How can you avoid RPA errors or exceptions?

When diving into RPA development, think of the process study like a treasure map! It helps you set goals, spot automation gems, confirm your picks, lay down the rules, craft a plan, test the waters, launch your solution, keep an eye on progress, and tweak things for top-notch performance. It's like your RPA adventure guide to success!

The question isn't "How can you avoid?" because that should be granted when working with a professional dev team. The question is "How can you handle?" Well, that depends on: 1. The process and users involved. 2. The type of exception Exceptions have 2 types: 1. Technical: That's on the dev (try/catch and whatnot) 2. Business: This is more sophisticated. You need to learn what processes and measures are in place. But the main questions to ask are: - Who to notify? - How to make it easy for them to handle?

In addition to understanding and documenting the process to be automated, it's crucial to adopt a mindset of continuous improvement and monitoring. After deploying an RPA bot, actively track its performance and exception-handling capabilities. Regularly reviewing and refining the bot’s logic and error-handling mechanisms based on real-world performance feedback ensures it remains effective and adaptable to process changes. This proactive approach minimizes disruptions and maintains efficiency in the automated workflows.

It is crucial to firstly document the process in a flow chart and identify & remove wastages,unwanted and obsolete processes. Clear all manual job’s road blocks before putting it into Automation. The team need to have 1-2 person who know how to execute the job manually, just in case the automation failed or anything happened to job ??

While understanding the process from business owners your goal is to: - Identify possible erroneous activities - Clarifying: What ifs - Possible outcomes in different cases Sometimes we understand the process the way business users are showing it to us. It is almost always better to take control and decide the flow of knowledge sharing with your questions and different path for the process. Asking many questions in the beginning will add value in the end.

Testing and debugging are vital to minimizing RPA errors. By using realistic scenarios and data, you simulate the real-world environment the bot will operate, allowing you to observe its behaviour under typical conditions. Monitoring and logging tools are indispensable for tracking the bot's performance and identifying issues. This proactive approach enables the early detection of problems, facilitating timely fixes and ensuring the bot operates efficiently and accurately in its designated tasks, thus guaranteeing its reliability and effectiveness in automating processes.

When automation solutions are tightly coupled to environment specifics like configuration or dat you need to "test-in-production". Before you throw rocks at me - I mean your test environments must mimic you product production environments as closely as possible. Experience has taught me this, it's probably issues either your production config or data. The ultimate tests besides production are the "acid tests in production". Those initial controlled runs in production that are closely watched and validated.

Regular maintenance and updates are essential for the smooth operation of RPA bots. You can refine and optimise the bot's functionality by actively reviewing performance and adapting to feedback and results. Additionally, staying informed about any changes in the process, data, or operational environment allows for timely updates to the bot. This proactive approach ensures the bot remains effective, minimizing errors and enhancing overall efficiency.

Maintenance and evolution (improving/ expanding) requirements are a key driver in the value formula. The time and cost of maintenance needs to be considered when choosing to automate i.e. your "run costs". These costs need to be considered when evaluating the case for automation. Consider using NPV calculations when evaluating automation investments.

Ideally this is something you discuss with the process owners during the early phases of the automation project. You go over what the automation will do, what its limitations are and what it will not do, as well as what is expected from the process owner. If everyone's on the same page early on, during the user acceptance testing phase of your project it'll only be refresher of what the involved people already knew. During the UAT phase you can show the process owners how the automation works in real-time and go over what is expected from them. As an extra: create a user manual about the automation that briefly sums up what it does and how it can be leveraged by the process owner. Proper documentation plays a key role as well.

If the process owners are not actively sponsoring and promoting the automation efforts go play golf, the outcome will be the same. Active supportive process owners are crucial to setting expectations and landing the change created by automation deployment.

Error handling and recovery mechanisms are good examples of how to manage unexpected errors, however you can work proactively and add also a data validation mechanism, this applies in some scenarios in which the Robot populates data in an specific field and then you make the robot extract the data from that field and now perform a comparison between the value populated against the value extracted, if the results are equal, the robot can move forward, if there is a difference, you can make the robot send a notification to the development team about which Robot failed and in which part of the process, this will helps the team work proactively and resolve a possible issue.

I think main issue is you cannot 100% eliminate them, and you shouldn’t aim to try, it will tied up in knots for far to long. Having a defined process to handle them, in my view is by far your best option Cheers JR ??

Don't go crazy with error handling in RPA! Focus on critical issues that crash your bot. Test those fancy error routines - they might create unfamiliar problems otherwise. Good logging helps too but keep it clear to avoid info overload. Test, target big errors, and use clear error messages.

When automation solutions are tightly coupled to environment specifics like configuration or dat you need to "test-in-production". Before you throw rocks at me - I mean your test environments must mimic you product production environments as closely as possible. Experience has taught me this, it's probably issues either your production config or data. The ultimate tests besides production are the "acid tests in production". Those initial controlled runs in production that are closely watched and validated.