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Software Robots in IT Production Support Monitoring and Fixes: An IDEA ?


Describing the Problem Statement / Business Need :-

•        IT Production Support Monitoring and Fixing is a task which runs without any interruption.
•        The concept of Onshore/Offshore partnership is currently prevalent in our IT industry to take care of this round-the-clock task to address any production issue which can happen at real time.
•        Humans are employed to ensure the production Jobs/Processes/Programs are running without any issue. In case of issue they resolve
•        Humans are expensive. They can make errors. We are not machines and naturally feel tired, or can fall sick.
•        Software Robots do not have the disadvantages mentioned in the previous line.


First we need to think, “Goals” are defined by whom and why. It is people like us, who need to understand from the past statistics, and accordingly generate a necessity of a Goal to be defined. Our objective should not be limited to think within the already set Goals, but also to think beyond (if possible). And that is exactly what we are trying to propose out here.



As Production Support needs to be given 24/7, 365 days a year, so we can reduce human intervention with software robots for Production Support Monitoring, and also train the robots accordingly to address error fixing for those errors which are of generic nature.

Additionally, I propose to implement Machine Learning Algorithm within our proposed software robot. As a result the robot will itself predict the repetitive errors and suggest/execute/trigger the required fixing steps. 


Points to be adhered to, for a prototype:-

Among the different types of Machine Learning tasks, a crucial distinction is drawn between supervised and unsupervised learning:

•        Supervised machine learning: The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.
•        Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein.



With the implementation of Pattern-Recognition using Machine-Learning-Algorithm within our proposed software robot, the robot can predict/anticipate probable production issues.

§  The robot will itself predict the not-so-frequent repetitive errors (through an Auto Learning Circuit, which will be responsible for monitoring the Drives/Servers and the respective Websites using the site maps) and execute/trigger the required fixing steps, when the errors actually happen.
§  In such a situation, we can gradually remove the dependency on Humans and Manual-Production-Fix-Documents.

Ø  We will develop automated tool modules for respective error fixing steps, which will be controlled by our primary software robot.

Ø  We will refer to the previous year’s error logs, which will help us as statistics for the known Use-Cases (as per the Manual-Production-Fix-Documents), which needs to be given for the already known repetitive production errors.



BENEFITS (Tangible/Intangible):-

•        Software robots can lower the costs by using automated tools which can work 24/7, 365 days a year, picking up an employee’s repetitive tasks.

•        Usually, one software robot can replace between two to five humans.

•        A software robot is at least three times faster than a human and less prone to committing errors, and thereby more efficient.

•        An example of one Robotic-Process-Automation process is where we take data from one application and match the data against specific rules from another application.

•        Another area of impact is IT infrastructure outsourcing. Here, software robots can keep tabs on critical thresholds -- such as storage capacity -- and take corrective action.

•        Software robots can monitor the thresholds and, under certain threshold breach conditions, robots can even go and address the fix if [they are] configured to do so.

•        Processes ripe for Robotic-Process-Automation include those that are rules-based, definable, repetitive, high volume or span multiple systems.



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