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rbrandes
Posts: **3**

Hi,

my name is Richard and I am currently writing my bachelor thesis about process mining. I've gathered some data about processes from a partner company which is quite nice, but I am not sure how make best use of this.

The data is about both the order administration and manufacturing processes. The tricky part is that orders from customers can have more than one ordered items. Sometimes the same customer order gets splitted apart when it comes to manufacturing the ordered items.

for example Order 001:

Order 001 consits of Item1, Item2 and Item3 and arrived at 12 am

After order registration there will be 3 seperate "sub orders" for manufacturing:

Order 001-1 for producing item 1

Order 001-2 for producing item 2

Order 001-3 for producing item 3

All 3 suborders would then be seperatly processed, each with their own timestamps. Since I've got data for both the customer and manufacturing orders, the question is now how can I put together those sets of order data?

Option 1:

I can map the manufacturing sub orders to the original customer orders. E.g. all 3 Order 001-1, Order 001-2 and Order 001-3 would turn into Order 001. This means I can evaluate the complete process from the order from the incomming order from the customer to the succesfull shipment and billing but I lose the information from the context of the suborders.

Option 2:

I could create for every manufacturing sub order a corresponding higher order. E. g. for Order 001-1 there would be Customer Order 001-1 with Order arrival at 12 pm, Customer Order 001-2 with Order arrival at 12 pm and also Customer Order 001-3 with Order arrival at 12 pm. This doesnt seem like the optimal scientific approach and would tripple the already not so small data size.

Does anybody know if there is a nice way to merge those data sets together? Optimally there would be a possibility to start with the single customer order and lateron split into the seperate sub orders, kind of like the 3 way split seen at the grafik.

Currently I am working with option 1 but honestly this kind of feels not right.

Feel free to ask anything about the problem, I realy struggled while trying to explain myself but hope I got my problem accross

Help or suggestions about to approach this problem would really be appreciated )

Have a nice day

Richard

my name is Richard and I am currently writing my bachelor thesis about process mining. I've gathered some data about processes from a partner company which is quite nice, but I am not sure how make best use of this.

The data is about both the order administration and manufacturing processes. The tricky part is that orders from customers can have more than one ordered items. Sometimes the same customer order gets splitted apart when it comes to manufacturing the ordered items.

for example Order 001:

Order 001 consits of Item1, Item2 and Item3 and arrived at 12 am

After order registration there will be 3 seperate "sub orders" for manufacturing:

Order 001-1 for producing item 1

Order 001-2 for producing item 2

Order 001-3 for producing item 3

All 3 suborders would then be seperatly processed, each with their own timestamps. Since I've got data for both the customer and manufacturing orders, the question is now how can I put together those sets of order data?

Option 1:

I can map the manufacturing sub orders to the original customer orders. E.g. all 3 Order 001-1, Order 001-2 and Order 001-3 would turn into Order 001. This means I can evaluate the complete process from the order from the incomming order from the customer to the succesfull shipment and billing but I lose the information from the context of the suborders.

Option 2:

I could create for every manufacturing sub order a corresponding higher order. E. g. for Order 001-1 there would be Customer Order 001-1 with Order arrival at 12 pm, Customer Order 001-2 with Order arrival at 12 pm and also Customer Order 001-3 with Order arrival at 12 pm. This doesnt seem like the optimal scientific approach and would tripple the already not so small data size.

Does anybody know if there is a nice way to merge those data sets together? Optimally there would be a possibility to start with the single customer order and lateron split into the seperate sub orders, kind of like the 3 way split seen at the grafik.

Currently I am working with option 1 but honestly this kind of feels not right.

Feel free to ask anything about the problem, I realy struggled while trying to explain myself but hope I got my problem accross

Help or suggestions about to approach this problem would really be appreciated )

Have a nice day

Richard

## Answers

8643thank you for your answer! Though I am not sure if I understand correctly what you mean with the mapping of the items.

I've double checked my customer order data set and realised that I can actually match my customer order positions with the manufacturing orders which means that I can trace my orders from the start to the end.

Still thank you very much

Greetings

Richard