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Detailed case studies from the financial sector for SAP "custom code" projects
Case study 1: Automation of payment reconciliation
Previous situation:
A finance company in the financial industry used SAP ERP to process its financial transactions. Payment reconciliation was done manually, which was time-consuming and error-prone. Employees had to manually reconcile payment documents with the corresponding entries in SAP, which led to delays and inconsistencies.
Project Objective:
The company decided to automate payment reconciliation to increase efficiency and reduce errors. It planned to develop custom code in SAP to automate payment reconciliation and reduce manual effort.
Implementation and results:
The company commissioned a team of SAP developers to develop the custom code. The code was developed to automatically match payment documents with the corresponding entries in SAP. This was done using set criteria such as amounts, invoice numbers and account information. After the implementation of the code, the payment reconciliation could be fully automated. This resulted in significant time savings and a reduction in errors as manual entry and verification were eliminated. The accuracy and consistency of the payment reconciliation was improved, resulting in better financial control.
Case study 2: Improving the cash flow forecast
Previous situation:
A company in the financial industry used SAP ERP to manage its financial data, including cash flow forecasts. The existing forecast models were based on manual assumptions and estimates, which led to inaccurate results. The accuracy of cash flow forecasts was insufficient, leading to challenges in liquidity planning and management.
Project Objective:
The company decided to improve the accuracy of cash flow forecasts by using SAP technologies and custom code. The goal was to develop data-driven forecast models based on historical data and other relevant factors.
Implementation and results:
A team of SAP developers was tasked with developing custom code based on the company’s cash flow data. The code was designed to analyse historical data, identify trends and build statistical models for cash flow forecasting. By using SAP HANA, an in-memory database, large amounts of data could be analysed and processed quickly. After implementing the code, the accuracy of cash flow forecasts improved significantly. The company was able to make more informed decisions, optimise liquidity planning and identify potential bottlenecks at an early stage.