The Accounting team at a Fashion Retailer currently reconciles their point of sale system to their bank system and performs a point of sales to SAP reconciliation if they notice a difference in totals. The reconciliation process takes an employee one hour on average and entails retrieving the monthly sales report from SAP and manually matching thousands of transactions with the corresponding billing information. To give accounting an opportunity to correct errors before having to perform the bank reconciliation, our RPA team used OpenBots to develop an automated solution to identify discrepant transactions from the POS report against transactions from the SAP report.

SOLUTION:

1. Bot Downloads Reports – The Bot logs into the POS system, enters a date range, and downloads the transaction report from that date range. The Bot then logs into SAP and downloads the transaction report from the same date range.
2. Bot Formats Data – The Bot creates a spreadsheet for each report and formats the transaction data, creating columns for Store, Transaction Number, Amount, and Tender Type.
3. Bot Tests for Differences – The Bot performs a query to find discrepancies between the POS report and SAP report. There are three exceptions the Bot looks for:
   1. Transactions that are logged in one report but not the other
   2. Transaction amounts that do not match
   3. Stores that do not match
4. Bot Generates Report – The Bot consolidates the query results into a report, then emails the report to the SME to make the necessary reconciliations.

This end-to-end automated solution runs on a weekly basis to preemptively catch errors before the Accounting department performs the bank reconciliation. The Bot takes 20-30 minutes to process one week’s worth of data, and approximately one hour to process the entire month’s transaction data. Along with the speed and accuracy yielded by this automation, streamlining the downstream process created more available time in the workday for the SMEs to focus on other high-priority tasks.