LABORATORY INTEGRATION AND SUSTAINABILITY: CURRENT TRENDS AND EMERGING USE CASES!
Laboratory operations are a vital part of industrial or academic infrastructure, and often involve hands-on or virtual experimentation, progressive technological developments to support them, and value-added learning of professionals involved. Moreover, a breakthrough of industrial revolutions in western world was initiated by technology integration in laboratory / manufacturing process. However, companies often focus on research investments or IPR properties as a key metric of their competitive strength. However, in case of emerging industrial sectors, technology integration with latest expertise is the key to achieving superior research and manufacturing infrastructure. In fact, most of the dominant technology players in current time (such as Apple, IBM, and Alphabet) acquired and integrated latest technology expertise into their research laboratories (and later into manufacturing system) to stay ahead of the market evolution curve.
Process or technology integration in
laboratories is an emerging trend wherein, relevant technologies are
increasingly being employed for new product development, process automation, or
service enhancements. An effective integration approach defines key parameters for
man-machine communication, process applications, and ways to address associated
challenges. This is driven by ongoing advances in the field of analytical
chemistry, information technology, electronics, and material science.
Why do laboratories fail? How can they succeed
in workflow integration or avoid a failure?
Pace of technology evolution is majority of
industries has been breathtaking in recent decades, with proliferation of newer
technologies and greater availability of skilled talent pool fueling the
availability of latest innovations with emerging applications, thereby
shortening product lifecycle and emerging competitive challenges.
Moreover, advent of novel technologies (such as
AI, cloud, big data, robotics) have resulted in generation of significant
volume of laboratory data sets in various end use industries (such as clinical
labs, biobanks, industries, govt labs). The role of data analysis and their
influence in research or commercial success is increasingly being important
especially in a regulated environment. However, laboratories faces various
challenges while embarking on path of technology integration and pursuing
effective data management strategies such as:
·
HIGH OPERATIONAL COST: A significant chunk of operational costs is assigned for workforce
management, and logistics. However, majority of facilities still do not
recognize importance of workflow integration or digitization in reducing
operational expenses and freeing up human capitals for more vital tasks.
·
COMPARTMENTALIZED WORK: Day-to-day operations in majority of tier II or tier III facilities
are still highly compartmentalized, with specific operational steps performed
in silos by dedicated personnel (such as sample preparation, analysis,
inventory management, data analysis etc.). This often leads to long down time
and wide margin of errors.
·
DATA SECURITY & ARCHIVING: Limited reliability of existing digital
infrastructure and inability of comply fully to latest industrial regulations
are the key reasons to rely on paper-based systems for data storage, analysis,
and archiving.
·
REAL-TIME OPERATIONS: In post-COVID world, majority of researchers and industries are
pivoting towards hybrid working model wherein, remote operating capabilities
offer desired flexibility while ensuring efficiency and reduced downtime or
errors. However, several end users are reported to have limited capabilities
for the same in real-time.
·
SUSTAINABILITY ISSUES AND LIMITED COLLABORATIONS: This stems from limited agility of
laboratory operations and process responsiveness to unseen challenges, that
might arise due to lack of knowledge sharing among various departments.
Need for workflow/technology integration in
laboratories and associated opportunities.
Basic laboratory infrastructure
is generally reported with a variety of configurations that are increasingly
imbibing automation and mechanization for various roles and responsibilities.
For example, step-by-step analysis of clinical samples is now giving way to
continuous or sequential analysis that permits a variety of analytes to be
tested in each specimen or multiple specimens getting tested in single run.
However, digital transformation is the epicentre of overall laboratory
modernization that is often challenging and significantly rewarding in the
longer run.
A truly successful lab
digitization involves carefully crafted technology integration in physical and
digital environments, and involve various automated platforms, agile
technologies, vendor neutral architecture, and flexible digital solutions to
integrate and operate them. Some of the associated advantages include:
·
Automation of standardized lab processes
·
Reduced operational expenditure
·
Automated processes and rationalized operations
·
Effective disaster management methodologies
·
Operational flexibility
·
Data security and agility
·
Persona-specific accessibility
·
Collaborative working
·
Rapid pace of technology integration and future
upgrades
·
Ease of audits and protocol validations
·
Economies of scale and platform interfacing
Emerging approaches for effective
integration and automation of laboratory processes include various components
such as workstations, pre-analytical automation platforms, device clusters,
integrated workflow solutions (pre- and post-analytical), data management
solutions, and connectivity solutions.
Emerging use cases for technology integration
in laboratory environment?
Emerging technologies such as AI, robotics, and
built-in sensors with connectivity are replicating into a higher demand for
network-capable laboratory instruments those can offer unrestricted ability to
communicate amongst each other. However, academic, and industrial laboratories are
increasingly adopting process automation and workflow integration to optimize
and modernize their daily operations while improving overall efficiency. Some
of the prominent examples are:
CONTINUOUS MANUFACTURING: Emerging regulatory framework for
product development and manufacturing QA in pharma/biopharma industry is mainly
driven by recent FDA, EMA, and PMDA guidelines. This is leading to incorporation
of newer analytical technologies (such as online QA sensors), better process
control tools, and emphasis to reduce product waste significantly. This would
replicate into higher demand for continuous manufacturing and monitoring tools
such as (bioreactors, raman spectrometers, RFIDs, etc.)
AGRIBIO AND BIOFUELS: Agriculture research labs and
breeders are increasingly interacting with each other for data sharing and
information exchange, which is facilitating optimization of their workflows,
reducing errors, assisting to identify novel applications, and augmenting
interdisciplinary collaborations. This would further supplement the demand for
process automation and integration to reduce errors, improve efficiency and
centralize data tracking and lab analysis
BATTERY MANUFACTURING: Rising adoption for electric
vehicles and public tilt towards renewable energy sources is driving the need
for superior quality and high-capacity energy storage solutions that can be
less polluting and environmentally sustainable. Battery cells undergo a
rigorous QA process to ensure their safety & survivability in varied
environments and applications. However, there is a significant lag in process
development and batch scale along with availability of effective quality
testing platforms, that is expected to support the demand for process
automation and integration of failure analysis and quality control approaches
in target industries.
MINING AND BULK CHEMICALS: Strong emphasis on performance of
energy storage solutions (such as Lithium-ion batteries) and unmet need for
superior performing manufacturing materials has laid bare the gap in mining and
manufacturing processes followed in mining and chemical manufacturing
industries. Evaluation of rare metals or bulk chemicals still follows the
traditional processes of moisture analysis, grading assays, and non-destructive
testing, those operate in silos. However, industries would need to pivot
towards an integrated and collaborative framework to improve product quality,
reduce errors, better performance monitoring, and support newer product
development approaches in metallurgy and geological testing labs.
How does the future look like?
Workflow integration and
automation is increasingly becoming a key necessity of various laboratories as
it is directly related to their long-term sustainability and operational
excellence. Manual or siloed approaches are no longer viable economically and
is giving way to innovative approaches to integrate and automate processes
(either specific aspects or end-to-end integration). Although, implementation
of laboratory automation and process integration is a time consuming and often
cost intensive task, there is a sufficient appetite and greater recognition of
their role in improved competition and long er sustainability among relevant
end users. Their demand growth is poised to be further augmented by next
generation techniques such as AI, IoT, and robotics, those would play an increasingly
significant role in future growth of laboratory excellence.
For any comments or queries on
this article, feel free to get in touch with me at anurag.khirwal@marketsandmarkets.com
Disclaimer: All views and
opinions expressed are of the analyst and do not constitute official point of
view of MarketsandMarkets Research Private Limited or its affiliates or
clients.
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