How many times did you click on a tab of related data and you find out there is actually no information in this tab? You click in another tab and still no info? Why did you even wasted time to click on this tab?
The solution was provided by some of our customers. Add a counter of the related information so you can’t get a direct insight of the related informations.
This is another key capability for our graph-based PLM solution, access management. If you look at your database, you not only have parts and documents but you also have the whole access setup. Users and their relationship to nodes they create and edit are part of the PLM knowledge.
A great request from today’s business is system auditability. The request got strong with the growth of Artificial Intelligence, which was creating Neuronal networks which were very efficient but hard to audit. Here it is very easy to understand why a user has access to a node, because it graphically says it. Either the user has a direct access or is granted access through a user group or a general permission you will know how it access the node.
Access Management Admin
We provide a nice UI to edit users, groups and permissions. It’s not only easy to understand but also fast to edit multiple permissions. You don’t need to navigate 100 different screens.
Direct Access Management
General rules, groups and permissions are great but in an agile world, if you are granted manager access on a node you should be able to give quick access to some specific people without the complexity of a whole group structure. We give that opportunity with another simple user experience directly on the node itself.
Still API First
I forgot to mention, that all this is API first. These mecanisms can work the same way with any other User interface, all the access management functions are API First.
The first time we were asked to exchange with someone about Product Lifecycle Management (PLM), we needed a whiteboard within 5 seconds of discussion to explain why we should use part masters, part versions, documents. How is this connected to a product definition? How do you relate it to a manufacturing process, to a DFMEA? We ended up with lots of graphs, which once they would be validated, would start defining the digital thread for a company.
PLM backbone is about maintaining a graph
Everything is connected in a graph and the information has value for the product whenever it happens. Whether it’s a requirement or a field incident it has a lot of value to a common product. These should be easily linked.
Importance of Semantic
Storing data like you use it is the real deal. On a graph you want to be able to specify node type names but also edges names to have a graph semantic that means something for anyone looking at the data.
Further analysis with graph algorithms
In addition to creating a PLM graph for the digital thread, an interesting use-case for Graph is to analyse production incidents and look for similar causes of defects. During graphconnect 2018, Boston scientific with graphaware presented some studies they made to find product defects coming from a similar production machine. When you see where to look, than any database solution could help you. When you don’t know where to look then graphs are opening a much wider spectrum of analysis.
What about Ganister?
We do store all our data within a graph database. Which means all the data you store and manipulate has the capability to be queried with a very advanced query model with graph algorithm. It’s also much easier to understand the data. The semantic graph representing your data gives you a good understanding of your information value. API First and graph database = the right way to digitize the product lifecycle management.