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Can Graph Technology Make the Supply Chain Properly Transparent?

Graph technology could be crucial for achieving the detail needed, says Emil Eifrem

​Today’s supply chains are vast and wide-ranging, which makes them incredibly useful – they’re the main lubricant of global capitalism, after all.

​By Emil Eifrem, CEO of Neo4j

Emil Eifrem, CEO of Neo4j

But – they are also fertile ground for vulnerability like fraud, contamination, insecure production sites and unknown product sources. These are all factors that make transparency more vital and more complex. For example, a mere six years on from the horse meat contamination scandal that shook the UK, a recent investigation discovered that a shocking one in five supermarket meat products still contain cheaper cuts from different animals, contrary to their official labelling.

This demonstrates the need for manufacturers to have precise information about the products being used to create the finished product, as well as on all participants in the supply chain. In fact, most brands only know their direct suppliers, leaving them with poor visibility into the wider network of their primary partners.

Let’s consider the deeply regulated pharma industry. In this intensely scrutinised industry, pharmaceutical companies must be able to identify where any individual medicine item is in the supply chain in the event a safety issue or recall is flagged. In such a scenario, it is imperative that items can be quickly removed from the market to minimise the risk to consumers and the cost of redress.

​Regulators also demand that individual medicine products are clearly verifiable as authentic. As of February this year, the EU’s Falsified Medicines Directive specifies that medicinal products must carry a unique product identifier code, and that manufacturers and distributors must demonstrate detailed record-keeping, while all products must be logged via a central database of drugs sold in EU countries.

​There are also social issues in the supply chain discussion. Increasingly, consumers want ethically produced goods – they want products to be sourced and manufactured sustainably and to understand their provenance so they can make informed choices. Supply chain transparency helps pharma businesses meets corporate social responsibility requirements, and with greater visibility into their supply chains, businesses can ensure their practices respect animal rights in drug testing scenarios, for instance.

How a relationship-centric approach could help

​So supply chain visibility is a problem. How do we address it? What seems to be required here is for manufacturers and brand owners to be able to share detailed information about all products, suppliers and facilities in their common ecosystem. Plus, companies need to be able to search for every product affected by specific raw materials or facilities issues, across thousands of products with no performance problems.

​The technical challenge of meeting these targets can be onerous. With hundreds of thousands of product lines produced across multiple sites and sold into hundreds of markets, keeping track of every stock unit exceeds the scope of the standard way businesses have to organise data, namely using relational database systems – think Oracle or Microsoft SQL Server. The numbers of unique serial codes alone can run into billions, and CIOs need a highly scalable way to manage the vast volumes of serial numbers.

​The problem is that if the data is stored on SQL-based database technology, a simple and fast navigation through all the data in order to recognise how a production line or particular pallets and their contents are connected proves impossible. And with increasing connectivity and a move to the Internet of Things, this complexity is unlikely to decrease.

Relational databases, which store information (product, pallet, production site, serial number etc.) in rows and columns, are poorly-equipped for identifying relationships within datasets. It’s these same connections, however, that are essential for identifying a specific product’s whereabouts, or to monitor, analyse and search the supply chain, and to share significant data about production sites and products.

​Making traditional databases work in real time is also problematic, with performance suffering as the dataset size grows. The good news is that a software called graph database technology is emerging as a solution, because of its ability to record complex data interdependencies. The idea is that when you track something, you create a hierarchy or ‘tree’ of data: if you scan the code of a particular pallet, it will automatically recall its contents.

Supply Chain Graph

​Graphs therefore offer a tremendous advantage over traditional relational databases when it comes to mapping complex, inter-connected supply chains, maintaining high performance even with vast volumes of data as they do. And instead of using relational tables, graph databases use structures better at analysing interconnections in data, and they also adopt a notational formalism closely aligned with the way humans think about information. Once the data model is coded, a graph database is highly efficient at analysing the relationships between a large number of data points.

​This kind of relationship-centric approach enables the manufacturer to better manage, read and visualise their data, giving them a truly trackable and in-depth picture of all products, suppliers and facilities and the relationships between them. Using a graph database, manufacturers can typically demonstrate 100 times faster query response speeds than that enabled by SQL RDBMS software.

As Chris Morrison, Chief Executive Officer of one of our graph customers, Transparency-One, which matches together consumer products and supply chain expertise with cutting edge technology, notes, “We tested graph with dummy data for several thousand products, and there were no performance issues. As for the search response time, we didn’t have to worry about taking special measures, since we got back results within seconds, something that we would not have been able to calculate without this solution.”

​That sort of response time is critical when you need to provide second or sub-second responses or to identify a specific product’s location. It’s also going to be critical in helping you comply with the latest global regulations of traceability and to manage that time-critical and reputation-critical product recall effectively.

​So graph database technology is a great enabler for any manufacturer or supply chain stakeholder today that needs to adequately tackle complicated, interconnected and real modern-day supply chains.

​The author is CEO and Co-Founder of Neo4j, the world’s leading graph database company

Process Industry Informer

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