How to Prevent BOT Failure?

We all know that now day's RPA is more and more seen as a silver bullet for many of the difficulty’s businesses face. Some companies see it as a direct path to reducing operating costs, while others view it as a way to enable consistent quality and a better customer experience. But remember implementing an RPA solution isn’t as simple as throwing a few BOTs at a manual task. So, whether we have been working with RPA for some time or are just exploring its usefulness for business, we have well-thought-out RPA BOT failures as a challenge to our program’s success.

There is no doubt that BOT has become an integral component of our business & production but do have we learned the art of dealing with them? Surprisingly we don’t have proper answer for the same. There are tools available in the market to govern & monitor work queues, schedules & execution rules. But the problem with these tools is that they are reactive in nature. They act only after the failure has already occurred & production has been affected. Let’s take a deep dive and check the types of BOT failures, limitations in BOT monitoring systems and what corrective/preventive measures we can take to mitigate these failures.

Types of BOT Failures…

  1. Normal Failures: This occurs when a BOT stops executing for an unexpected but deceptive reason. For example, imagine a BOT designed to retrieve data from an ingest engine via a series of 10 steps. When it reaches step 8, the partial requisite information has been relocated to another position, so the BOT is unable to continue until step 8 has been updated with the data’s new location in the ingest. 
  2. Predicted Failures: These failures are already predicted! This means the developer is aware of its possibility and can write code to accommodate it. For example, if login credentials for a given system expire every 90 days, the developer can write code to automatically send an email to the IT group requesting new login credentials when the current ones fail.
  3. Noiseless Failures: This is the most challenging failure; it doesn’t provide notification of a problem, and the runtime log indicates no errors. This is an invisible problem and cannot be solved until someone recognizes that the automation has failed! Luckily, these errors are rare, happening only about 1% of the time. For example, All the data points are provided to BOT via ingest mechanism to update five webpages of a particular site. The BOT completes three tasks, but by the time it reaches to the fourth one, it gets a URL having special characters that it does not make out and gets into loop to resolve the issue. There was no BOT reported errors but gives an impression to end-users that BOT is not working or got hanged. This is what makes noiseless failures the most challenging to address.
Let’s also check the limitations that we have in our BOT monitoring systems before talking about the possible measures to address the issues.
  1. Our available centralized BOT monitoring systems can't predict the future of BOT.
  2. Enterprise level organizations have multiple BOT platforms that lead to multiple monitoring systems that are challenging to integrate.
  3. Most of the BOT related data points like data collections, performance monitoring etc. are available in distributed systems.
  4. Alerts are triggered only after the failure has happened that leads to production downtime.
  5. When BOT fails, it takes time to understand what caused the issue before fixes can be made, delaying remediation efforts.
Normal BOT Failures can be addressed at coding level. The development should be done in such a way that in case BOT encountered any error it should raise a flag via notification and include a log file that details the reason it failed. This will help add context to what went wrong, and it quickly points developers to the necessary solution. Resolving Predicted Failures can largely be done during the planning stages of an automation. Proper planning, combined with experience, often illuminates potential exceptions and faults in a process flow. Noiseless Failures do happen, often due to an unanticipated variable in a new project. Unfortunately, Noiseless Failures are usually discovered by the party most affected by the failure. While they’re extremely rare. There is no standard procedure to fix the problem but the good part is that usually once this is discovered, it can be quickly remediated.

Prevention at Centralized BOT Monitoring Level
  • Create Automation Islands: Organizations within them create automation islands — using multiple technologies, as well as different RPA and artificial intelligence (AI) platforms, with limited standardization. BOTs are reactive in nature and don’t alert in time to avoid problems. Wouldn’t it be great, though, if we could predict BOT failure — or even prevent it? It’s happening already in the manufacturing industry, where robots handle high precision machinery. We need a similar system for software BOTs, one that can help us stay on top of potential problems while continuously evaluating BOT performance.
  • Build a Predictive BOT monitoring & Forecasting System: A comprehensive framework can help prevent automation investments from failing and harming Organizations. Think of it as a predictive monitoring system for BOTs. It can not only evaluate and forecast a BOTs future performance but also its failure. Using data analytics, machine learning and deep learning models, it’s possible to gather insights that will open up new operational efficiencies, productivity gains and cost benefits.
Model of Building a Predictive BOT monitoring & Forecasting System

We need to work on Data Cumulator, Preparatory Engine, & Forecaster Engine to have this kind of system in place. Some of the leading IT Consulting & BOT development companies have explained this quite nicely. 

The first step towards building a Data Preparatory engine is to gather all BOT performance related data and stored the same in a Data Cumulator. Once the data is stored, it is cleansed, reduced and fed to a preparatory engine, where it is pre-processed to prepare for either Seasonality analysis or Trend analysis. If the data is influenced by seasonal factors such as months, quarters, or year then choose for Seasonality analysis. It is fixed in time & known as periodic time series forecasting. But if the data is historical, go for trend analysis. It predicts what will happen in the future.

The Data preparatory engine output is pre-processed and transformed data then be used for forecasting. Forecasting can be achieved by using a forecaster engine that includes any of the available forecasting models like Machine learning that uses techniques like SMA, SES & ARIMA or Deep learning that includes RNN with LSTM cells model or Ensemble that combines two models using either machine learning or deep learning as a base model and the other as a high-level model.


Robotics Process Automation began as a well-structured solution that organization used in their existing platforms to improve operational aspects without classy upgrades. Today, RPA — together with AI technology — brings new methods to improve organization’s effectiveness and performance. But disbeliefs, and unanticipated BOT downtime's, start negatively impacting their business. Already deployed BOT monitoring system lack the capability to predict the future. So now its time to adopt a predictive monitoring system that offers a three-pronged approach: Continuous BOT performance surveillance, an integrated environment that collects, processes and analyses data and then uses that data to forecast bot health. And finally, leverage a multi-model and dynamic approach to make forecasting efficient.

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