First steps of RPA implementation
Can we use RPA to identify probable RPA candidates?
With the current unprecedented situation in which the whole world is grappled with COVID-19; it is becoming imperative to make use of software robots to automate business processes if organizations want to maintain their service level. Increasingly, Robotic Process Automation (RPA) solutions are used to leverage the digital workforce and transform the way companies do business.
Though more companies come forward and embrace RPA, it is overwhelming to incorporate such change into the fabric of the company. Every company has its own operating model to implement ICT transformations. Similarly, RPA implementation is conducted over multiple phases which can be defined in a typical RPA lifecycle. Typically, companies adopt RPA by performing a POC to check its suitability within their system whereas some initiate RPA after having the Business Process Management (BPM) tools model their business processes.
In either case, the RPA lifecycle begins with the Process assessment phase wherein companies assess them to locate the potential standardized, rule-based, low-exception rate and high-volume activities to automate. This phase of process assessment helps companies to discover their processes and identify any anomalies. However, process mining(PM) technology can be used to perform this task for you. The PM software uses activities event logs produced by applications to generate a business process workflow with different possible variants of that process along with insights into delays and bottlenecks within the process. This drastically reduces the process assessment time which otherwise is time-consuming. Lastly, PM also determines potential observations for RPA. On completion of process assessment, the following phases are similar to any other software development cycle(Plan-Design-Develop-Test-Deploy) followed within the organization.
So rather than being overwhelmed with RPA, let’s make use of Process Mining to discover current business processes and use its insights to identify probable RPA candidates.
Auteur: Aditi Vaishampayan