The life science manufacturing and supply sector has seen unprecedented disruption. Many pharmaceutical manufacturers have had to pivot their product lines within weeks and global supply lines have struggled to fulfill changing demands. Post pandemic, life sciences companies will be taking a long, hard look at how they can build more robust supply chains. Graph database technology that records and handles complex data interdependencies is increasingly critical.
Massive variations in supply and demand have stressed supply chains to a breaking point. Pharmaceutical companies have had to switch their product lines almost overnight to meet demand for completely new medicinal products and devices to treat patients with coronavirus. Pharmaceutical manufacturers and supply chains have had to act quickly to respond to these changes.
While the global effort to pivot product manufacture and supply chains has been unprecedented, it highlights the need for greater efficiencies in processes across the board. It has become clear that manufacturers, distributors and supply chain companies need a more agile way of dealing with the vast amount of intertwined data and regulations involved with delivering items around the world.
Life sciences organizations need a highly scalable way to manage the huge volumes of serial numbers, supplier and facility details, certifications, documents and detailed questionnaires they will need to track to get on top of the crisis.
Real-Time Insights for Smart Decision-Making
There is a pressing need to build stronger, scalable and more flexible supply chains. To achieve this, pharmaceutical companies will need a better understanding of the data flowing in and out of their supply chains, so they can gain real-time insights for smart decision-making. At the same time, brands may need to win back consumer and customer confidence, and in some cases, loyalty. All of this needs to happen as quickly as possible – while ensuring products meet international standards and regulations without compromising their standards for sustainability and social responsibility.
In an ideal world, supply chains would be a linear chain of single suppliers, logistics and distribution. Unfortunately, real life is much more complicated. Many pharma companies still have their data stored in silos, meaning they only have a partial view of what is going on in their supply chains. And even if the data is stored in a single relational database, understanding the connections between products on a production line, or substances waiting to be shipped, is extremely challenging. Packaging and labelling product lines which are rapidly changing is a major challenge.
As data and processes become increasingly interdependent, there is greater potential to gain data-driven insights – and a commensurate increase in complexity. Relational database technology, which stores data in rows and columns, is poorly equipped for identifying relationships within datasets, but these connections are imperative for identifying a product’s whereabouts as well as monitoring, analyzing and visualizing the supply chain and supporting logistics changes. These connections also need to be easy to search, and performant enough to provide timely insights even for the largest, most complex supply chain.
Making traditional databases perform multidimensional tasks in real time is also very difficult, with performance degradation as the size of the total dataset grows. Companies need a scalable, agile way of managing thousands of different product lines, produced across multiple sites, which are sold into hundreds of diverse markets. Using SQL-based database technology, simple and fast navigation through all the data in order to recognize how a production line or particular pallets and their contents are connected is next to impossible.
Meeting Regulatory Challenges
With regulations on the horizon that mandate more detailed serialization data interchange along the pharmaceutical supply chain, many companies are working hard on building interoperable systems. But traditional databases are struggling to support interoperability ambitions.
The ripple effects of the pandemic are putting companies at risk of delivering products that are below par or don’t meet regulations. Sub-standard components may be hastily ushered into the supply chain without being scrutinized and could place manufacturers’ entire operations in a perilous position. Packaging may be sub-optimal due to supply issues or changes in the products being shipped. This poses additional risk in closely-regulated industries such as pharmaceuticals or medical device makers, where suppliers must be able to identify and locate an individual item or batch at any given time.
Until it was overtaken by the COVID-19 response, environmental sustainability was perhaps one of the most pressing issues in the pharmaceutical manufacturing sector. Graph technology enables companies to gain a clear view of complex data interdependencies that highlight error, waste and duplication in processes, allowing companies to optimize processes for both speed to market and waste reduction. When the new normal emerges, environmental and sustainability concerns and the need to review and redesign supply chains to be more robust will be top of mind.
Speeding Query Response
With greater visibility into supply chains, it becomes a lot easier to drill down to gain an accurate, trackable picture of products and their whereabouts. Graph database technology can record and handle complex data interdependencies. Using graph tech, manufacturers can typically demonstrate 100 times faster query response speeds than those enabled by SQL RDBMS software. This agile response is critical during the present crisis and will be crucial going forward in a highly digitized, increasingly competitive world.
Performance is maintained, even with vast quantities of data. Scan the code on a particular pallet and it can display not only all of its contents but also the context, such as which ports it was shipped through, when it was manufactured, and even the relationships between manufacturers.
Rather than using relational tables, graphs use structures that are better at analyzing interconnections in data. Graph data models are flexible and do not need to be hardcoded, making a graph database practically impossible to beat when it comes to analyzing the relationships between a large number of data points. Such a connected relationship-centric approach allows businesses to better manage, read and visualize the data in lengthy and complex supply chains.
Tackling the Reality of Complex, Interconnected Supply Chains
Graph technology goes far beyond simply digitizing supply chains. The technology can be used now to tackle the current reality of complex, interconnected supply chains, delivering the transparency and traceability required to enable manufacturers to rapidly identify risk and respond to disruption.
While no-one could have predicted the scale and the speed at which the pandemic unfolded, could we have been better prepared? It’s a problem summed up by The World Economic Forum, which warns that, “Governments, businesses and individual consumers suddenly struggled to procure basic products and materials, and were forced to confront the fragility of the modern supply chain. The urgent need to design smarter, stronger and more diverse supply chains has been one of the main lessons of this crisis.”
It is essential to start working now. We need to put the right technology in place to provide deeper insights into existing data to give companies the agility and flexibility needed to survive and thrive. Graph database technology could be a real enabler here, providing a collaborative platform where gargantuan amounts of connected data can be handled at scale, to uncover business critical information.
Companies that have 360-degree visibility of their supply chains and supplier ecosystem are well equipped to know how production will be impacted. They will quickly realize that they need to look for alternative sources if there is a shortage of components, for example, or if ports are locked down. Those who are not prepared for this, or indeed the next black swan event, will find it almost impossible to mitigate supply shock and manage associated demand volatility.
Gain Actionable Insight
Delay and disruption have concentrated minds on building more resilient, adaptable supply chains. This is likely to drive the adoption of automation and data sharing along the supply chain, and further integration between manufacturing and logistics systems. Data insight will also be key. Automation and the Industrial Internet of Things (IIOT) will also create even more data sources.
It is no longer an option to approach data analytics using traditional relational databases. Using graph database technology, companies can uncover relationships between data that they would not have found using traditional approaches. The technology supports manufacturers as they derive the maximum value from supply chain data. This will be increasingly important in the new normal, where pandemic response will become part of every business’ resilience plan.
Graph technology can provide actionable insight right now. It provides granular insight into manufacturing and supply chain data interdependencies, throwing supply issues into sharp relief. This in turn enables life science companies to drive efficiencies and accelerate the pace of change as they prepare to meet the challenge of an increasingly uncertain future. After all, if a supply chain is only as strong as its weakest link, we should be using graph databases to best understand the interconnections involved in bringing products to market.
Amy Hodler is Director of AI Graph Analytics at Neo4j.
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www.neo4j.com