RPA bots can automate repetitive and rule-based tasks, saving time and money for your business. But they also require maintenance and support to ensure they run smoothly and adapt to changing conditions. How can you reduce the costs and risks associated with RPA bot management? Here are some tips to help you optimize your RPA bot lifecycle and performance.
The quality of RPA bot design and development is a key factor that influences its maintenance and support costs. Poorly designed and developed bots are more likely to experience errors, failures, and inefficiencies, resulting in more frequent updates and fixes. To avoid this, you should adhere to best practices for bot design and development such as utilizing a standard framework and naming convention for components and variables, clearly documenting the bot logic and workflow, validating inputs and outputs and handling exceptions gracefully, thoroughly testing the bot in different scenarios and environments, as well as using modular and reusable code to simplify changes and enhancements.
Some of the best practices are as follows: 1. Proper Requirement Gathering: The requirement should be clear enough with all the business exceptions clearly defined. 2. Feasibility Check: Thorough feasibility check of the applications involved for automation. 3. UI vs API: It's always good to have API automation over UI in order to remain tension free from selectors. 4. Proper Exception Handling: In RPA its a key for projects success. 5. Use of Hot keys: Avoid making use of hot keys and hard coded delays. 6. Testing: Thorough testing of the BOT with all the possible scenarios. 7. Proper Categorisation: Based on the complexity, the project should be categorised as Low, medium or high complexity.
Leverage every mechanism possible to centralize the management of common entities across the fleet of automations. These entities could be global variables, connections to APIs, or UI control specs, among others. For instance, if you can apply tags or labels to automations in the central control plane that indicate which applications are used by that automation, you can be more prepared to anticipate maintenance needs at scale if or when apps incur any change (UI or API). Things like central object repositories can greatly help as well in pushing version updates across many automations that address the same applications.
To reduce maintenance and support costs for RPA bots, prioritize robust initial development to minimize errors, implement regular testing and monitoring for proactive issue identification, automate maintenance tasks through scripting, and conduct periodic reviews to identify optimization opportunities. Additionally, invest in comprehensive training for bot operators to enhance troubleshooting capabilities, and establish clear documentation and knowledge-sharing practices to facilitate efficient issue resolution. Continuous improvement and a well-organized support structure contribute to long-term cost reduction for RPA bot maintenance.
1. Regular Audits & Optimization: Regularly audit & optimize the workflows to reduce the load on bots, making them more resource-efficient & lowering maintenance costs. 2. Exception Handling Improvement: Enhance exception handling mechanisms within the bots to address issues more effectively. This can reduce the occurrence of errors and minimize the need for manual intervention, lowering support costs. 3. User Training & Support Documentation: Invest in user training to ensure that end-users understand how to interact with the bots. This can reduce the number of support requests & increase user self-sufficiency. 4. Automated Testing: It can significantly cut down on maintenance efforts related to fixing post-deployment issues.
Bot monitoring and reporting tools can help reduce the maintenance and support costs of RPA bots by providing features such as dashboards and alerts to visualize and notify the status and activity of your bots. Logs and analytics can be used to diagnose and troubleshoot bot errors and failures, while metrics and KPIs can be used to evaluate and improve bot productivity and quality. Additionally, feedback and suggestions can be used to enhance bot functionality and user satisfaction. Implementing such tools can help you identify and resolve issues before they escalate, as well as optimize your bot utilization and efficiency.
A proactive bot maintenance strategy can help you reduce the maintenance and support costs of RPA bots by preventing or minimizing the impact of bot disruptions and breakdowns. To achieve this, it’s important to perform regular bot audits and reviews to check for compliance, security, and performance issues. Applying timely updates and patches is also necessary to fix bugs, vulnerabilities, and compatibility issues. Additionally, periodic bot training and retraining should be conducted to ensure the bots can handle new or changed processes and data. Furthermore, planning ahead for bot scaling and migration can accommodate changing business needs and demands.
Finally, you can reduce the maintenance and support costs of RPA bots by leveraging bot governance and collaboration tools. These tools can help ensure your bots are aligned with business goals, policies, and standards. Moreover, they can facilitate communication and coordination among stakeholders and users. Some of the advantages of these tools include a centralized repository and inventory to store and organize bot assets, a role-based access and permission system to define who can create, edit, deploy, and use bots, a version control and change management system to track bot changes and releases, as well as a feedback and communication system to share information about your bots.
The more automation developers can think like software engineers in designing and developing their automations, the greater the potential for long term success and reduced maintenance. This encompasses topics like standardized frameworks, best practices for exception and error handling aligned to the business needs, and certainly choosing the most robust approach to data movement across apps. Programmatic or (back end) connections should be prioritized wherever possible. The architecture of the automation from a workload perspective should also be considered (i.e. bulk item processing, single transaction, etc).