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Exponential Boost to Quality Assurance and Testing Services Powered by Artificial Intelligence

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Jane Brewer
Exponential Boost to Quality Assurance and Testing Services Powered by Artificial Intelligence

The uses of Artificial Intelligence across major sectors today is incomprehensible AI has penetrated almost every running industry today. One such growing segment of the AI applications market is AI-based predictive quality and maintenance or PQM. 

The rise of Big Data Analytics and AI acted as fertilizers to the QA & testing services industry. It took the industry a step forward and put it in the category of emerging technology services. 

It is a relatively new technology area which has been designed to aid companies predict exactly when the issues might occur in a product and advise them on how to identify and fix them. This in return helps prevent problems right before they cause serious damage. 

AI has significantly added value to Quality assurance and testing services in the market today.

The predictive quality assurance and testing services are centered on detecting the quality issues and on improving operational processes that are addressed by accessing and analyzing data. This data is inferred in real time. 

The primary aim of predictive quality and maintenance is to ensure product quality and anticipate maintenance needs. They have, for most period till now, have worked as discrete, distinct technologies. Now with the advent of PQM solutions, these quality and maintenance activities have merged together. 

The major idea behind PQM solutions is to provide a competitive edge to the companies by prioritizing how to allocate their resources, cost, and time in terms of improving product quality and maintaining equipment in a more timely and efficient manner.

Coming to how this process is carried out, we know that a large amount of data is captured  for decision making. The amount of data we are talking about here is simply astronomical. This data consists of both structured and unstructured, and processed in both batch and real time.

A strong example of this is, along with the data that is stored in a traditional database, engineers or workers capture their notes on handwritten pieces of papers, or invaluable information is mined from online chats or email exchanges.

How predictive quality and maintenance services are solving issues in different sectors of the industry:

PQM in Manufacturing

There are times when still, the manufacturers predict machine failures in the assembly lines by digging out the root causes from scribbled maintenance notes or by simply depending on a human subject matter experts. 

These manufacturers have a task of reducing such unplanned downtime and avoid revenue losses that are associated with failed lines. They always have a hovering task of accelerating time to market while also not compromising on the quality.

Along with this, manufacturers need to identify major similarities in parts that predict defects or failures which could occur during manufacturing and assembly, and along with that balance cost with quality once they gain visibility into the life cycle of parts from different vendors. Predictive maintenance helps them in taking corrective actions before the assets break down. This allows the service to be performed anticipating the outcome, and the data of all corrective actions captured for future use.

PQM in the Software Industry

The relationship of software industry with predictive quality and maintenance services is stronger than in any other industry. It has become an essential part of the quality assurance, management, debugging, performance, and cost-estimation exercises.

The primary goal of analytics based predictive maintenance is to minimize bugs, fix any existing ones faster, reducing costs, reducing the strain on senior engineers, and increasing return on investment (ROI).

The sooner a software company detects software problems, the easier and less expensive the troubleshooting and fixing process becomes. 

Conclusion

The method of comprehensive learning-based PQM helps identify, assess and resolve issue as and when they arise or even before they arise.

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Jane Brewer
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