Good question.

This is the bread and butter of Process Mining.

What kind of result are you looking for? Do you want to have the results in a table as info or do you want to act upon instantly and trigger actions?

Some suggestions to identify orders that do not align with FIFO (First IN, First OUT I assume). However, you need to define a strict business rule/logic first to define what FIFO is to you.

• What kind of data do you want to use? What kind of data do you think you need? How is this data structured? Is this data complete, clean, reliable and up-to-date?
• Do you need data from one or multiple sources? How are you going to combine data if you require multiple sources, etc.

But let's assume you have this all figured out and have a dataset that fits your requirements: Some suggestions to identify roque/maverick behavior/conflicting FIFO.

• Just use the timestamp of ordering? focus on the oldest available timestamp first (or define the orders by date or other identifier to define the oldest order and check whether the order that is being shipped matches the oldest order > if not it is a conflict > do you want to list these or do you foresee other required (real time) actions?
  • You can use ordernumbers, case_keys, timestamps, sorting or other identifiers
• Perhaps use a status (if available) or priority to flag some orders that in fact are allowed to be != (not) FIFO.

Hope this helps to get you going 👍

Hi Kayla,

It depends a bit on the context of your problem. We, for example, have the Open Order Processing app which prioritizes orders for customer service teams to make sure that any blockers are resolved on time and hence to avoid any shipment delays or inefficiencies.

In addition, we have recently released an Unshipped Orders app that is more focused on the fulfillment side and analyzes root cause of shipment delays.

I'm happy to give you some more details if that goes in the right direction?

Best,

Moritz