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Parallel tasks for single case
I am new to process mining, but thought my data would fit the requirements to perform process mining algorithms. After some tries with Prom I am not sure if I can use those methods with my data.
It's about robots, that are working on an item with an order ID. So my mapping would be robot=task, orderID=case, timestamp from, timestamp to. The goal would be to discover bottlenecks in the workflow.
What makes the thing challenging is the fact that multiple robots can work on the same order ID at the same time.
case; task; from; to
1; A; 2016-07-04 19:19:49; 2016-07-04 19:22:40
1; B; 2016-07-04 19:19:49; 2016-07-04 19:21:36
1; B; 2016-07-04 19:22:30; 2016-07-04 19:22:54
1; C; 2016-07-04 19:22:56; 2016-07-04 19:24:27
1; D; 2016-07-04 19:24:49; 2016-07-04 19:27:55
1; B; 2016-07-04 19:24:53; 2016-07-04 19:25:24
1; C; 2016-07-04 19:25:10; 2016-07-04 19:25:38
When I use the inductive miner I usually get a model with all tasks in parallel, which doesn't help a lot.
So my question is: Is it possible at all to use process mining methods to model event data, where multiple tasks are executed in overlapping time intervals on the same case?
Thanks for any help on this.