From 2/4/19,
A Ducks #2 = 2 (largest value)
A Swans #3 = 2
B Swans #4 = 1
Total 5
In the real data;
on a given date....
- there will be many patients
- each patient might have multiple different drugs prepared for them
- each drug can have multiple presentations
- some of the presentations might take more work to prepare
We grabbing a workload snap shot from a different data set which is primarily financial rather that capacity/workload planning. We have a capacity model that assigns a complexity rating (crudely a time measure) to the preparation of differing drugs and their pharmaceutical presentation.
Using the financial data gives us faster access to workload data, all be it only an approximation, and allows better matching resource to patient load than we can achieve waiting for manufacturing and clinic data feedback.
Ideally I need to....
Total daily workload ('value')
Total by active drug ('Ducks & Swans') - however I need to grab only the hardest preparation (highest 'value') as my capacity (complexity) measure otherwise I will end up with grossly inflation estimated workload which will be valueless in maintaining maximised throughput
Total by patient ('A & B')
Sorry for the long winded explanation!
I
