Digitalization is the key to best lab performance : Experts

Digitalization is the key to best lab performance : Experts

Besides enhancing outcomes, it can play a critical role in connecting different units, even surpassing geographical boundaries

  • By Rahul Koul | June 09, 2021

There is a need for cross sectional data integration, believes Kapil Kumar, Chief Scientific Officer, SRF Limited who stresses on the need for integrating the scientific data with the engineering data. 

“For engineering driven companies like us, the software needs to be customized as per flow, from the current to the next system. At SRF, we have already gone through two levels of customization and are now at a level where all our systems are relaxed and stable. However, despite 25 years of digitalization process, integrating all the systems is still a challenge. To make them talk to each other is really difficult yet we are trying to integrate the scientific data with engineering data. The silos need to be integrated," said Kumar. 

Kumar was speaking at the E-conference, "Unlocking Lab Efficiency through Digital Transformation" organized by the Indian Chemical News (ICN) in partnership with PerkinElmer on June 8, 2021. The session was moderated by Pravin Prashant, Editor, Indian Chemical news 

Speaking about the best practices, Vinod Shivanand Honmute, Head - Knowledge Management, Aarti Industries Limited mentioned, “Setting up a goal is the first best practice, followed by a foolproof plan. Then comes the training and awareness among the resources, besides dynamic software with timely updates to meet digitization plans. We have prioritized design of software as per requirement and are also planning to digitize the procurement process. We will be building the Internet of Things (IoT) while digitizing our past knowledge and making it available for integration with present needs. While we are implementing the digitization plan in bits and pieces, we require a wholesome package for the entire system.” 

Emphasizing the need for clarity of purpose on digitalization plans by organizations, Ankit Aggarwal, Head - Research IT and Scientific Computation, PI Industries Limited said, “In India, the R&D budget is always limited and it is difficult to expand or use high end integration without clarity. Since digital culture is a must, to ensure its implementation, commitment from the top management is important. Once the compound is discovered in a lab, the data has to be shared outside the organization, especially when collaborating abroad. For meeting necessary compliances, we have to act fast most of the times and in this scenario, data integration could come handy. IT can play a critical role in connecting different units and even surpass geographical boundaries. While it can’t replace the function of business, it can bring down silos and build trust between teams.” 

It is important for companies to define their strategy for digitalization beforehand, says Sanjay Nandawadekar, Director - IT, Cipla Limited. 

He opines, “Organization has to define what they want to do with data. They may have multiple objectives such as ensuring compliance, creating a lab, process optimization or RoI and for that they require a strategy. Once you have a clear understanding, you need to shortlist vendors based on the need and implementation strategy. While it might take a while to convince the scientists to move from paper based systems to IT based, organizations could begin with priority areas. Technology experts can sit with them and answer their questions on tech adoption. Since a laboratory is a critical part and so are the processes, a lot many things may change in 4-5 years. Therefore, it is important to work with the right partner for best adjustable solutions.” 

Way Forward

When we talk about digitalization at a larger level, the challenge is data transformation from one system to another, mentions Kumar. “Once data collected from instruments or analytical labs is put together, one has to figure out how to transform it for customer systems. We need better optimization tools to bring in more efficiency. AI (Artificial Intelligence) is picking up fast. In a few countries, we have seen supercomputers driving chemist decisions. It could be a holy grail where all the chemical rules are put in the machine and we get all the answers that are beyond human power. It might be a far-fetched idea but I feel there would be a time when engineers can pick up data directly from labs and a lot of time that is lost in translation would be saved.” 

Nandawadekar says Cipla has been already promoting hybrid models where the data integration has been done at a considerable level. It has helped our scientists to work from home. Going forward, scientists working remotely might be able to access data based on proper authorization. This will be a normal thing in the next few years.” 

Experts consider the high cost of digitalization a major hindrance. They say the cost of license and annual maintenance costs act as barriers to the expansion and that providers could make profits on volumes if the costs are taken care of at the earliest. 

Outlining the need for utilizing digital tools to bring in transformation, Patrick Ansems, Director - Informatics Field Application Scientists, EMEAI PerkinElmer Inc. lays thrust on data flow from one system to the other.

"For digital transformation it is important to analyze the needs, which can differ from one organization to another. The key critical aspects on this journey include 3 major points. The first is the transition from paper to electronic and the associated change management aspects for the users. The second point is the need to analyze and organize the workflow which could be different from the current paper process. Finally when all data is captured and structured where needed analytics can be used to bring together data from various sources," added Ansems. 

Manish M. Khandagale, Senior Field Application Specialist, PerkinElmer Inc. shared a few project examples such as SAR analysis from a pharmaceutical company data. He showcased how the hypo-toxicity data analytics could help in exploring commonality between similar compounds and also finding compounds that need to be taken for further analysis.

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