Lab 4.0: Making Digital Transformation Work for Your Laboratory

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Digital transformation is occurring in many aspects of our everyday life, from the ability to control household appliances through apps, to monitoring our health through wearable devices. But it’s not just happening at home: we are in a fourth industrial revolution – Industry 4.0 – where digital technologies connect automated processes and equipment, monitor and control supply chains, and work alongside robots programmed to leverage artificial intelligence (AI).

This transformation is known as “digitalization”. It differs from digitization, which is the process of converting information into a digital format. Digitalization means using digital technology to collect data, establish trends, automate processes and make better business decisions.

In the scientific laboratory, the application of Industry 4.0 principles – often referred to as Lab 4.0 – has the potential to accelerate innovation by streamlining and automating processes and driving more insightful data analysis. Yet, the life sciences industry is lagging behind in digital transformation, with continued reliance on legacy systems and siloed information. To realize the potential of Lab 4.0, organizations will need to improve their digital maturity – their ability to create value using digital technologies. The critical first step is connecting people, equipment, consumables, systems and data. Here, we explore the benefits of this connectivity, and ways to develop a laboratory fit for the future.

Emerging technologies, such as AI, robotics and objects with built-in sensors and connectivity (collectively known as the Internet of Things or IoT), are increasingly making their way into our homes and workplaces. Although many laboratory environments have some support from IoT and use partial automation for a few processes, such as sample preparation and analysis, the life sciences industry is at an early stage in its digital transformation journey. This is in part because most laboratories house instruments and systems from different vendors, which each rely on proprietary software creating disconnected data siloes. Integrating these systems and their data presents a challenge to most scientific organizations, and is a barrier to effective data management and sharing.

The COVID-19 pandemic highlighted the importance of digitalization in the laboratory for ensuring business continuity and enabling global collaboration. Organizations that were more advanced in their use of digital technologies, perhaps having some form of automation or remote access to experimental data, were better able to cope with disruptions and adapted quickly to new ways of working. During the pandemic, company leaders realized their organizations needed to be more agile and this encouraged embracing digitalization. In a survey of 200 laboratory leaders, 77% said the COVID-19 crisis accelerated their digital transformation plans.¹

The pandemic also illustrated the importance of global data and information sharing while working towards a common goal – such as the rapid development of vaccines. A modern digital infrastructure can support a globally distributed, highly skilled workforce, allowing access to data from anywhere and enabling scientists to conduct data analysis remotely.

Each organization will be at a different stage in its digital transformation journey. Those at the beginning of their journey may have limited digital capabilities and almost entirely manual operations. By contrast, organizations that are digitally mature will be utilizing fully integrated and connected technologies, with maximum automation. In the most digitally mature organizations, AI and machine learning (ML) will be in place to help optimize laboratory processes, from managing supply chains of consumables to maintaining equipment.

There are three key steps to achieving this level of digital maturity:

1. Connect everything

2. Establish end-to-end workflows

3. Implement advanced analytics

By fulfilling this first step of achieving as much connectivity as possible, you will be building foundations that enable your organization to reap the rewards of Lab 4.0.

Successful digitalization within a laboratory requires connecting as many processes, instruments, people, systems and consumables as possible. From this connected laboratory framework, you can build automated end-to-end workflows that accelerate science and improve productivity.

The investment required to achieve connectivity in a global organization with many unique laboratory environments can seem daunting, but the benefits are manifold:

Connected technologies can reduce or even eliminate some of the repetitive, manual work that scientists and technicians need to carry out. If scientists conduct experiments using connected instruments, data can be instantly fed into a laboratory information management system (LIMS). This removes the need for manual data input or transfer, and reduces the risk of transcription errors, improving data quality.

Using standardized and automated processes for data capture allows metadata from connected instruments – such as analysis settings – to be uniformly and consistently included with analytic datasets. This ensures data and metadata conform to the FAIR Principles – a set of sharing standards which, if applied, make data findable, accessible, interoperable and reusable (FAIR). This assures data integrity, and makes data accessible and actionable for everyone in an organization.

