I am facing the same problem, but in a different context, namely supply chain / inventory optimization. I work in the manufacturing industry, and I would like to create an RNN model to represent the sequence of manufacturing steps from raw material to finished part.
At each step, the RNN would predict the advance/delay of the execution of the manufacturing operation compared to the planned execution date. The goal is to reduce the buffers between two manufacturing steps (thus reducing overall stock and save on working capital) while still delivering the part on time at the last step of the manufacturing process.
Right now I am using a plain tabular structure similar to the Rossmann example. But the RNN would be useful because then I would be able to simulate a decrease in time between two successive manufacturing steps (in other words: decreasing the buffers) and see whether the part would still be delivered on time at the last step.
I have access to a large amount of historical data coming from my company’s ERP system. Each line in the dataset represents the validation of one specific step of the manufacturing process (execution date, planned date, part id, manufacturing step id, + lots of additional metadata)
One key aspect is that the number of steps in the manufacturing process is not fixed: it can go roughly from 5 to 15 depending on the type and complexity of the part. This is why I am relating to your post. I haven’t started to look into the details of the RNN but I understand from your post that this seems difficult to implement right now.
Therefore I am very interested to a solution to both these RNN applications.