The majority of organizations searching for automation in their order-to-cash process hit the wrong obstacle. They set the order to be processed – the work, the approvals, the routing, without doing anything with its underlying data. And then they question why the bots stop running.
I’ve seen the same story enough to see that this is not a unique case. This is what happens when master data governance is viewed as a separate project instead of being the very essence of OTC automation.
What OTC is Actually Based On
Order-to-cash seems to run in a straightforward manner – order received, product delivered, invoice issued, payment received. In reality, it is a chain of dependencies, which starts from customer master creation and includes processing sales orders, checking availability, delivering the product, shipping, billing, and collecting payments. Each step of this process refers back to the master data pool, which consists of customer records, sales areas, pricing channels, taxation codes, shipping addresses, payment terms, and credit limits.
None of this sounds unusual whatsoever, but this is precisely where things start to break. Duplicate customer records, outdated tax classification, addresses never validated – any of these issues can block the process that otherwise appears perfectly automated.
The Silent Expense of Inaccurate Master Data
Budgeting for master data issues isn’t the kind of thing that typically gets thought of when people plan their budgets, though one of the reasons for this issue is it’s effectiveness in being too expensive. Incorrect terms of price. Duplicated clients. Occurred delivery issues. Adjustments in billing which take lots of time that one AR manager would spend on the task. Blockage in credit lines that shouldn’t even be there. Each of these aspects is not a single major failure, it is rather a collection of little troubles connected with the issues caused by manual efforts which nullify the whole purpose of automation.
Automation based on RPA technology and SAP workflows has lots of issues in dealing with such troubles. Most importantly, the organizations that use them have to suspend their automated processes, delaying many efforts or suspending them and switching to manual ones instead of using automation technologies.
Where MDG Comes into the Picture
SAP MDG is not a spectacular solution, however, it solves a very specific task – bringing order to unregulated processes of master data development.
The result is not simply “cleaner data” in an abstract sense. It is a credible single source of customer data that all downstream processes – whether they involve sales, credit, billing, or collections – can rely on without having to double-check it.
What Happens When You Integrate MDG with OTC
The important thing is not merely automating the order creation itself. The significant aspect is what can be achieved with customer data that comes with order creation.
Customer onboarding process no longer is a manual bottleneck – the customers no longer submit requests by emails or spreadsheets which then are inputted into the system manually. The requests go through standardized intake forms and are validated based on mandatory fields and duplicates rules automatically before being routed to the right approver without anyone chasing it down. The record going out is ready to be used, not something that will need to be fixed later.
Sales orders are not bounced anymore – many order rejections happen due to bad customer data rather than bad ordering rules, i.e. inaccurate addresses, missing delivery details, outdated payment methods, incorrect tax classifications.
Credit checks are expedited – Most of the time, a blocked order or manual credit evaluation is a result of the lack of proper customer classifications and not of any real threat with finances. Clean data has made it possible for an automatic credit check to perform its role and avoid the need for human intervention.
Invoicing is improved – The necessity to troubleshoot just about invoices has reduced due to our consistent invoicing information.
Collections speed up – Standardized payment terms and accurate banking details have simplified the reconciliation process, and this is visible in the actual results of Days Sales Outstanding where we see a better outcome.
What is Gained by Organizations
Enough theory – let’s understand what MDG and OTC integration means for actual practice.
First, the coverage of automation is raised, due to workflows not being stuck anymore due to bad information. In addition, the manual processing is reduced, as less amount of people are needed to spend their days correcting the data of customers or investigating the mistakes in billing statements. The speed of processing order increases considerably, as orders are no longer being stalled due to problems with data. Store as much information about approval-business operations as possible, as conducted approvals are being more effective. Finally, customers notice the positive effects of what is being done, even if they do not realize the reasons for this.
The Importance of Effective Integration
Some organizations have become highly adept at creating effectively functioning systems while others remain stuck with poorly functioning models. The prerequisites for impactful integration have a much clearer definition than previously known, such as:
- Explicitly stating data ownership (not some vague “IT owns master data,” but clear assignment of data responsible persons from each domain).
- Standardizing data processes including and up to customer creation before putting it through automation, rather than after.
- Validation rules should be strict regarding mandatory fields instead of only providing soft warnings to users.
- Duplicate detection should run not just at any time but continuously.
- Implementing master data governance should be done before starting OTC and not the other way round.
Although all the above-mentioned seems not complicated conceptually, it is something that is usually skipped in teams with a team of people who think that they will take care of data later.
The Direction of Development
The increasing involvement of AI systems in OTC processes, which include the application of predictions in credit scoring, automated adjustments of prices, and proper routing of orders, makes MDG essential. At this point, excellent master data systems become the ultimate requirement. Actually, all the abovementioned systems are ineffective if the input data used is defective, and this is the feature that is inherent in all AI systems in general.
So, organizations that connect MDG and OTC together, rather than treating them as two independent efforts, will be ready to make use of new innovations instead of merely seeing how these companies repeat the same mistakes of using old RPA systems.
Takeaway
Automation is only as effective as the data it receives. Those companies that focus on the transactional part of Order-to-Cash, that is order entry, workflow, and approval automation, but disregard the master data behind it, are laying a dubious foundation, even if the interface appears to be perfect.
Incorporating SAP MDG into the OTC process will eliminate this issue from its root. Customer data remains fresh and accurate in all systems, which leads to fewer exceptions, faster order processing, better compliance, and customer satisfaction.