Freeing up time spent on manual, repetitive tasks also transforms the role of scientists and laboratory personnel, empowering them to focus on more meaningful and complex work. In a digitalized laboratory, they can quickly and easily engage with connected instruments, share, merge or link data from different sources and seamlessly collaborate with others. This future-fit working environment will help attract highly skilled workers, while motivating existing staff to expand their skillset.

With connected technologies you can access, manage and report data in real-time throughout laboratory workflows, rather than just at the end. This can be particularly important in industries such as biomanufacturing, where every process must comply with good manufacturing practice (GMP). By connecting everything to a LIMS it becomes possible to manage, track and report on samples, tests and test results at every step, from raw material analysis through to finished product, supporting auditing requirements and making it easier to demonstrate compliance.

Connected systems also make it easier to manage samples and consumables by tracking and providing insights on their use. Such systems can monitor and control stock levels of raw materials, in-process and finished products, ensuring supplies are used optimally throughout laboratory workflows and opening up the potential for automatic reordering.

Finally, the improved data quality and integrity achieved through connectivity provides a robust foundation for more advanced analytics. For example, machine- and deep- learning methods can identify previously undetectable trends and deeper insights from your data to support decision-making or workflow optimization. With further use and training, these algorithms can be used to build AI into your workflows to improve laboratory processes, or automatically manage maintenance of instruments or equipment.

Taking this a step further, the most digitally mature laboratories will seek to prioritize connectivity in all environments. For example, extended reality (XR) tools provide scientists with hands-free access to LIMS while conducting experiments, so that – even while working in a cleanroom – a scientist can easily input data or view protocols.

To realize these benefits, laboratories must start with a solid connectivity framework, including a data management infrastructure that supports connectivity across laboratory operations.

In many cases, legacy data infrastructure prevents scientists from benefitting from the insights that a connected and automated laboratory can bring. In a survey of global laboratory leaders, 67% said their efforts to automate their laboratories at scale was hindered by their current laboratory equipment and 71% said their laboratory software was preventing these efforts.¹

To realize the potential of Lab 4.0, you need an effective LIMS to provide the necessary foundations of high quality, high integrity data. It’s essential to have a system that manages the entire workflow and adheres to ALCOA+ principles (Figure 1).

Figure 1: ALCOA+ principles

A state-of-the-art LIMS can provide:

1. Connectivity: By connecting to instrument sensors and eliminating manual data entry via devices such as electronic lab notebooks or advanced tools such as augmented reality (AR), an effective LIMS can support the real-time integrated flow of high-quality data between processes, equipment and people to achieve a truly connected, automated and intelligent laboratory.

2. Compliance and security: Through a secure system with data access control, version control and automated logging of events, a LIMS provides an instant audit trail and regulatory compliant data management – ensuring all raw data and associated metadata is accessible for the entire data lifecycle.

3. Workflow and consumables management: A LIMS is not only a centralized secure location for data. More recent iterations also comprise lab execution capabilities, managing laboratory workflows from sample through to final result, guiding analysts through standard operating procedures to ensure and document compliance, and maintaining supply availability by managing stocks of consumables, reagents and other chemicals.

Combined with a culture that supports digitalization, a LIMS that enables laboratory connectivity is essential for any organization’s digital transformation journey.

The laboratory of the future is a connected, automated environment that enables scientists to quickly engage with equipment, data and collaborators wherever they are. To progress towards digital maturity, and realize the productivity and quality gains promised by Lab 4.0, a critical first step is connectivity. Key to achieving this connectivity is to implement a LIMS that integrates organizational and laboratory systems and instrumentation, regardless of manufacturer or technique, to improve data quality and preserve data integrity. This will allow your organization to benefit from maximum automation, advanced analytical technologies and a highly skilled globally distributed workforce.

References:

1. Forrester. The Lab of The Future Is Here. https://assets.thermofisher.com/TFS-Assets/CMD/Reference-Materials/wp-95975-lab-future-wp95975-en.pdf. Published January 2021. Accessed December 2021.

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