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.
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