David Gomez- Associate Director for Data Infrastructure and Intelligence, Life Sciences Manufacturing, Cognizant; Thomas McCarthy- Life Sciences Industry Principal, AVEVA
Harnessing the full potential of data in pharmaceutical manufacturing can be complex for life sciences companies. Challenges include connecting systems, comparing data from sites with different systems, managing the volume, variety, and quality of information, overcoming data silos, and legacy technology. These all exacerbate interoperability, contextualization, and statistics issues, which impede innovation. Scalability and flexibility issues, along with a lack of analytical capabilities, further hinder the translation of insights into action.
Overcoming these obstacles is vital to optimizing data accuracy to feed into artificial intelligence (AI) systems and generate actionable insights that enhance productivity and efficiency, accelerate the commercialization of new products, improve the patient experience, and empower the digital transformation roadmap. However, delaying investment in digital transformation can complicate matters, creating a “chasm” between traditional and new data infrastructure that can become harder and more expensive to bridge the longer the action is delayed.
SaaS and cloud computing solutions can help life sciences companies bridge this data, infrastructure, and intelligence (DII) gap. DII collectively refers to the raw facts and figures generated during life sciences manufacturing, the foundational technological systems that process and store them, and the actionable insights derived from their analysis. A SaaS or cloud-based approach to DII involves delivering and accessing advanced software applications and underlying resources over the internet without requiring on-premises hardware or software installation.
This article explores real-world examples of how these partnerships, combined with cutting-edge cloud computing technology, are helping life sciences companies break down barriers to utilizing their manufacturing data..
From Insights to Actions
Proactively updating digital infrastructure in the life sciences sector delivers value for manufacturing operations, far outweighing the perceived implementation complexities. By embracing new technologies and doing so sooner rather than later, companies can unlock added value across a range of critical areas, including:
- Enhanced business and manufacturing reporting: Gain deeper insights into operations, enabling better decision-making and improved overall performance.
- Streamlined process monitoring: Implement real-time monitoring of manufacturing processes to identify and address inefficiencies promptly.
- Advanced process analytics: Leverage data analytics to optimize processes, improve product quality, and reduce costs.
- Continuous business process improvement: Foster a culture of continuous improvement by using data-driven insights to refine workflows and enhance productivity.
SaaS and cloud solutions offer a compelling pathway for life sciences companies to modernize their infrastructure quickly and in a resource-efficient manner, overcoming the challenges associated with updating legacy systems. The benefits include:
- Reduced investment in on-premise infrastructure: Companies can minimize capital expenditure by leveraging cloud-based solutions, eliminating the need for costly hardware and maintenance.
- Accelerated implementation timelines: New infrastructure can be deployed more rapidly, enabling faster time-to-value and quicker realization of benefits.
- Simplified scalability, flexibility, and updates: Organizations can easily scale resources up or down as needed, adapt to changing business requirements, and benefit from seamless updates and upgrades.
- Facilitated continuous improvement: Processes can be streamlined, and ongoing optimization can be enabled through readily available data and analytics. Moving away from the heavy lift of maintaining legacy on-premises platforms frees up internal resources. With infrastructure maintenance and upgrades shifted to the cloud, teams can redirect focus toward innovation, process optimization, and delivering greater value to patients.
SaaS and cloud solutions designed specifically to address the DII challenges within life sciences manufacturing offer a powerful approach to digital transformation. This technology is becoming further tailored to the needs of the pharmaceutical sector, thanks to increasing collaboration between digital transformation partners with different technical capabilities. The resulting SaaS solutions are uniquely designed to meet the changing needs of the sector and are designed to overcome the challenges posed by years of legacy infrastructure.
The type of offering resulting from this kind of collaboration assists life sciences companies in navigating their digital evolution by establishing a resilient data infrastructure within the cloud, seamlessly integrating information from various sources, even older, well-established systems.
By leveraging such SaaS solutions, life sciences organizations can effectively harness the full potential of their manufacturing data. This leads to significant enhancements in productivity, accelerates the pace of innovation, and ultimately contributes to improved patient outcomes.
SaaS Benefits in Action
To illustrate the transformative power of SaaS and cloud computing in the life sciences sector, let’s explore compelling case studies. These examples highlight how companies are leveraging these technologies to unlock new value from their data and empower their teams.
Case Study 1: Bringing a Data Historian Model Strategy into the Cloud
One significant challenge for life science companies is leveraging the wealth of historical data residing in their on-premise historian systems to generate new insights in the cloud. Concerns often arise about the difficulty of migrating this data and whether the substantial investment made in these on-premise systems can be effectively leveraged in a cloud environment. However, by harnessing the power of SaaS and cloud computing, the acquisition of this valuable historical data can be achieved rapidly. Existing data models can be efficiently transferred to the cloud, unlocking a newfound ability to apply advanced analytics to continuous data streams. This cloud-based environment also facilitates seamless connection to a wide array of powerful data analytical tools, enabling tangible benefits such as enhanced analytical capabilities, improved reporting accuracy, and the development of sophisticated predictive models that were previously difficult or impossible to implement on-premise.
