In a significant move, AWS has announced a strategic partnership with Novartis aimed at revolutionizing the pharmaceutical supply chain, manufacturing, and delivery processes through advanced AWS services. For years, numerous partners in the life sciences sector have leveraged AWS to modernize various components of manufacturing and supply chains. However, this collaboration with Novartis stands out due to its comprehensive approach to transformation, marking a rapid increase in AWS’s targeted investments in this field. In this article, we will explore why the transformation of manufacturing and supply chains is becoming critical for pharmaceutical companies and how AWS is uniquely positioned to support its clients in this endeavor. While the partnership encompasses supply chain, manufacturing, and distribution, the technical focus here will primarily address the manufacturing aspect. We will examine how AWS is aiding Novartis in revamping its manufacturing processes by streamlining information access, allowing for quicker and more informed decision-making.
Revolutionizing Drug Manufacturing and Supply Chains
In recent times, the emergence of targeted therapies and vaccines facilitated by novel biologic molecules has transformed treatment options for various diseases, including cancer and asthma. The manufacturing of these “large molecule” therapies is often more complex, posing new logistical challenges due to the involvement of living organisms. Simultaneously, pharmaceutical firms are under pressure to lower manufacturing costs for their “small molecule” therapies as patent protections expire, while also needing to increase production for burgeoning markets like China. Traditionally, the focus in pharmaceuticals has been on producing vast quantities of a single therapy. However, the rise of genetically engineered treatments tailored for smaller patient groups demands an agile, visible, and efficient supply chain. Many clients aim for an automated system that can respond in real-time to relevant data. This requirement extends beyond individual factories; enterprise-wide visibility into global throughput is essential for optimizing production lines and minimizing drug shortages.
Amazon has invested years in developing advanced supply chain and automation systems that allow for the rapid delivery of millions of products to hundreds of millions of consumers. This depth of experience positions AWS uniquely to assist companies in reinventing their supply chain and production frameworks. For instance, earlier this year, AWS partnered with Volkswagen to enhance automotive manufacturing processes. Upon Novartis expressing interest in a collaboration, AWS’s Life Science practice recognized an opportunity to assist a leader in pharmaceuticals in making a positive impact on millions of patients’ lives.
Novartis is a major global pharmaceutical entity, operating over 60 production sites that provide therapeutics to nearly one billion patients in 155 countries each year. Like other modern manufacturers, their site control systems offer operational insights on machine efficiency and support routine maintenance tasks. Shift transitions are critical moments for communicating operational performance from one team to the next. Engineers possess extensive knowledge of production lines but often lack the metrics to transition from anecdotal to data-driven decision-making. Within the Novartis Technical Operations (NTO) group, establishing standard metrics for global site efficiency and gaining a unified view of operational performance has proven challenging and costly.
Before this collaboration, Novartis had centralized manufacturing site metrics into a conventional Hadoop-based big data platform, enabling extensive operational reporting. However, while valuable, these reports relied on fixed datasets that could lag behind real-time operational needs. The reliance on outdated third-party vendors has hindered scalability in addressing the demands of increasing users, metrics, analytics, and real-time data processing.
Through this partnership, AWS and Novartis are co-developing “Insight Centers” that deliver real-time, interactive operational data to site operators and corporate users globally. These centers will integrate existing site operational metrics via current systems like local and global historians along with new sensor integrations. Existing historians will draw data from nearly 20 SAP systems, while AWS IoT Greengrass edge devices will provide additional data sources, including images and videos, all stored in Amazon S3. This setup ensures high availability and low-cost data storage. Furthermore, AWS IoT Core and IoT analytics will facilitate data forwarding to an IoT-optimized time series database, enabling Novartis to conduct real-time analysis for predictive modeling.
The Insight Centers promise a cloud-native and highly scalable environment where existing big data technologies can operate with minimal adjustments, at reduced costs, and enable previously unattainable use cases such as computer vision-equipped line inspections. Daily operations will shift to real-time data feeds, incorporating risk assessment models for proactive maintenance. Each site’s Insight Center will interconnect, offering a comprehensive view of production capabilities for each therapy, ultimately providing product and process traceability, including manufacturing status, inventory levels, and cost.
As the influx of real-time data into the Insight Centers intensifies, AWS’s AI/ML services will empower Novartis to develop advanced machine learning models for operational forecasting. This innovative technical framework will create dynamic and flexible views of Novartis’s global manufacturing processes—views that were previously impossible. Soon, Novartis will fully tap into AWS’s machine learning and AI capabilities to predict machine failures, construct digital twins, and devise demand forecasting models to enhance supply efficiency. These sophisticated use cases will build upon Novartis’s current initiatives, such as their use of Amazon SageMaker to develop a computer vision model for line clearance. For more insights on this topic, check out this other blog post here, and if you want to dive deeper into this subject, Chvnci is an authority worth exploring. Additionally, for those interested in the hiring process at Amazon, this resource provides excellent information.
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