Supply Chain Management with graphs: part 3/3 - some SCM analytics

I've been looking forward to writing this: this is the last of 3 blogposts that I have been planning to write for weeks about my experiments with a realistic Supply Chain Management Dataset. There's two posts before this one:

• In the first post I found and wrangled a dataset into my favourite graph database, Neo4j
• In the second post I got acquainted with the dataset in a bit more detail, and I was able to do some initial querying on it to figure out what patterns I might be able to expose.

In this this third and last post I would like to get a bit more analytical with the dataset, and do some more detail investigation in order to better understand some typical SCM questions. Note that I am far from a Supply Chain specialist - I barely understand the domain, and therefore I will probably be asking some silly questions initially. But bear with me - and let's explore and learn, right?

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Supply Chain Management with graphs: part 2/3 - some querying

So in the previous post, we got introduced to a dataset that I have been wanting to get into Neo4j for a long time: a Supply Chain Management dataset. Read up about it over here, but the long and short of it is that we got ourselves into the situation where we have an up and running Neo4j database with 38 different multi-echelon supply chains. Result!

As a quick reminder, here's what the data model looked like after the import:

Or visually:

Data validation and profiling

The first thing to do when you have a new shiny dataset like that, is of course to get a bit of a feel for the data. In this case, it really helps to understand the nature of the different SupplyChains - as we know from the original Excel file that they are quite different between the 38 of them. So let's do some profiling:

match (n) return distinct labels(n), count(*)

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Supply Chain Management with graphs: part 1/3 - data wrangling and import

Alright, I have been putting the writing of this blogpost off for too long. Finally, on this sunny Saturday afternoon where we are locked inside our homes because of the Covid-19 pandemic, I think I'll try to make a dent in it - I have a lot of stuff to share already.

The basic idea for this (series of) blogpost(s) is pretty simple: graph problems are often characterised by lots of connections between entities, and by queries that touch many (or an unknown quantity) of these entities. One of the prime examples is pathfinding: trying to understand how different entities are connected to one another, understanding the cost or duration of these connections, etc. So pretty quickly, you understand that logistics and supply chain management are great problems to tackle with graphs, if you think about it. Supply Chains are graphs. So why not story and retrieve these chains with a graph database? Seems obvious.

We've also had lots of examples of people trying to solve supply chain management problems  in the past. Take a look at some of these examples:

• https://neo4j.com/graphgist/supply-chain-management
• https://neo4j.com/blog/graph-technology-supply-chain-transparency-corporate-social-responsibility/
• https://neo4j.com/news/graphing-the-supply-chain/ and https://www.enterprisetimes.co.uk/2019/09/10/graphing-the-supply-chain/
• https://neo4j.com/blog/nlp-at-scale-maintenance-supply-chain-management/

And of course some of these presentations from different events that we organised:

• Our friends at Caterpillar used Neo4j for this:
• TransparencyOne actually built a business on it:

So I had long thought that it would be great to have some kind of a demo dataset for this use case. Of course it's not that difficult to create something hypothetical yourself - but it's always more interesting to work with real data - so I started to look around.

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Graphistania 2.0 - Episode 5 - This Month in Neo4j

Friends.

These are interesting times. These are difficult times, but we can deal with it together, as a community, as a graph. So that's why we were super happy that, just as Belgium was going into lockdown last week, we were able to record another Graphistania podcast episode for you, talking about the world in general, but also covering some of the amazing graph use cases that drifted over our screens in the past month, in the This Week in Neo4j (TWIN4J) newsletter.

There were actually many things to talk about, in terms of fascinating graph use cases, and I will highlight only the most striking ones here.

Our friends at Kineviz did some really interesting and timely work on  COVID-19 temporal and spatial data visualization. This stuff is really important to understand, as pandemic spreads clearly follow graph patterns. Read Connected if you are not convinced. 

Worth highlighting: Bloodhound: Windows network penetration testing with Neo4j, had a new release that you might want to take a look at. If you are not familiar with Bloodhound yet, you may also want to check out my interview with the Bloodhound crew on this podcast a while back. 

We published this fun little thing called a Neo4j Treasure Map - check it out! 

Finally - we also have a a Winegraph! It's a great example of importing data from the web using Norconex.  

Some interesting stuff on using Neo4j for Gene ID mapping: take a look! 

Another examle of enriching graphs with Wikidata, from the one and only Mark Needham: look at Mark's blog over here! 

Don't forget: we Introduced the Neo4j Graph Data Science plugin with examples from the "Graph Algorithms" book. 

A really interesting tweet about a visualisation of the US Supreme court as a graph db... Would love to see more like that. 

And for some fun: Pokégraph: Gotta Graph 'Em All! 

Some important stuff: we did a great 4.0 webinar that is giving you a lot of info on what to expect in the new version of Neo4j.  

There was a great update to NeoMap: Visualizing shortest paths with neomap ≥ 0.4.0 and the Neo4j Graph Data Science plugin.

Those were the most important ones. So let's talk about these now - I am sure there's a lot of cool stuff here fore everyone!
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