Predictive analytics for laboratory equipment.
By Jeff West, XiltriX North America, LLC
Labs Develop a
by Embracing IoT Integration
For life science organizations focused on developing
new therapies and treatments, one of the biggest risk
factors is the loss of time. Without the proper processes and
systems in place, the challenges of replicating results, mitigating failures, ensuring the consistency of products and delivering them to market can grow exponentially over time.
Therefore, it is becoming increasingly important in today’s
laboratory facilities to have more control and visibility over
every input and step of the development process.
Predictive analysis, or developing a thesis of future
events based on past behaviors, can range from the simple
exercise of visually examining patterns in measurements
and theorizing possible future outcomes, to building and
testing sophisticated statistical algorithms based on robust
historical data from various sources. This same real-time
data can be used for monitoring, diagnostics, quality reporting and preventative maintenance.
The Internet of Things (Io T)—interconnected devices,
such as laboratory equipment and sensors, working
homogeneously in a virtual environment—is quickly
enabling scientific researchers and other personnel to
perform tasks in a laboratory and gather data that would
typically take hours, even days, to manually complete.
The life science industry is often conservative when
adopting new technologies, but has been a major pro-
ponent in the adoption of Io T technology. International
Data Corporation (IDC) reports that life science compa-
nies, particularly manufacturing, will increase spending
in Io T investments by close to $200 billion in 2018.
Predictive analytics for lab equipment
There are several important ways in which advances in
predictive analytics facilitate innovative practices for life
science organizations. By utilizing sensors on laboratory assets and equipment, organizations can monitor
and anticipate equipment malfunctions and proactively
schedule any calibration or repairs. Predicting potential
failures and optimizing maintenance schedules ensures
operations are safeguarded from downtime—or worse,
In most instances, technologies that enable predictive
analytics provide real-time notifications and feedback
for researchers. Organizations actively monitor any
anomalies on equipment operation and continuously
control conditions. Certain solutions enable researchers
to access equipment data through an online portal and
generate automated reports for quality and regulatory
compliance. These systems also send real-time alerts