Case Study 2: Empowering Data Science and MSAT
Another critical challenge in life sciences manufacturing involves enabling data science and Manufacturing Science and Technology (MSAT) teams to proactively analyze and optimize production processes. Specifically, businesses often struggle to run sophisticated forecast models to analyze critical factors like asset performance degradation, production yield variability, and the prediction of maintenance needs. SaaS and cloud computing solutions are proving instrumental in helping companies harness their Operational Technology (OT) historian data and contextualize it within a broader data landscape. This empowers MSAT teams to conduct in-depth analyses for operational and product improvements with unprecedented efficiency. The significant benefit of the low upfront IT investment costs of a cloud-based approach makes advanced analytical capabilities more accessible to these crucial teams.
Case Study 3: Automating Testing and Validation
A significant hurdle for life sciences manufacturers lies in establishing agile release processes for critical components such as AVEVA™ PI System™, its AF (Asset Framework) data models, and AVEVA™ PI Vision™ displays, which are fundamental for visualizing and analyzing manufacturing data. The challenge lies in ensuring the integrity and validity of these components through rigorous testing while maintaining the agility required for continuous improvement and deployment.
SaaS and cloud computing solutions are providing a pathway to overcome these challenges by enabling the automation of testing and validation processes. By leveraging cloud-based infrastructure and specialized testing tools accessible through a SaaS model, companies can build agile release pipelines for both AF data models and PI Vision displays. This automation ensures that any changes or updates to these critical systems undergo standardized and repeatable testing procedures before deployment, significantly reducing the risk of errors and ensuring compliance. The inherent scalability and flexibility of the cloud environment allow for the rapid provisioning of testing environments and the efficient management of the testing lifecycle, fostering a more agile and reliable release process.
Expert Collaboration is Key to Successful SaaS DII Transformation
Working with digital transformation partners experienced in life sciences manufacturing offers significant advantages, streamlining the journey to a data-driven future. These partnerships provide:
- Deep expertise and industry-specific experience: These partners possess in-depth knowledge of the life sciences industry, understanding the intricacies of regulatory compliance, data security, and the specific challenges related to DII within this sector. They bring a wealth of experience in implementing successful digital transformation projects in similar environments. This expertise translates to a more efficient and effective transformation process, minimizing risks and maximizing returns. For example, they understand the nuances of GxP compliance and can ensure that cloud solutions meet stringent regulatory requirements. Furthermore, the most effective partners often collaborate with peer digital transformation companies possessing complementary expertise in the life sciences, combining their unique resources to deliver even more tailored and robust SaaS solutions.
- Accelerated transformation and faster time-to-value: Leveraging the experience and proven methodologies of a specialized partner can significantly expedite the digital transformation process. Instead of navigating the complexities of implementation alone, life sciences companies can benefit from established frameworks and best practices. This reduces the time-to-value, allowing them to realize the benefits of their new infrastructure sooner. For instance, a partner can help streamline the migration of legacy systems to the cloud, ensuring minimal disruption to operations. This efficiency is often amplified when these partners work in concert with other experienced digital transformation firms, leveraging combined knowledge to tackle complex integration challenges more effectively.
- Innovation and adoption of best practices: Digital transformation partners are at the forefront of technological advancements and industry best practices. They bring insights into the latest cloud computing solutions, data analytics tools, and AI/machine learning (ML) applications relevant to life sciences manufacturing. This ensures that companies adopt future-proof solutions, positioning themselves for long-term success and competitiveness. They can also help identify and implement innovative approaches to data management, process optimization, and quality control. The benefit is often compounded when these partners collaborate with other leading digital transformation companies in the life sciences space, fostering a broader ecosystem of innovation and shared best practices that ultimately benefit the client.
A Silver Lining for Overcoming DII Obstacles
The future of life sciences manufacturing is inextricably linked to the effective management and utilization of DII. As the industry faces increasing complexity, driven by factors such as personalized medicine, evolving regulatory landscapes, and the need for accelerated innovation, the value of robust DII becomes even more pronounced.
Effective DII is crucial for meeting future manufacturing challenges. It empowers life sciences companies to optimize processes, enhance product quality, accelerate commercialization, and ultimately improve patient outcomes. By leveraging advanced analytics, AI/ML, and cloud-based solutions, manufacturers can gain deeper insights into their operations, enabling data-driven decision-making and fostering a culture of continuous improvement.
The time to prioritize digital transformation is now. Companies that proactively address their DII challenges will be best positioned to thrive in the evolving life sciences landscape. Embracing cloud solutions and partnering with experienced digital transformation providers offers a clear path to overcoming legacy infrastructure limitations and unlocking the full potential of manufacturing data. Importantly, the shift also liberates resources currently consumed by the upkeep of legacy platforms. By embracing cloud-first strategies, life sciences companies can channel those resources into what matters most: advancing therapies, accelerating innovation, and improving patient outcomes. This proactive approach will not only enhance current operations but also lay a strong foundation for future growth and innovation.
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