Basic Information
| BW Server | WBP |
|---|---|
| Application | ITC - PCM |
| Query Name | BW - PCM - By GBU (Core Workbook) |
| Query Technical Name | BW_WBK_PCM_0001 Workbook (Query : BW_QRY_MVFIAR10_0001) |
| Core/Specific Scope | Core |
| Provider Name | MVFIAR10 |
| Usage type | Direct use of Workbook via Analysis |
| Expected users | Cash Collectors |
Purpose of this query
These reports will provide a tool to the credit manager to be able to have predictions about the customer payments. These predictions will be based on the history of the customer payments.
The data come from AR from several systems :
- PF1
- WP1
- RHO (this part will be removed soon)
- PI1 (factoring process)
The data of these reports are updated once a day around 10am.
Variables screen
| Variable Name | Authorization Object | Description |
|---|---|---|
| BFC GBU | NO | GBU |
| PRS Cust Zone | NO | Zone of the PRS Customer |
| Collection Specialist | NO | Code/Name of the cash collector assigned to the customer |
Filters
There are no specific filters directly in the query but several filters are applied before (in the dataflow) in order to be aligned with the filters found in query BW_QRY_MVFIAR01_0006 - Credit Mgt - Aged balance (Core Query)
Characteristics
| Characteristic Name | Explanation |
|---|---|
Zone (FSCM) | The FSCM zone groups countries by geographical zone : APAC, EMEA, LATAM and NAFTA. |
Customer country | The country of the customer |
BFC Global Business Unit | The GBU |
PRS Payer | The PRS payer is the main reference for the payers that belong to different system. It will permit to see the global situation even if the customer local reference comes from several systems. |
Coll. Specialist | The collection specialist comes from the FSCM master data. |
Rep. group | The representative group comes from the CM master data. This information is taken from the credit account (payer + credit control area). |
Source system | The source system of the item. It will serve to determine the master data associated to the local reference of the customer. |
Customer number | Local customer reference. |
Last payment method | It’s the last payment method used by the customer. |
Key Figures
| Key Figure | Explanation |
|---|---|
Outstanding amount | Situation of the open items for the payers. |
Overdue amount | Open items that will be in overdue at the end of the month. |
Pre-chasing last 3d | Total open amount for documents with a net due date in the last 3 days of the month |
Risk ranking | Rating score, taking into account both chasing and prechasing risk |
Avg not PIM (%) | Amount considered at risk for payment before the end of the current month (PIM = Paid In Month) |
Risk amount not PIM chasing | Amount to be chased and considered at risk for payment before the end of the current month (PIM = Paid In Month) |
Rate not PIM (6) % | Overall % of documents not paid in month over last 6 months history for the payerRate |
Average Delay (6) | Average delay of payment of the payer in the last 6 months |
Pay cycle dom | Most frequent Day of payment in the month when a pay cycle is detected (in day of month) |
Pay cycle week | Most frequent Day of payment in the month when a pay cycle is detected (in week) |
Nb GBUs for payer | Number of GBU for the PRS Customer with open amountPayer |
The following key figures are hidden by default but can be displayed :
| Key Figure | Explanation |
|---|---|
Overdue 0-4 | Overdue with 0 to 4 days in arrears |
Overdue 5-10 | Overdue with 5 to 10 days in arrears |
Overdue 11-30 | Overdue with 11 to 30 days in arrears |
Overdue 31-60 | Overdue with 31 to 60 days in arrears |
Overdue 61-90 | Overdue with 61 to 90 days in arrears |
Overdue > 90 | Overdue with more than 90 days in arrears |
Pre-chasing amount | Total open amount for documents with a net due date between today and the end of month (PRECHASING) |
Risk not PIM pre-chasing | Amount to be prechased and considered at risk for payment before the end of the current month (PIM = Paid In Month) |
Overdue risk | Expected amount to be paid after the net due date (overdue, disregarding payment in month or not) |
Exp. Amt overdue (%) | % of the above compared to total pre-chasing amount |
Chasing amount | Total open amount for documents with a net due date in the past |
OB nb overdue | Number of orders blocked because of an overdue (nota: this indicator is currently refreshed on a weekly basis in BW) |
Rate not PIM (12) % | Overall % of documents not paid in month over last 12 months history for the payerRate |
Rate not PIM (3) % | Overall % of documents not paid in month over last 3 months history for the payerRate |
Average Delay (3) | Average observed delay on overdue documents, taking into account last 3 months history for the payer |
Currency Conversion
All Key Figures are in EUR. Conversion is done by the predictive system (Python/SQL) based on the same rules are the ITC aging balance queries. It is not possible to display in another currency.
Jump Query
This workbook/Query contains a "Jump" option which allows to select a value and then jump into another query that shows the details by document.
To do it, just right click on the level you need, and the detail by document number will be displayed.
Documentation on the Jump detail query can be found here.
FAQ
Question : Is it possible to choose the date ?
No. The report will always display the data while selecting the last day of the current month for the overdues calculations. It is not possible to change it. The reason is that this query uses the predictions done by the Predictive Credit Management (PCM) algorithm. This algorithm is run every day and updates the predictions only for the current situation.
Question : At which level is the prediction done ?
The prediction is first done at very detailed level by FI document. Then it's aggregated at Customer Number X GBU level. The "GBU" report shows the results at Customer PRS + GBU level but you can in fact add the Customer number to have the detail. To have even more detail you need to use the jump query.
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