Our most recent release, Houston 2.0, marks a huge step forward in our mission to offer businesses everywhere end-to-end automation management and orchestration at scale. Because it is made to handle extensive deployments, this new release is simpler to use than ever before.
With the addition of new features that enable dynamic scheduling, SLA-based triggering, process self-recovery, and much more, this launch takes our acclaimed multi-vendor automation optimization solution to an entirely new level.
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It's a terrific idea to use the advanced control module to:
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Increase operational effectiveness and scheduling.
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Lower operating expenses and complexity.
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Eliminate bottlenecks to increase the ROI of your automation initiatives.
Automated and Dynamic Scheduling
Processes can be sped up and made more efficient with the aid of automation initiatives, but as you scale up and add more solutions, like RPA processes, to your automation platform, the complexity of the system may rise.
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Scheduling processes can become complicated, particularly when you have items with various degrees of criticality, changing volumes, and diverse frequencies. As your automated portfolio expands, integrating recurring financial operations with ongoing operational activities can become even more challenging.
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Automation teams must manage complications while also being tugged in several ways by conflicting demands. For instance, they can be instructed to optimize the timeline, guarantee high license utilization, and satisfy business stakeholder expectations.
Finding a balance between optimum scheduling and fulfilling business stakeholder satisfaction demands constant manual efforts.
These issues are resolved by dynamic and SLA-based scheduling, which also increases operational efficiency, customer happiness and confidence, and capacity-based resource consumption.
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Let's look at how we approach and resolve each issue:
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The scheduling process is complex and time-consuming
The time and money required to scale automation is frequently significant, which has an impact on ROI. The application scheduler is the ideal solution to streamline scheduling for your organisation and make sure that all key stakeholders are satisfied with the outcomes. The platform will automatically start the process following the technical requirements (for example, what bot/resource process can operate on) and the business stakeholder requirements (SLAs) that you provide. At the same time, we will optimise your schedule and triggers while taking into account any other process conditions.
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Low utilization of licenses and resources
Process execution is challenging and maintenance costs rise as a result of RPA suppliers' platforms' lack of dynamic scheduling functionality. Our platform can optimise how processes are carried out, how robots and resources are used, and how licences are used. By scheduling your automation solutions and dynamically triggering them as necessary, we can help you reduce your maintenance expenses.
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No guarantee if that process can be executed on SLA
Although processes are frequently planned to satisfy business stakeholders' expectations, there is no assurance that they will be carried out properly and on time. To address this problem, we make sure processes are carried out in accordance with SLAs and alert users and business stakeholders when SLAs are not satisfied. More details about the notification features are provided below.
Self-Recovery of Processes
Monitoring process runs is crucial since it ensures that all runs are completed on time and yield the desired outcomes. When performed manually, it is frequently a laborious procedure that takes a lot of operating effort. Technical error handling documentation gives first-line assistants the knowledge they need to respond to a failure or issue.
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Based on this documentation, operators are aware of what to do and when to perform it. To guarantee that execution is carried out effectively and in line with organizational expectations, these tasks can be time-consuming and frequently necessitate restarting the process or opening an incident for automation help.
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Automated first-line assistance and monitoring lead to improved operations and support, shorter resolution times, fewer issues, and process self-healing.
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Let's look at how we approach and resolve each issue:
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Monitoring and alerting are done manually
A lot of manual work is frequently involved in automation processes, including checking that various processes were completed and monitoring them.
Let our execution and recovery features handle all the work for you rather than wasting time manually monitoring processes and spotting errors. You'll be able to focus on other crucial activities by freeing up your time by automating these processes.
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First-line support is time-consuming
After spotting a probable failure, the monitoring team's next move is to look into its cause and effects. By doing this, we can identify what must be done to resolve the issue and stop it from occurring again. First-line assistance normally handles this. To find out what went wrong, they'll start by looking at any errors in the logs. They will determine from that data if the process needs to be resumed, whether an incident needs to be launched, or whether no action is required.
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All necessary duties will be completed for you by our AI. If a process needs to be restarted, we may try it again once, twice, or a few times on the same system. You have the option of setting global or specific execution and recovery settings for each process. All of the time-consuming first-line work will be handled by us for you.
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Unable to set conditions for when the business considers the process successful
Incorporating error management within the process's code results in dependencies and a protracted lead time for modifications. The control module handles and defines errors and failures, which can help you save time. On the platform, all error definitions and handling can be pre-set and modified. When a process fails once, twice, or more on the same computer, you can choose whether it counts as an incident. These configurations can also be configured globally for all processes or per-process. Your error handling is defined and managed in one location this way.
Automatic and SLA-Based Notifications
In coding, error notifications are frequently included. Although some vendors send error alerts to operational staff, the notifications are occasionally unable to handle business situations or successfully interact with consumers.
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We provide efficient operations and support, quicker support response times, and visibility for business stakeholders thanks to our automated notifications and issue generation. It offers an interface for business stakeholders to quickly evaluate the performance and status of the various deployments thanks to automatic incident notification.
AI-Based Anomaly Detection
Our latest Houston release includes the first-ever AI-based error prediction capability that helps you to detect, prioritize and classify errors to keep your processes running smoothly and with significantly lower maintenance costs. This delivers value to customers in three ways:
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1) First, with Anomaly detection, we can determine which errors are important, fall outside of the predicted bound range and hence allocate resources appropriately.
2) Also, all errors are not equal. Our AI algorithm helps you determine which error has a higher deviation and frequency vs. the bound range and hence requires greater attention.
3) Our latest release also helps you classify errors to better understand what fixes should be put in place.
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This is the first-ever solution that helps you eliminate alert floods and alarm spam. Rather, it helps you to focus on problems that matter, enabling you not just to remediate errors but find the underlying cause to fix them. We are the first-of-its-kind multi-vendor Automation Optimization Platform that optimizes your end-to-end automation lifecycle.