Hyperautomate with BPM and RPA for Optimized Operations
With the evolution in the automation industry, business leaders, like you have encountered a dilemma on what is the right technology to invest for end-to-end automation. But, over the years I’ve learnt from my experience in the automation space that no single tool can entirely replace human workforce. In such a scenario, hyperautomation is gaining momentum as it combines disruptive technologies, including intelligent process automation (BPM), robotic process automation (RPA), process mining, artificial intelligence (AI), and machine learning (ML) to create an end-to-end automated solution for business users. Hyperautomation augments human workforce in ways that are significantly more effective than isolated automation tools.
As your first step towards the hyperautomation journey, let’s explore how to make BPM and RPA work together in harmony.
The First Approach: Process-driven RPA
Consider order management, an automated process that uses a BPM system, but lacks integration with the shipping vendor’s system.
In order to complete the “ship order” activity shown in the process above, a user needs to work on multiple systems to complete different tasks:
- Search and open order details
- Copy and paste all the required data from the order management system to the shipping system
- Ship the order and copy tracking number from the shipping system back into the order management system
- Mark order as shipped in the order management system
If you get one or two orders a day, these tasks might not seem like a big deal, but if this is happening multiple times a day, you end up spending too much time on mundane activities!
The idea behind the process-driven RPA approach is that your process keeps running inside a BPM system without any major modifications. You place bots in the BPM workflow to automate repeatable tasks. In the order management process specifically, once the transaction reaches the “ship order” stage, a trained bot can execute all the tasks, eliminating human intervention.
The Second Approach: RPA-initiated Process
A bot is very useful when you have rule-based, repeatable tasks, but what happens when there are data inconsistencies or errors? It is impossible to train a bot on how to deal with all the possible exception cases.
To understand this approach better, let’s look at the trade reconciliation process. This process usually happens at the end of a trading day, and the goal is to make sure that the balance is accurate across two or more systems.
To ensure reconciliation, an agent has to perform a number of different tasks:
- Search and select the customer in the trade management system
- Search and select the customer in the broker system
- Verify that the end-of-day balance in both systems matches
What if the balance does not match in both systems? In this exceptional case, an agent will need to intervene and perform follow-up tasks, such as calling the client and broker to discern a reason for the mismatch.
In such cases, a bot can be trained to perform the daily recurring tasks of checking the balance across two systems, but teaching it to address all exception scenarios, perform follow-ups, and execute follow-up actions may be impossible. This is where a BPM system comes to the rescue.
The idea behind the RPA-initiated process approach is that when a bot has not been trained to handle exceptional cases, a human agent can intervene. When a bot finds anomalies, the transaction can be routed to a human queue, with the help of BPM, so that they can follow-up and resolve the issue manually.
In a nutshell, what your organization needs is the flexibility to use RPA and BPM in tandem with other complementary technologies. Stay tuned for the next blog where I will share how process mining and AI can further accelerate your hyperautomation journey.