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InfluxDB
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Building synthetic BOMs can be fairly straightforward if you can define the level of coherency you want to see. The only big trick in building structured data like this that I have built is to first build dictionaries of randomized data with very little coherence and then build larger structures that include elements of the dictionaries.
As an example, you might want to have a model of users interacting with a web site, ordering products and shipping them to their homes. This can start with building a dictionary of user records and orderable item descriptions. The user records would have an address and some "interest" variables that define what the user is likely to order. The item descriptions can have lots of a little information but would centrally contain a part number and some information that allows the part to be selected efficiently (a numerical vector may be enough). If you want to be crazy, you can use generative models to generate descriptions from random semantic starting points or use lower level tables to piece together these things.
At this point, you can pretty easily build a user model and run it for each user to generate coherent transactional histories.
Several of these ideas are present in a project I worked on called log-synth [1]. For instance, the VIN generator has tables of factories and such for BMW and Ford so it generates kind of coherent VINs that can be traced back with factory location, engine and body type. If you look hard these are nonsense, but if you squint the right way they look fine.
The commuter generator or the DNS query generator are examples of a higher-level transaction generators. For the commuter, there is a model of a user with a home location and a work location. These commuters go to work some days and run errands other day and there is a simple model to pick an activity. Digging in, each activity breaks down into journeys along entirely incoherent road structures but details like a physical model of the engine and car velocity is maintained so you can get realistic diagnostics from the vehicles from somewhat realistic life histories. The DNS query generator is similar but with less physics.
One nice statistical concept in all of this is the concept of a statistical distribution over a notionally infinite set. Some things in the set will be much more commonly seen than others and thus we are likely to see those sooner. The generator of these things can maintain an estimate of the frequency of all previously seen things and a probability of seeing something new (see the Chinese Restaurant process [2]). You only need to generate the specifics of a thing in this infinite when you first see it which gives you pretty realistic texture to the fictional transactional world.
Relative to your problem of multi-level BOMs, you could say that a BOM is a list of items. Pick the desired length from a suitable distribution. Then pick each item from a Chinese Restaurant process. As you generate new items, decide if the item is composite and if so, generate a BOM for it recursively. Constraints like forcing a composite item to not recursively contain anything of the same type can be enforced using a rejection method (sometimes).
If this seems at all interesting, ping me by filing an issue on the log-synth github repository.
[1] https://github.com/tdunning/log-synth
I recommend the OMOP schema as a goto standard for EHR data. There's an ETL pipeline for converting Synthea output into OMOP.
https://github.com/OHDSI/ETL-Synthea