Robotic Process Automation Serves as an Intelligent Automation Tool Capable of Increasing Efficiency, Accuracy
Intelligent automation technologies—artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Big Data, cloud computing, and more—are increasingly being leveraged within supply chains to help manage the tremendous complexity associated with getting goods into the hands of the people who buy them. These digital technologies typically provide the data that is used to drive automation. While each brings value, a solution is also out there to help increase efficiencies and accuracy while simplifying the capture and analysis of various data sources: robotic process automation (RPA).
RPA is a software tool that enables the programming, deployment and management of software robots (“bots”) that emulate human actions in performing tasks that require interaction with data, digital systems or software—such as a warehouse management system (WMS), order management system (OMS), enterprise resource planning (ERP) system, transportation management system (TMS) or labor management system (LMS).
RPA uses both business logic and structured inputs to automate routine, high volume and repeatable tasks that were previously performed by humans. It can also be applied to making complex decisions at high speeds. That makes it of particular interest to supply chains.
“Automation is the execution of routine, standardized processes based on certain data inputs,” explained Hein Pretorius, CEO of OnPro, who notes that traditional RPA is the scripted simulation of human keystrokes in a business system, like an ERP.
“For example, you deploy a debtors clerk bot that automates the ‘day in the life of’ their work. The bot logs into the ERP system with a debtor’s clerk username and simply simulates a human, but faster and less prone to error and with no tea break,” he said.
Initially, RPA was deployed in financial and accounting processes, said Steve Mulaik, partner at Argon & Co.
“RPA is great for matching purchase orders with a packing list for the purpose of validating payment, for example,” he noted. “But, as we’ve seen the volume of orders in distribution centers grow with e-commerce, the complexity associated with filling those orders has grown too; it’s growing beyond the human grasp to watch everything and weigh all the variables and tradeoffs. RPA can help here too.”
Companies often believe they need an AI or ML solution to conquer that complexity, Mulaik continued, when often RPA would be more appropriate. In a world filled with supply chain chaos, RPA allows for faster, more effective decision making based on current conditions.
“ML is ideal for trying to determine the root causes of a problem, such as late orders, or to make predictions,” he said. “RPA is straightforward programming of a series of executable steps across systems with some logic built in—if I see this over here and that over there, do the following—that can be layered on top of other applications to pull data from them unobtrusively without changing the proprietary code of the WMS, OMS, ERP, or LMS.”
For those reasons, RPA has the potential to generate significant value within supply chains. Here, a look at how RPA fits into the alphabet soup of digital technologies, where it benefits supply chains, and a pair of deployment case studies.
How RPA complements other digital technologies
Utilizing RPA does not preclude the need to use other digital technologies, such as AI, ML, IoT, Big Data and other solutions, cautioned Saratendu Sethi, vice president of artificial intelligence for GEP. Instead, they all play a role in helping supply chains address challenges and meet their goals.
“RPA is a vehicle to bring all these technologies together to create more automation so that organizations can derive maximum efficiency,” Sethi said. “Whether it is enhancing work productivity or increasing resilience in terms of changing environments, RPA has a key role in bringing all these new technologies
together to derive value.”
By applying RPA bots as a layer on top of other digital technologies, it can be leveraged for collaborative data acquisition, added Rakhi Mullick, vice president of digital transformation for GEP. “There are a lot of intelligent technologies, but they’re often siloed. Adding RPA can automate the extraction of data from siloed systems and generate reports stitched together from different, complex technologies to solve specific problems.”
That capability, in turn, is what generates the return on investment, added Sethi. “RPA is the fastest way to realize ROI, because with RPA you are automating a specific process that is grounded in your direct workflow,” he said. “Anything you are able to implement with RPA allows you to quickly derive value, and then incrementally build on it.” Pretorius agreed, noting that collaboration and data sharing will support RPA to help supply chains achieve their goals of increased business agility, reduced overhead cost and improved end consumer satisfaction. What’s key is identifying patterns that can provide rules and data to enhance the effectiveness of RPA over that of humans.
“The effectiveness of RPA can also be enhanced by adding context data, using AI and ML to identify buying patterns and the driving forces of buying patterns, using automation to drive supply chain buying and shipping decisions, and using anomaly detection to keep a beady eye on proceedings,” he continued.
For supply chains, therefore, “it is all about having the relevant product on the shelf when a customer decides to go to the store to buy it,” added Pretorius. “The closer you get the shipping chain to the demand chain, the better for everybody. This is the ultimate goal to strive toward, and Big Data, AI, ML, IoT, RPA and business pattern automation are key building blocks in this mission.”