Graphing the supply chain: Image credit - by Tumisu from PixabayToday’s supply chains are vast and wide-ranging, which makes them incredibly useful and the main lubricant of global capitalism. The inherent complexity of any global supply chain, which crosses oceans, borders, and cultures also makes it easily compromised and difficult to control: and complex supply chains are highly vulnerable to fraud, contamination, insecure production sites and unknown product sources.

These are all factors that make transparency vital. There are also social issues in the supply chain discussion. Consumers are increasingly demanding ethical goods – products sourced and manufactured sustainably and from businesses that can ensure their practices respect labour rights in factories, for instance. This kind of supply chain transparency also meets corporate social responsibility requirements.

The consequences of getting it wrong can be huge: a product recall is potentially the most risk-laden situation a company can face. This this risk is increasing, as supply chains grow more complex and the regulatory landscape becomes more robust. According to Allianz Global Corporate & Specialty (AGCS), which looked at product recall claims across 12 sectors, the average costs of a recall can exceed €1.4m ($1.65m). This can rise to over €12m ($14.5m) and almost €8m ($9.42m) for significant claims in the two most impacted sectors, automotive and food and beverage. The loss totals from individual events can far surpass these figures.

Precise information on components key to visibility

This underlines the need for precise information about the components used to manufacture the finished product, as well as visibility into all participants in the supply chain. The biggest challenge to ensuring secure and responsible sourcing of raw materials and inputs is getting an accurate picture of where they originate. Most brands only know their direct suppliers. This leaves them with poor visibility into the wider network of their primary partners.

The more steps there are in the chain between the various suppliers, manufacturers, retailers and consumers, the greater the risk of fraud, contamination, and unethical practices. Every link adds another layer of complexity and is a potential opportunity for wrongdoing and a lack of accountability.

The $64,000 question is: “how can supply chain visibility be ensured?” Manufacturers and brand owners need to share detailed information about all products, suppliers and facilities in a common ecosystem. Companies also need the ability 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 managing these requirements is far from trivial. Companies produce hundreds of thousands of product lines across multiple sites and sell into hundreds of markets. This means that keeping track of every stock unit pushes the boundary of the standard way businesses have to organise data, namely using relational database systems (Oracle or SQL Server).

Storing the data on a SQL-based database technology means that simple and fast navigation through all the data, in order to recognise how a production line or particular pallets and their contents are connected, is impossible. With increasing connectivity and a move to the Internet of Things, this complexity increases exponentially. Note that the numbers of unique serial codes alone can run into billions.

100 times faster query responses

Relational databases, which store information in rows and columns, are also poor at identifying relationships within datasets. These connections are essential for identifying a product’s whereabouts. They also make monitoring, analysing and searching the supply chain, to share data about production sites and products, possible. Making traditional databases highly performant in real time is also problematic with performance suffering as the dataset size grows.

CIOs need a highly scalable way to manage the vast volumes of serial numbers. Graph database technology is emerging as a possible answer to these issues. Its ability to manage complex data interdependencies means that when you track something, you can create a hierarchy or ‘tree’ of data. Scanning the code of a particular pallet will automatically recall its contents and supply chain lineage.

Graphs are adept at mapping complex, inter-connected supply chains, and maintaining high performance even with vast volumes of data. Their inherent relationship-centric approach enables firms to better manage, read and visualise their data. This gives a trackable and in-depth picture of all products, suppliers and facilities and the relationships between them.

Using a graph database, our data shows that manufacturers can typically demonstrate 100 times faster query response speeds in contrast to a traditional SQL database. Chris Morrison, CEO of Transparency-One, which brings together supply chain expertise with cutting edge technology, notes how: “We got back results within seconds, something that we would not have been able to calculate without this [graph] solution.”

That sort of response time and ability to follow complex chains of dependencies is critical when you need to provide time critical responses or to identify a specific product’s location and journey.

Now is the time to look at a fundamentally new approach to supporting supply chain that the business is crying out for and which consumers will react highly positively to.

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

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Neo4j is the leading graph database platform that drives innovation and competitive advantage at Airbus, Comcast, eBay, NASA, UBS, Walmart and more. Thousands of community deployments and more than 300 customers harness connected data with Neo4j to reveal how people, processes, locations and systems are interrelated. Using this relationships-first approach, applications built using Neo4j tackle connected data challenges including artificial intelligence, fraud detection, real-time recommendations and master data. Find out more at


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