The pharmaceutical industry happens to be rapidly modernizing its supply chain so as to meet market demands as well as patient needs. The transition towards advanced therapy medicinal products- ATMPs, rising supply chain flexibility, and the want for sustained remote laboratory access after COVID-19 happen to be driving the significance of the digital laboratory environment. This shift happens to be supported by the rising R&D, testing, as well as analytics services market. Pharma R&D is anticipated to grow from $244B that was seen in 2022 to $285B by 2028. The worldwide testing laboratories market witnessed $84B in expenditures and is also anticipated to grow at a 5-7% CAGR in the next 5 years. In 2023, $6B has been funded in the laboratory automation as well as software market so as to reduce non-value adding tasks, presently occupying c.25% of lab staff’s business-as-usual time.
Laboratories go on to face umpteen challenges when it comes to their daily operations. Operational tasks happen to be overwhelming, and in most of the cases there happens to be a reliance on paper for logs as well as checks. Data management is complex by non-interoperable systems, thereby leading to extensive manual processing. Juggling numerous laboratory IT systems, like laboratory management and information system- LIMS, electronic laboratory notebook- ELN, and document management systems-DMS, with complex interfaces to adjacent systems such as ERP and MES, results in ad hoc calculations for important data. This complexity goes on to lead to poor visibility of key performance indicators- KPIs and a dearth of transparency within laboratory performance. Moreover, optimal equipment utilization gets hindered due to a lack of system-based resource planning for equipment and staff, and also a lack of integrated staff training.
Investing in the digital laboratory journey happens to be essential to staying competitive so as to make sure of future growth as well as adaptability, profitability, and also patient satisfaction.
Throughout the laboratory transformation and the developing roadmap that it is going through, there is indeed a requirement to take into account key developments in the ecosystem so as to adapt to such factors that are influencing and ensure success with digital laboratory design.
Such key developments in the ecosystem go on to enable a standardized, integrated, as well as data centric laboratory in order to increase efficiency, enhance capacities, and ultimately push audit robustness. In any kind of business transformation, one can recognize that the journey to the digital laboratory goes on to rely on people, systems, processes, as well as data landscape.
Developing a modern as well as effective work environment for the laboratory team goes on to involve providing cutting-edge training as well as skill enhancement opportunities. This makes sure that the workforce gets well-prepared for a workplace that’s digitally advanced. By way of streamlining as well as standardizing processes, getting process design incorporated into the laboratory operational structure, and also defining role-specific ownerships, processes don’t just happen to be efficient but at the same time also scalable and automated. The integration of purpose-built laboratory system landscape like LIMS, ELN, e-planning, CDS, etc., automating dataflows, as well as linking these systems with adjacent production systems goes on to establish a cohesive as well as a robust technological foundation. Finally, implementing a governance model in terms of streamlined data management and AI-readiness, in addition to data lifecycle management along with interoperability, goes on to unlock a data governance approach, which in turn adds prominent value.
The pharmaceutical sector increasingly values resilience as well as a flexible supply chain, stressing the requirement for a digital laboratory ecosystem so as to enable businesses to be competitive across the world and deliver to patients with speed. By way of optimising laboratory operational processes and taking into account emerging technologies as well as systems like automation, robotics and also artificial intelligence, laboratories can go on to directly impact the quality, efficiency and responsiveness of the entire supply chain. The future of digital laboratories will need an assessment of the present laboratory operating model along with digital maturity, such as an understanding of major value levers, to help in the design of an efficient, effective, and also future-ready digital laboratory ecology.
