How Predictive Analytics and Model Deployment can Make Your RPA Even Better

Josh Zable and John Aaron | 5/30/2024

Topics: Model Ops, AI

Robotic Process Automation (RPA) refers to the use of software robots or "bots" to automate highly repetitive, routine tasks typically performed by humans. These tasks often involve interacting with digital systems and applications in a structured and rule-based manner (similar to the way Minitab uses AI). RPA is widely used to improve efficiency, accuracy, and compliance in various business processes.

State-of-the-art RPA now extends beyond repetitive tasks, leveraging "intelligent automation" (IA) to recognize text and make probabilistic decisions under uncertainty. This is where predictive analytics can further enhance RPA's business value.



RPA is typically used for the automation of routine tasks like data entry, copying and pasting, moving files, form filling, data extraction, and processing transactions. Effectively, by automating mundane tasks, RPA reduces the likelihood of human errors, speeds up processing times, and frees up employees to focus for more strategic, value-added activities.

RPA has many applications across different parts of an organization. In finance and accounting, it can help automate invoice processing, accounts payable/receivable, and financial reporting. In customer service, RPA can potentially handle customer inquiries, process orders, and manage customer data. In human resources, it can assist with payroll processing and benefits administration. For supply chain work, RPA can automate inventory management and logistics tracking.


TEXT Recognition IS OFTEN BUILT INTO RPA’s to Constitute “Intelligent Automation"

It is quite common for RPA systems to leverage text recognition to enhance its capabilities, enabling the reading of documents and automation of more complex and unstructured tasks. Text recognition involves processing and analyzing large amounts of text data to extract meaningful information, perform phrase matching, and make decisions. Among other things, RPA tools now embed text recognition capabilities to analyze emails and documents, to understand sentiment of customer feedback, to process and categorize volumes of documents and to monitor compliance.



RPA is dependent on rules to help automate processes, but wouldn’t it be even better if RPAs could extend their capabilities to make reliable decisions under conditions of uncertainty? Predictive analytics offers the capability to extend RPAs in this way. In addition, RPAs now work in complementary fashion with process mining tools (such as SAPs Signavio or Celonis) to identify inefficient or non-conforming processes. The next logical step is to add predictive analytics to expand the capabilities of “Intelligent Automation” to include capabilities such as predicting KPI risk and signaling RPAs to take pre-emptive mitigations.

Example 1: Inventory Management. Imagine a rules-based RPA system that not only automates inventory reordering based on stock thresholds but also makes reordering decisions by analyzing predicted patterns using additional ERP system data. We all know backorders happen because optimizing inventory isn’t simply a function of how much is sitting there. Using a data-driven approach with predictive analytics can help optimize inventory & profitability. Once you have the insight and predictive model, you can deploy the model into RPA, using solutions like Minitab Model Ops®, to make better decisions.

Example 2: Fraud Prevention. The main point of RPA is to be efficient and process faster. While certain rules are able to be defined, like the number of transactions by an account, the size of transactions, etc., predictive analytics can identify patterns in transaction data and customer behavior that can flag or block fraudulent activities. Once you’ve taken the appropriate steps to detect fraud and build your model, deploy the model to RPA to improve the decision-making of the system.


Get More Out of Your DATA AND RPA

Any organization deploying RPA understands the importance of efficiency and “model deployment” because they are effectively deploying rules-based models. By incorporating and deploying predictive analytics along with RPAs, decision making is enhanced and performance will improve.


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About the Guest Blogger

John Aaron (PhD Economics) has been an independent SAP project manager and business transformation consultant since 1995. He is president of Milestone Planning and Research, Inc.

In addition to being a project manager John is a data scientist and has worked extensively in business process analysis, forecasting and applied machine learning. John was an adjunct faculty member at Elmhurst University for five years where he developed courses and taught in the data science, supply chain management and project management masters degree programs.