This is Part 2 of our 3-part blog series based on the ARC Advisory Group whitepaper, “Digital Transformation of the Pharmaceutical and Biotechnology Industries.” We’ve added a brief commentary about the 3DEXPERIENCE vision of how data analytics is driving the future of business transformation in these two key industries.
In Part 1, we examined the major challenges facing the pharma and biotech industries as they pursue digital transformation to glean more value from data. Now let’s look at how data analytics is accelerating innovation.
Some Digital Transformation Initiatives
Digitalization, combined with new manufacturing processes, automation, and therapeutics and personalized medicine have the potential to transform the pharmaceutical and biotechnology industries in new and exciting ways.
Digitalization represents a significant opportunity for companies to become more competitive globally. Many companies have already started on their digital transformation journeys with cybersecurity initiatives and new platforms; moving data to the cloud; and adopting new technologies such as additive manufacturing, robotics, analytics, mobility and wearables.
Automated validation testing of scripts and regulatory documents and data are being moved to the cloud. Companies are adopting advanced analytics to help them optimize their processes. While not as widely adopted, augmented reality (AR) and virtual reality (VR) are also helping optimize processes. Similarly, simulation and digital twins are being used to make faster changeovers and to help determine process bottlenecks ahead of time.
[Some content omitted here. For the full white paper, please visit: “Digital Transformation of the Pharmaceutical and Biotechnology Industries”]
Data Visualization and Accessibility
By improving data visualization, accessibility, and collaboration, digital transformation enables companies to improve product quality, empower operators, and adhere better to regulations. How well the information is used to improve the manufacturing process will become critical to the survival of pharmaceutical and biotech companies. Most companies have connected different plants or sites, but not yet embarked on an enterprise-wide initiative for data visibility.
Modern MES solutions are often employed to share data-based manufacturing intelligence across the enterprise in a secure and intuitive manner. The ability to visualize the metrics at a glance enables users to uncover actionable insights quickly. Even though different pharma manufacturers have different products and processes, they often track many of the same key metrics. These typically include:
- Sales and product demand
- Regulatory performance and compliance issues
- Production output and yields
- Downtime and maintenance
- Rejects and scrap
- Material and product costs
Advanced Analytics, AI, and ML
Pharmaceutical companies are adopting a range of advanced analytics. These include descriptive analytics for condition monitoring of machines and production equipment, predictive analytics to determine what will happen, and prescriptive analytics to determine how to fix recurring problems. Some of these advanced analytics are self-service tools for engineers and workers that make it easier to analyze the data. Others integrate AI and machine learning to be self-learning. Some of these tools are being used by, integrated with, or built into other solutions. While analytics are not new, newer technologies make it quicker and easier to analyze real-time data to support timely decisions.
Predictive analytics and machine learning are an important part of the digital transformation for life sciences manufacturers. ARC has spoken with several pharmaceutical companies that are applying predictive analytics solutions for a variety of challenging applications. These range from reducing the time for clean in place to determining optimum bioreactor end points. All have strong value statements resulting in better use of equipment, materials, improved time to market, and reduced manufacturing costs. Other pharmaceutical companies discussed examples of how they are using predictive analytics solutions for maintenance to prevent downtime.
Another class of industrial analytics, marketed as artificial intelligence (AI), gets much of the attention today. This class, a subset of AI, uses algorithms that mimic the human brain to replicate human capability for recognition. Machine learning (ML), natural language processing, and chatbots are examples of this AI subset.
As it ingests large amounts of data, ML can identify discriminative patterns and identify the probability of a behavior occurring. ML techniques can adapt to incorporate new behaviors and data sets without being explicitly told what to look for. However, in most cases in the pharmaceutical industry humans are still involved to make the final determination.
[Excerpt from the ARC Advisory Group whitepaper, “Digital Transformation of the Pharmaceutical and Biotechnology Industries”]
As pharma and biotech enterprises begin to adopt digital transformation initiatives to tackle the challenges we discussed in Part 1, comprehensive alignment across the organization is crucial. To effect this transformation, companies must have a cohesive plan in place that fundamentally includes technology. The solution is a platform which allows individual researchers, teams, and even whole departments to more efficiently manage data. Only when they can access data from across all systems will they be able to gain meaningful insights from analytics.
An all-in-one platform enables researchers to analyze data, collaborate on ideas with colleagues, and monitor projects. The platform also provides the means for data traceability, ensuring data integrity and quality which is, of course, essential for sound and reliable decision making. Visibility across projects and data throughout the organization fosters better collaboration and helps users make the best, data-driven decision at each step in the process. A “single source of truth” for all data ensures confidence in predictions and decisions.
Delivering visualization, accessibility and collaboration is important across the entire enterprise—not only for manufacturing. For example, analytics and collaboration are equally essential in Research & Development with the use of scientific and experimental data. A platform approach enables improved tech transfers between each stage of the product lifecycle, ensuring everyone has access to the right data at the right time, to make better decisions more efficiently.
The adoption of advanced analytics must include semantic analyses as part of the strategy. Intelligent searches include artificial intelligence and machine learning to build relationships in the data and to connect people, processes and information. For any science-based company, the technology used needs to provide the scientific depth and awareness to be able to provide the right data and information on demand. This intelligent search layer must be embedded throughout the IT infrastructure to maximize its impact and value.
In the next blog installment, Part 3, we will look at critical digital transformation initiatives in the pharmaceutical and biotechnology Industries.
Part 3: Critical Digital Transformation Initiatives in the Pharmaceutical & Biotechnology Industries