Hi dear community,
my objective is to analyse a production for throughput times.
I want to distinguish in the analysis between two types of orders: those that include the production step „mixing“ and those that don’t (full-production vs. part-production). The data I have is a list with alle the return infos from the single workplaces. A second list connects the workplace numbers with the production steps
Order number | Workplace number | Duration (h) |
4025 | 7 | 0,1 |
4025 | 13 | 0,3 |
4025 | 19 | 0,5 |
4026 | 14 | 0,4 |
4026 | 19 | 0,55 |
4027 | 8 | 0,2 |
4027 | 13 | 0,3 |
4027 | 20 | 0,6 |
Workplace number | Production step |
7 | Mixing |
8 | Mixing |
12 | Modling press |
13 | Molding press |
14 | Molding press |
19 | Oven |
20 | Oven |
I was thinking about a variable input:
ordernumber~all orders|‘those ordernumbers where productionstep={[*mixing*]}‘~full-production| ordernumbers – ‘those ordernumbers where productionstep = {[*mixing*]}‘~part-production
Even though the logic seems clear I can’t put it into code – please give advise!
Btw: not possible via the number of steps, as the production steps can differ later in the production. Also I cannot just take out the „mixing“ in the process filter, as that means only to eliminate the duration of this step from the visualization, but not to exclude the orders with this process.
