Bulk postings in SAP
Invoice entry, order creation, stock adjustment and material master creation from spreadsheets — a classic in tax and procurement.
We automate repetitive tasks that eat your team's hours today — reconciliation, invoice posting, report extraction, master data entry. With one rule: we only build the robot if the ROI adds up on the spreadsheet first.
RPA has a structural problem that gets little airtime: the robot is fragile by nature. It imitates a human clicking on a screen. When the screen changes — an upgrade, an SAP note, a new field — the robot breaks. If nobody maintains it, it gets switched off and the company goes back to doing the work by hand, now convinced that 'RPA does not work'.
The second reason is picking the wrong process. Automating a bad process only makes the error happen faster and in greater volume. An unstable process, with too many exceptions and unclear rules, is not a candidate for RPA — it is a candidate for redesign.
Our approach tackles both head on: we filter processes before automating, and we deliver the robot together with the plan for who maintains it.
The cases below repeat at practically every client we serve.
Invoice entry, order creation, stock adjustment and material master creation from spreadsheets — a classic in tax and procurement.
Comparing statement, ledger and source system, flagging discrepancies automatically. Cuts closing time dramatically.
The robot logs in, runs the transaction, exports, formats and sends. The simplest case to automate and the one with the most immediate return.
User creation, role assignment and provisioning across several systems — a process that usually crosses three departments and stalls in all of them.
Data collection, validation and submission on government portals, with evidence logged for audit.
Extracting data from invoices, contracts and payment slips in PDF or image, validated against the system before saving.
We map candidate processes and score each one by volume, current manual effort, rule stability and criticality. The output is a prioritised queue, not a wish list.
For the top of the queue we calculate hours saved per month, build cost and annual maintenance cost. If the payback does not add up, we recommend not automating — and explain why.
We define the robot's flow, the exception handling and what happens when something goes wrong. A robot without exception handling is a time bomb.
Robot construction, tests with masked real data and sign-off with the team that does the process by hand today.
The first weeks with daily follow-up, comparing the robot's output against the manual process running in parallel.
Execution monitoring, failure alerts and a report of hours actually saved — the number that justifies the next robot.
RPA is the right tool when there is no API, when the system is legacy or third-party and cannot be changed, or when the process crosses several systems that do not talk to each other. In those cases the robot is the cheapest and fastest route.
But when an API is available, or when the process lives entirely inside SAP, an integration or an ABAP development is usually more stable, faster to run and cheaper to maintain in the long run. A robot that could have been an integration is expensive technical debt.
Because we also do software factory and SAP projects besides RPA, we have no incentive to push a robot where it is not the best answer.
RPA (Robotic Process Automation) is software configured to perform, through the systems' own interface, the same actions a person would: open a screen, type, click, copy, paste, export. Because it works through the interface, it requires no change to the systems involved — that is what makes it attractive in environments full of legacy systems.
High volume, high repetition, clear rules, few exceptions and structured digital input. If the process depends on frequent human judgement, changes every week or has exceptions in half the cases, it is not ready for RPA. In that scenario the gain comes first from redesigning the process.
The basic sum compares the cost of building and maintaining the robot against the cost of the hours spent on the manual process today, including rework from errors. The common mistake is forgetting maintenance: every robot consumes support hours over the year. Our business case always includes that line — it is what separates the promise from reality.
In what we see in practice, the most common effect is reallocation, not redundancy. The robot takes the mechanical part — typing, checking, exporting — and the person moves on to handling exceptions, analysing discrepancies and dealing with what needs judgement. Teams that communicate this from the start face far less resistance during the project.
We are platform-agnostic and work with the main tools on the market. The choice depends on what already exists in your company, the number of robots expected and the licensing model that makes sense in your case. If you already hold a licence for a platform, the normal move is to use it.
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We run an initial mapping and return a prioritised queue with an estimated return per process. You decide what goes in.