Graph technology: the remedy for broken supply chains is connected data 

The Covid-19 pandemic has exposed the fragility of global supply chains. Many pharma companies do not have a transparent end-to-end view of their supply chain. They lack the agility to flex and adapt to rapid change. Graph database technology that can record complex data interdependencies can help address these issues right now, argues director of AI graph analytics at Neo4j, Amy Holder. 

Globalised supply chains have proven to be only as strong as their weakest link. Huge variations in supply and demand have stressed supply chains to breaking point in many sectors over the past few weeks. Fashion is at a standstill, but the cosmetics market is still open for business; grocery retail spending is high, but restaurants are closed. Pharma companies have had to switch their product lines overnight to meet demand for completely new medicinal products and devices to treat patients with coronavirus.

The supply chain has simply not been flexible enough to handle these changes. 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”.

Manufacturers, distributors and logistics 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 organisations need a highly scalable way to manage the vast volumes of serial numbers, supplier and facility details, certifications, documents and detailed questionnaires they will need to get on top of the crisis and re-start their businesses.

SQL is falling short

There is a pressing need to build stronger, scalable and more flexible supply chains. To achieve this, pharma 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 and maintaining standards of sustainability and social responsibility.

In an ideal world, supply chains would be a linear chain of single suppliers. 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.

As data and processes become more interdependent, there is increased potential to gain data-driven insights – and at the same time an 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, analysing and visualising the supply chain. These connections also need to be quick and easy to search and scalable to the size of the 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 recognise how a production line or particular pallets and their contents are connected will be next to impossible.

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 scrutinised and could place manufacturers’ entire operations in a perilous position.

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.

Mapping the reality of interconnected supply chains  

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 complex data interdependencies.

Using graph tech, manufacturers can typically demonstrate 100 times faster query response speeds than those enabled by SQL RDBMS software. This sort of response time is critical during the present crisis and will be crucial going forward in a highly digitised, 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 analysing 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 analysing the relationships between a large number of data points. Such a connected relationship-centric approach allows businesses to better manage, read and visualise the data in lengthy and complex supply chains.

Graph technology goes far beyond simply digitising 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.

Preparing for future supply shocks

It is essential to start work now on putting the right technology in place to provide insights from existing data that will 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.

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 realise 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. Graph technology can support companies who are struggling with their stressed supply chains to gain actionable insight right now.

About the author

Amy Hodler is director of AI graph analytics at the Graph Database company, Neo4j.

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