5 Predictions for Life Sciences Manufacturing in 2024

The life sciences sector is at the cusp of a transformational step change, propelled by advancements in technology and the urgent need for innovation amidst the shifting regulatory landscape. In our recent webinar, "Frontline Excellence: Life Sciences Manufacturing Forecasts for 2024," we dove deep into the trends poised to redefine the industry's future. This period of transition underscores a critical juncture for life sciences manufacturers, and those that embrace change and adopt new technologies and mindsets are in a position to drive significant operational improvements.

As we head into 2024, here are five key predictions on how the life sciences sector will evolve. These underscore the integration of new technologies, the value of data-driven insights, and the need for a human-centric approach — signaling the need to move towards more agile, resilient operations. Now is the time for organizations to look ahead and plan how they want to allocate their resources in light of these changing conditions.

The industry is increasingly moving away from the cumbersome implementations of traditional, monolithic systems to instead embrace agile, composable platforms. These platforms provide users with building blocks which can be fully customized to create solutions that can be deployed and iterated on rapidly — driving a much faster time-to-value. This approach also significantly lowers the bar for entry, and allows those closest to operations to solve problems and capture compliant data.

The shift towards these composable systems that can adapt to the specifics of any production process are only set to accelerate this year. According to Gartner, “by 2025, at least 25% of manufacturing operations applications will use a composable technology architecture, up from less than 2% in 2022.” This represents a significant shift, where new solutions can be developed, validated, and deployed at scale faster than ever before.

The adoption of the Industrial Internet of Things (IIoT) technology in life sciences is expected to reach a critical mass, serving to make data an invaluable asset in driving quality, compliance, and productivity for the organizations that embrace it. IIoT facilitates holistic visibility over all of your operations by enabling both machines and humans to provide compliant data.

In practice, this can take the form of electronic logbooks on the shop floor that allow anyone in the organization to have real-time visibility into equipment statuses. Compliance data for batch records or device history records can also be captured electronically throughout the production process, enabling you to significantly improve traceability and eliminate cumbersome paper records. Even something as seemingly straightforward like work instructions for procedures like line clearance can be transformed by integrating with data from machines to error-proof the work your operators are doing. Information from other enterprise systems like a QMS or ERP can also be integrated to help eliminate data silos and make sure that people can get the data they need, when they need it.

Much of life sciences is inherently focused on the well-being of humans, but oftentimes this has only included the patient or end user. Now, the industry is shifting to extend this thinking to the frontline operators working on the shop floor or in the lab.

The evolution toward human-centric operational technology is embodied by the adoption of no-code platforms that enable the concept of citizen development. This means that process experts, who may not have a technical background in IT, are empowered to build solutions that are tailored from the ground up to the unique challenges of their particular production process.

This approach also allows you to build, test, and validate these solutions much faster in response to operator feedback. The result is a much more intuitive and engaging user experience for the operators compared to the interfaces of traditional, monolithic systems.

Not only does this enhance operator engagement at a time when resources are scarce — with an estimated 2.1 million American manufacturing jobs expected to go unfilled by 2030 according to Deloitte and the National Manufacturing Institute — but it leads to improvements in productivity, quality, and compliance.

The industry is moving from traditional on-prem historians that have long served as the backbone for capturing operational data to a cloud-based approach that enables greater contextualization of data. This transition — from being data-rich but information-poor to leveraging data for actionable insights — is critical to driving further advancements in quality and productivity.

Historian systems, while effective in amassing data, often fall short in providing the actionable insights needed for rapid decision-making and innovation. Instead, organizations must focus on capturing a wider array of data, particularly data from frontline operators, which has been historically underutilized — if it was even captured at all.

As media like photos and videos are becoming as ubiquitous as other data types, they too offer new possibilities for solving quality inspection and compliance challenges. Regulatory guidance will soon be released around these new types of data to provide guidelines for their use, and remove barriers for life sciences to get the same value from this data that other industries are already able to extract.

By combining context from operators, equipment, and other data sources at the moment it is captured, organizations can transform selective, siloed data sets into a real-time stream of enriched, actionable information.

Moving beyond the initial hype surrounding innovations like ChatGPT, the life sciences industry is moving towards applying generative AI tools to use cases that bring real value to operations. Regulatory bodies across the world are actively preparing guidance for the use of generative AI tools in production environments. The FDA's Quality Management Maturity and Emerging Technology Programs in the United States, alongside the European EMA’s Quality Innovation Group are in the process of developing frameworks to ensure that generative AI deployments align with other regulatory standards.

This technology promises to enable a number of valuable use cases, including assisting with audit preparation, deviation investigation, and root-cause analysis. It can also be a tool to democratize data analysis and empower those who aren’t trained data scientists to generate insights that help solve problems in their operations. Much of the industry is already looking to put these technologies into use in 2024, with ZS finding that 92% of life sciences leaders expected their companies to invest in generative AI capabilities over the next 12 months.

This year stands to be pivotal for the manufacturing industry, bringing with it numerous challenges but equally as many opportunities to innovate. Where should life sciences manufacturers get started? Here are three practical ways to capitalize on the upcoming shifts in the industry.

Transition to a Composable Approach

The move away from the one-size-fits-all model of traditional production systems towards a more composable and adaptable framework calls for solutions that are not just broadly flexible but that can be entirely customized to individual manufacturing processes.

Embracing a composable approach means creating an environment in which changes can be made rapidly and new systems can be easily integrated to meet shifting production demands, allowing for improved efficiency and agility.

Prioritize Your Most Important Asset: Your People

Without a doubt, the most valuable asset any organization has are the people working on the frontlines of its operation day-in and day-out. Any decision on which technologies to adopt or what changes to make in your operations in 2024 should be done with them in mind first and foremost.

When it comes to your operators, this means focusing on reducing monotonous tasks and empowering them to use their intimate knowledge of your process to solve problems and innovate. For engineers, this involves cultivating an environment where they can fully utilize their skills along with modern technologies to continuously improve the production process. Especially given the labor shortages the industry is facing, taking steps like these is critical to attracting and retaining the next generation of talented employees.

Adopt the Pharma 4.0 Baseline Guide for Quality and Compliance

The ISPE’s Pharma 4.0 Baseline Guide is instrumental for life sciences organizations navigating digital transformation, offering a detailed framework that balances technology adoption with regulatory compliance and quality management. This guide presents a structured model for operational excellence, highlighting the necessity of aligning digital initiatives with human-centric strategies.

Key to this model is the recognition of technology as only one component of a broader ecosystem that also prioritizes human resources and organizational culture. The guide advocates for leveraging digital technologies not just for automation, but as tools to facilitate decision-making, improve product quality, and streamline compliance processes through Quality by Design (QbD) principles.

This approach is supported by regulatory bodies, which encourage the adoption of digital solutions to achieve these goals more effectively and efficiently. Its adoption is essential for organizations seeking to digitally transform their operations and stay ahead in this shifting economic and regulatory environment.