Reducing Network Outages and Improving Performance with Predactica's Explainable AI Solution: A Case Study in the Telecom Industry

Challenge

The telecom company faced several challenges related to network outages and machine learning model interpretability. Frequent network outages were impacting customer experience and causing downtime for business clients. The machine learning models implemented to optimise network performance were opaque and difficult to interpret, hindering the company’s ability to make informed decisions. The complexity of the telecom network and the large number of variables involved made it difficult to identify the root causes of the network outages and develop effective solutions.

Solution

The telecom company implemented an XAI solution from Predactica’s AL/ML platform to address challenges related to network outages and machine learning model interpretability. The XAI solution provided visualisations and explanations of the factors most important in predicting network performance, allowing network engineers to identify key factors contributing to network outages and optimise the network accordingly. This improved the interpretability of the machine learning models, built trust in the AI, and improved the overall efficiency of the company’s operations.

Business Impact

  1. 15% increase in operational efficiency
  2. Improved the interpretability of the machine learning models

Organization Overview

The telecom company is a large US organisation that provides telecommunications services to customers and business clients. They were facing challenges related to frequent network outages and difficulty interpreting their machine learning models. To address these challenges, they reached out to Predactica and implemented an XAI solution from the AL/ML platform, which helped them reduce network outages, improve network performance, and increase operational efficiency.

Context

The company was experiencing frequent network outages, which were negatively impacting their customer experience and causing significant downtime for their business clients. The company had implemented machine learning models to optimise their network performance and minimise downtime, but they were struggling to understand why the models were making certain recommendations and decisions.
The company decided to implement an XAI solution from Predactica’s AL/ML platform to gain better insight into the factors and features that the models were using to make their decisions. The XAI solution analysed the machine learning models and provided visualisations and explanations of the features and factors that were most important in predicting network performance.

Process Undertaken

  • Analysed the machine learning models: Predactica’s XAI solution analysed the machine learning models that the telecom company had implemented to optimise their network performance. This helped identify which features and factors were most important in predicting network performance.
  • Provided visualisations and explanations: The XAI solution provided visualisations and explanations of the factors most important in predicting network performance, helping network engineers understand why the machine learning models were making certain recommendations and decisions. This improved the interpretability of the models and helped build trust in the AI.
  • Allowed for proactive optimization: Armed with insights from Predactica’s XAI solution, network engineers were able to identify key factors contributing to network outages, such as high network traffic and equipment failures, and proactively address them before they caused downtime. This led to a reduction in network outages, an improvement in network performance, and an increase in operational efficiency.
Using this information, the network engineers were able to make informed decisions about how to optimise the network, such as upgrading equipment in high-traffic areas and prioritising repairs for equipment with the highest failure rates. They were also able to more accurately predict network performance and proactively address issues before they caused downtime.

Conclusion

Increased operational efficiency: The XAI solution improved the interpretability of the machine learning models, allowing network engineers to make informed decisions and optimise the network more efficiently. This led to a 15% improvement in operational efficiency, reducing costs and increasing profitability for the telecom company. This had an indirect impact in reducing network outages and hence improved performance.