by Ivona Crnoja and Pierre Col
Robotic Process Automation or RPA is probably this years’ most hyped term in the digital world – as you would say in France: RPA is currently “en vogue”.
And that’s for a good reason. RPA is an innovative technology, automating business processes using software robots, that perform tedious and repetitive tasks – error susceptibility equals zero.
Imagine the magic!
Thanks to robots the human employee does no longer have to waste time on nerve-wrecking, time-consuming and manual activities. The result is relieved and happy employees, with extra time for tasks that really matter, improved work results, adding an extra pinch of value to your employees’ daily hustle.
No wonder, RPA has already proven to be an intelligent and efficient driver to push digital transformation in enterprises, by increasing productivity, reducing cost, and ultimately leading to revenue and business growth. Hence, RPA is more than just a buzzword, which is also why its market size is expected to hit 3.97 billion dollars by 2025.
For a deep dive into “What is RPA” have a look at this 4-part series on “Intelligent RPA”.
Next level: Making RPA Intelligent
So, we learned that digital bots automate repetitive and routine tasks – by this significantly increasing productivity and employee and customer satisfaction.
On other terms however, these bots are not able to draw any conclusions from their actions – they are not ‘intelligent’. This is where Machine Learning (ML) comes into place.
Connecting Machine Learning with RPA is meaningful whenever business automation is pursued in an integrated and strategic manner. In order to automate business processes efficiently, future-oriented and strategically, the combination of these two technologies is essential. By infusing intelligence into RPA, hence combining Machine Learning capabilities with process automation, we can design an advanced form of RPA – a bot that is able to analyze, comprehend and draw conclusions from both structured and unstructured data. This powerful symbiosis is consequently able to not just process, but effectively use data. This newly created Intelligent RPA now analyzes data before acting on it, continuously learns from data, becomes more intelligent over time, and is able to make smart decisions based on previous learnings.
Automating processes with the help of RPA and ML therefore especially makes sense whenever huge amount of data needs to be processed, analyzed, compared and structured. While ML covers the task of thinking and learning, RPA executes. ML functionalities that come to play in connection with RPA are technologies such as image- and speech-recognition or document information extraction for example.
SAP has just recently entered the market with an integrated SAP Intelligent Robotic Process Automation solution, combining it with existing SAP Leonardo machine learning capabilities.
One example of how SAP Leonardo is combining these two powerful forces is the automation of accounts payable.
A Real-Life Example: Automating Accounts Payable with SAP Intelligent RPA and SAP Machine Learning
Let’s take a very common business case. Worldwide, every day, millions of accountants receive invoices from their suppliers which notify about payments to be made.
30 years ago, these documents came printed on paper made out of wood and water, put into envelopes and delivered by a post agent on a bike.
Today, thanks to the digital transformation, invoices are reaching the accountant as a PDF file attached to an email. What an improvement!
The bad news is, the whole process around it remains the same.
- Processing the invoices is very time-consuming and constitutes a rather dull task for accountants.
- These highly qualified employees must invest a significant amount of working time to read the invoices and select and extract the relevant information manually – name of the supplier, invoice number, total amount to be paid, payment date and so on.
- Following the manual extraction of the data, employees open the accounting module of their organization’s SAP ERP (if the organization is one of the best run of course 😉), they enter the data into a form and click a button to validate the entry.
End of the game. If we may call that a game. The manual processing of an invoice takes a couple of minutes per invoice. For millions of invoices per day worldwide.
So what did we do at SAP to simplify this process? Easy—we automated as much as we could. With the help of intelligent RPA!
- In the first step of the process an RPA bot opens the inbox folder of the accountants’ mailbox and identifies the emails related to invoices with an attached document.
- The bot takes the email attachment from the accountants inbox, reads it, and in the next step sends it to an intelligent Machine Learning application called Accounts Payable, where the document is being processed and the invoice results obtained.
- The application uses Machine Learning capabilities that are trained to process every kind of invoice, from any kind of company in the world; automatically extract the data; and hand it back to the RPA bot.
- In the last step, the bot creates the supplier invoice in the SAP S4/HANA system, where all running jobs can be further monitored by the employee (due to the RPA bot being scheduled).
End of the game. Yes, for an RPA bot using Machine Learning capabilities, this process is a quick and simple game, taking only a few seconds per invoice to be played. And more than that, it is free of human errors.
Now, multiply 1 minute per the millions of invoices sent in a day worldwide, and you can imagine the ROI which can be brought about by the smart combination of RPA and Machine Learning technologies. This combination ultimately frees up employees time, so they can focus on tasks that really matter.
See SAP Document Information Extraction in action in this short video, explaining how the Intelligent RPA bot is created, deployed and monitored:
And this is just one out of hundreds, if not thousands, of examples of what intelligent technologies available now at SAP can deliver. Stay tuned!
Bridging the Gap between Academia and Industry
While SAP Leonardo has several machine use cases like the one described above in action, continuous research is necessary to drive progress in this field. The SAP machine learning research team therefore focuses on solving tough machine learning problems with practical applications across multiple domains, such as text, image, or video. The emphasis lies on generic ML techniques that have potential to impact a wide range of business use cases across finance, procurement, logistics and travel management.
Through multiple bilateral collaborations with the world’s best research institutions, SAP Leonardo research enlarges its expertise and actively contributes to the continuous development of the machine learning and deep learning solutions offered by SAP Leonardo Machine Learning. Once ready to scale, the developed machine learning models can turn into new enterprise solutions or empower SAP’s existing ones.