How UMS Reduced Manual Customer Clearing in SAP With BEST

For finance teams managing high-volume customer remittances (also known as customer payment advices), customer clearing can become one of the most time-consuming parts of month-end. When payments, deductions, claims and open invoices need to be checked line by line, the process can slow down the close, increase allocation risk and leave less time for query resolution.

At Unitrade Management Services (UMS), the Debtors Team needed a faster, more accurate way to manage customer payment allocation inside SAP. By implementing the BEST Customer Clearing module, UMS reduced manual effort, improved clearing accuracy and strengthened debtors control across a growing member base.

About UMS

Unitrade Management Services is a South African voluntary trading organisation supporting independent wholesale and retail traders across Southern Africa. Established in 2000, UMS operates across several trading formats, including Powertrade, Food Town, Best Buy and Price Rite.

Within this environment, the UMS Debtors Team manages customer remittances, deductions, claims and payment allocations across a high-volume member network. As remittance volumes increased, the team needed a more controlled way to process payments, clear open items and manage debtor ageing inside SAP.

The Challenge: Manual Customer Clearing Creating Month-End Pressure

Before implementing BEST Customer Clearing, UMS managed customer payment allocation through a highly manual process. Remittances had to be printed, processed line by line, and matched manually against open invoices in SAP. Staff also had to raise credits individually, making the process repetitive, time-consuming and vulnerable to allocation errors.

The risk was especially high when customers had multiple invoices or claims for the same value. If a customer had ten invoices for the same amount but only paid nine, accurate allocation required extensive manual checking to confirm which invoices should be cleared and which should remain open.

These challenges placed significant pressure on the Debtors Team during month-end. Staff could spend until the 10th or 11th of the month reconciling member remittances, leaving less time to investigate claims, follow up on queries and manage ageing balances.

The Solution: BEST Customer Clearing Inside SAP

UMS went live with BEST Customer Clearing at the start of August 2024, extending its existing use of BEST solutions inside SAP.

The decision was shaped by UMS’s previous experience with the BEST Vendor Recons module and BEST Statement Portal. The team had already automated creditor statement processing through BEST, with supplier statements uploaded through the existing portal process and integrated into SAP.

For customer clearing, UMS wanted to build on its existing internal capabilities. The organisation already had its own OCR capability and portal interface, so the project was designed to work alongside those systems while using BEST to support structured customer payment allocation inside SAP.

Members now receive a structured Excel remittance containing open items, claims and deduction columns. They return the remittance showing what they are paying. UMS’s in-house OCR process reads the remittance, generates the relevant claim lines and prepares the file for upload into BEST Customer Clearing.

This gave UMS the flexibility to keep using its existing portal and OCR capability while introducing automated matching, clearing and control directly inside SAP.

The Results: Faster Clearing, Greater Accuracy and Better Control

BEST Customer Clearing helped UMS significantly reduce the time spent on customer remittance processing.

Where the Debtors Team previously spent until the 10th or 11th of the month reconciling member remittances, the process is now completed by the 2nd of the month at the latest. In some cases, a large member remittance that previously took a full day to process can now be completed in around 15 minutes.

By March 2026, UMS had processed 189,035 remittance lines through BEST Customer Clearing. Of these, 181,086 lines had been cleared, representing a 99.6% clearing rate, with only 691 lines remaining open.

Across 2024 and 2025, the total value of remittances processed through the module reached R4.875 billion.

The time saving has also freed up between 10 and 12 days each month for the Debtors Team to focus on queries rather than manual remittance reconciliation. This has helped UMS address claims and customer issues earlier, rather than only dealing with them once they become aged.

UMS also reduced its debtor ageing balance by 40%, while managing a growing member remittance workload without increasing headcount.

Why It Matters for SAP Finance Teams

UMS’s results show how customer clearing automation can improve more than processing speed. By reducing manual allocation work, the Debtors Team gained more time to focus on claims, queries and ageing balances.

The improvement in allocation accuracy also reduced the risk of write-offs caused by inefficient or incorrect manual allocations. Customer communication improved too, with payment allocation queries effectively removed from the process.

For SAP finance teams, this is where automation can have a meaningful impact. Rather than relying on manual spreadsheets, printed remittances and line-by-line checks, teams can manage customer payment allocation through a more structured, visible and controlled process inside SAP.

Automating Customer Clearing With BEST

BEST’s Customer Clearing module helps finance teams automate customer payment allocation, improve clearing accuracy and reduce manual work inside SAP.

For UMS, the module created a faster, more reliable customer clearing process while still working alongside the organisation’s existing OCR and portal capabilities. The result was a calmer month-end, stronger debtors control and more time for the team to focus on higher-value activity.

For finance teams managing high remittance volumes, BEST’s Customer Clearing module provides a practical way to reduce manual effort, improve visibility and strengthen control over customer payment allocation in SAP.