How to use /PRA/FP_VAR_RPT_MO - ONRR-2014 Variance by Report Month

SAP Transaction Code - Details

• Transaction Code: /PRA/FP_VAR_RPT_MO

  Description: ONRR-2014 Variance by Report Month

  Release: S/4HANA and ECC 6

• • Program: /PRA/R_FP_VARIANCE_REPORT_MNTH

    Screen: 1000

    Authorization Object:

  • Development Package: /PRA/ONRR_2014

    Package Description: ONRR-2014

    Parent Package: /PRA/

  • Module/Component: IS-OIL-PRA

    Description: Production and Revenue Accounting

Smart SAP Assistant

• SAP Tcode: /PRA/FP_VAR_RPT_MO - ONRR-2014 Variance by Report Month

  
  Overview:
  The SAP transaction code /PRA/FP_VAR_RPT_MO - ONRR-2014 Variance by Report Month is used to generate a report that shows the variance between the actual and expected values for a given report month. This report is used to identify any discrepancies in the data and to ensure accuracy in the reporting process. 
  
  Functionality: 
  This transaction code allows users to generate a report that shows the variance between the actual and expected values for a given report month. The report can be used to identify any discrepancies in the data and to ensure accuracy in the reporting process. The report can also be used to compare different months or years of data, as well as to analyze trends over time. 
  
  Step-by-Step How to Use 
  1. Enter the transaction code /PRA/FP_VAR_RPT_MO - ONRR-2014 Variance by Report Month into the command field. 
  2. Select the desired report month from the drop-down menu. 
  3. Select the desired output format (e.g., PDF, Excel, etc.). 
  4. Click “Execute” to generate the report. 
  5. Review the report for any discrepancies or trends in the data. 
  
  Other Recommendations: 
  It is recommended that users review their reports regularly to ensure accuracy in their reporting process. Additionally, users should consider using other SAP transaction codes such as /PRA/FP_VAR_RPT_YR - ONRR-2014 Variance by Report Year or /PRA/FP_VAR_RPT_QTR - ONRR-2014 Variance by Report Quarter to compare different periods of data and analyze trends over time.