The impact of instability on the manufacturing supply chain

Neo4j’s Alicia Frame on how COVID-19 & geopolitical instability has hit the manufacturing supply chain & how graph database technology can help

Over the past two years, disruption to global supply chains has been constant. COVID-19, various conflicts, and economic sanctions are affecting production capacity and logistics routes, creating even more unpredictability in supply chains. Food supply chains, for instance, have become increasingly globalised. The more complex a supply chain is, the more vulnerable it is. 

As one supply chain technology expert Transparency-One points out: “Supply chains that incorporate multiple tiers, are heavily globalised, and/or involve a number of components or stages of transformation, are inherently more complicated and at greater risk of being severely affected by a crisis.”

According to The New York Times, current global supply chain issues and chaos at ports, warehouses and retailers will persist throughout the year - perhaps longer. The newspaper reports that time alone will not solve the current supply chain disruption. It will require investment and technology.


From food to manufacturing, visibility is key

Visibility into all tiers of the supply chain is key. Manufacturers, distributors, and logistics companies also need a more agile way of dealing with the vast amount of intertwined data and regulations involved with delivering items globally while maintaining sustainability and social responsibility standards. Achieving visibility is one challenge. Making sense of the data and getting actionable business insights is another.

Companies with 360-degree visibility of their supply chains and supplier ecosystem understand how this will impact production. They know they need to look for alternative sources if there is a shortage of ingredients, for example, or if ports are locked down. Operators who are ill-prepared for significant disruption will find it almost impossible to mitigate supply shock and manage demand volatility.


Graph database technology can work faster than traditional database software

Many food sector businesses still have their data stored in silos, meaning they only have a partial view of what is going on in their supply chains. According to Forrester Analyst Alla Valente, the supply chain risk management tech stack often consists of Excel and SharePoint, which don’t have the ability to handle the data volumes or the sophisticated analysis required. Even if the data is stored in a single business database, she warns, understanding the connections between products on a production line or in pallets waiting to be shipped is next to impossible. Making traditional databases perform multidimensional tasks in real-time is also not a viable option, with performance degradation as the total dataset size grows.

Graph database technology’s inherent ability to record complex data interdependencies offers a highly scalable way to manage vast volumes of serial numbers, supplier and facility details, certifications, and documents. Graph technology records and joins the dots between complex data interdependencies. 

When you track a product or component, you create a hierarchy of data and store how it’s all connected. As graph-based tools are built specifically for connected data, performance is maintained across vast quantities of data. This is a useful capability as the number of unique serial codes alone can run into the billions. Suppose you record the code on a particular pallet, a graph database can automatically recall all of its contents. This includes the operational context such as which ports it shipped through, where it was manufactured, and the relationships between manufacturers. 

Using a graph database, businesses can typically demonstrate query response speeds 100 times faster than from traditional relational database software. This elevated response time is critical in a challenging and competitive world.

Food sector leaders need to prioritise sourcing appropriate tools and technology to provide visibility and insight into the complex, interconnected supply chains they work with. With insight into data flowing in and out of supply chains, the kind of smart, data-driven decision-making needed to make sense of today’s challenging world becomes possible.


Byline by Alicia Frame, Director—Graph Data Science, Neo4j, the world’s leading graph database company. 


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