Abstract
Robotic process automation (RPA) is a fast emerging technology that is adopted globally to automate business processes. Many service and financial industries have adopted RPA as a competitive strategy to automate back-office operations departments. RPA technology is proving to be competitive as the market value for the technology continues to grow in millions USD.
With RPA technology, the robot mimics the human user by completing the same structured tasks the same way a human would. This method of automation is advanced as the technology can be layered on existing technology infrastructure, without requiring integration to legacy systems. This results in faster implementations and quicker return on investment. The benefits of RPA include cost reduction through the automation of back- office processes, improved accuracy and quality as the robot performs tasks in a structured way, and improved customer satisfaction as the robot is available to work in a 24/7 environment to meet customer needs.
However, the literature review indicates a gap in the standardisation of RPA implementations. Organisations have in the past adopted different methods when identifying and selecting processes that can be automated through RPA. Furthermore, the implementation journey is not standardised, meaning an effective sequence of the implementation journey is still unclear. These non-standard practices create a risk for organisations where incorrect selection or implementation may result in the technology being ineffective. Time and money may be lost when an unsuitable process is selected for RPA, or incorrect implementation practices may lead to rework or abandonment of the RPA journey.
This research aims to provide a guideline that standardises the RPA implementation process. The guideline details the characteristics of manual processes that can be automated through RPA. The guideline further details the implementation guidelines for RPA. A quantitative study was conducted to collect data on RPA implementations in the banking industry. The data was analysed and presented as a standardised guideline that could be used for future RPA implementations.