Rolling out orders over multiple time axis

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Hey community,

I need some advice in modelling. The scenario is that we receive orders, which will be partly shipped with some delay over up to 12 month. Each shipment needs up to 12 Month to be delivered. So we implemented the following:

  1. Plan monthly order volume
  2. Plan the shipment delay in relation to the order month as %
  3. Plan the delivery delay in relation to the shipment month as %
  4. Calculated how much volume will be shipped in a given month based on 1 and 2
  5. Calculate how much volume will be delivered in a given month based on 3 and results of 4

What we can report/analyze now is

  • How much order volume we (plan to) have each month
  • How much shipment volume we (plan to) have each month
  • How much will (assumingly) be delivered each month

What we can’t report/analyze is

  • Of which order months consist the total delivery volume of a given month

In my mind I thought of introducing 3 time axis. Order month, shipment month and delivery month. Then we would have a cross product of 3 time points. That could in theory hold the data in a way, that we can slice each time axis accordingly and do not loose details on the other two time axis. But I don’t think, that this can be done in Anaplan. For the obvious reason, that there are no multiple time axis and for the less obvious reason, that there aren’t any function to process data from other dimension – I think.

Do you fine people have an idea?

Note: The concrete use case isn’t processing orders in a transportation company, but processing contract signing in the energy sector. The problem condenses down to what I described above. I thought this description is more accessible to people not working in the energy sector

Details to the current modelling approach:

Module 1: Input Order Volume

Dimension:

Time

[and some more details but not relevant for my question]

Line Item:

Order Volume

Modul 2: Input Shipment Delay

Dimension:

Time

[and some more details but not relevant for my question]

Line Items:

Percentile Month +0

Percentile Month +1

Percentile Month +12

Modul 3: Input Delivery Delay

Dimension:

Time

[and some more details but not relevant for my question]

Line Items:

Percentile Month +0

Percentile Month +1

Percentile Month +12

Module 4: Calculated Order to Shipment Volume

Dimensions:

Time

[and some more details but not relevant for my question]

Line Items:

Order Volume (Reference to Modul 2)

Percentile Month +0 (Reference to Modul 2)

….

Percentile Month +12 (Reference to Modul 2)

Order to Shipment +0 (Calculated as “Order Volume” * “Percentile Month +0”)

….

Order to Shipment +12 (Calculated as “Order Volume” * “Percentile Month +12”)

shipment volume from offset -0 (Calculated as “OFFSET('Order to Shipment +0', 0, 0)”)

….

shipment volume from offset -12 (OFFSET('Order to Shipment +12', -12, 0))

Shipment Volume (Calculated as the sum of “shipment volume from offset -0” to “shipment volume from offset -12”

Module 5: Calculated Shipment to Delivery Volume

This module works exactly as module 4, but doesn’t process order volume but the “Shipement Volume” from module 4

I added the module and line item export files as an attachment.

Answers

  • Hello @OliverIbach ,

    Could you perhaps make the above a bit more digestable or even break it in small chunks so we can help?

    I am afraid it requires way too much attention right now.

    Cheers!

    Kind regards,

    Bruno Rodriguez

  • Sure. I ll try it with a visual representation

    As mentioned, orders are placed, converted to shipments and shipments are converted to deliveries.

    The current approach:

    1. calculates for each order the shipment volume and shipment time points. This results in the shipment volume for each point in time. Each shipment may consists of several order parts. For example shipment S2, with consists of a part of order O1 and a part of order O2.
    2. Having the total shipment volume calculated for each point in time, the shipment volume is converted into delivered volume and corresponding delivery time points. Accordingly each delivery may consists of several shipments. For example the delivery D2 consists of Shipment S2 and S3.

    The Problem with the current approach is, that we don’t know of which orders and which order volumes for example D2 consists of, hence the approach aggregates the orders to shipments in the first step.