrated approach. Sustainable Production and
Consumption. Published 2021 July.
3. Lee, J., et al. Intelligent Maintenance Systems and
Predictive Manufacturing. Journal of Manufacturing
Science and Engineering. Published 2020 August 18.
4. Bousdekis, A., et al. Decision Making in Predictive
Maintenance: Literature Review and Research Agenda
for Industry 4.0. IFAC-PapersOnLine. Published 2019
January 1.
5. Shin, W., et al. AI-assistance for predictive maintenance
of renewable energy systems. Energy. Published 2021
April 15.
6. Arpilleda, J.Y. Exploring the Potential of AI and Machine
Learning in Predictive Maintenance of Electrical Systems.
International Journal of Advanced Research in Science,
Communication and Technology. Published 2023.
7. Carvallo, P., et al. Multi-cloud Applications Security
Monitoring. Green, Pervasive, and Cloud Computing:
12th International Conference, GPC 2017, Cetara, Italy,
May 11-14, 2017, Proceedings 12. Published 2017 April
13.
8. Chen, Y., et al. Preserving user query privacy in cloudbased
security services. Journal of Computer Security.
Published 2014 December 16.
9. Samonte, M.J.C., et al. Internet-of-Things Based Smart
Laboratory Environment Monitoring System. 2021 IEEE
8th International Conference on Industrial Engineering
and Applications (ICIEA). Published 2021.
A serial entrepreneur in IoT, medical
devices, and wearables, Sridhar
Iyengar was a founder of Misfit,
makers of elegant wearable products
acquired by Fossil in 2015. Prior to
Misfit, he founded AgaMatrix based on his Ph.D.
research, a blood glucose monitoring company
that made the world's first iPhone-connected
medical device. Sridhar holds more than 50 U.S. and
international patents and received his Ph.D. from
Cambridge University as a Marshall Scholar.
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