RFT – Challenges and Opportunities to Improve With Visual Automation

The benefits of computer vision

In the highly competitive landscape of FTTP, computer vision solutions help operators significantly increase RFT rates through the immediate detection of anomalies and poor-quality operations, enabled by the automated analysis of visual data captured and uploaded by engineers or auditors in the field. The technology can be used to ensure quality compliance across end-to-end full fibre networks from the fibre exchange to the premise, covering both active and passive network equipment.

Potential use cases include verifying compliance against pre-defined quality standards through to detecting network anomalies or issues across an end-to-end full fibre network, from equipment in the fibre exchange to cabinets and chambers housing primary and secondary nodes, to splice boxes and optical sockets at the customer premise.

Some specific use case examples outlined below.

Shared Infrastructure Access – many FTTP providers are reliant on access to third party ducts, poles and chambers to deliver and build out their own networks. In a shared infrastructure model, adherence to strict quality standards and compliance with photographic evidence is mandatory, such as Openreach PIA in the UK, NBN Co in Australia, or ONX in Canada. RFT is essential as non-compliant photos or sub-standard work can lead to delays and unnecessary costs due to resubmissions or additional engineer visits.

Pre-RFS Quality Control – build quality is often compromised as networks are rapidly deployed. Computer vision can be used to ensure build standards are met prior to network sign-off, avoiding delays in handover and accelerating RFS network availability. Relevant for equipment across end-to-end fibre network, automating real time quality checks via AI, significantly reduces rework, as well as the time, effort and costs needed to complete desktop and site surveys.

As-Built Network Reviews – AI-powered visual automation can help with retrospective network documentation and auditing of as-built networks. Whilst reviewing photos manually is resource intensive, AI-powered analysis can scale up reviews enabling post build audits to be completed across an entire network footprint, which would otherwise be limited to a very small sample of the network estate.

Customer Installations – issues with light levels, broadband speeds, reinstatement or even poorly mounted ONTs can result in service delays, issues or unhappy customers, where an additional site visit may be required. Using computer vision to automate install health checks, operators are able can detect any corrective rework needed on the day of install, as well as guarantee the best possible install experience for their customers, essential for maximising NPS feedback.

Network Faults and Maintenance – empowering field agents to resolve faults on the first visit is a key objective for all operators, eliminating the need for an additional site visit or truck roll. Crucial for meeting SLAs, reducing time to repair and ensuring customer satisfaction, AI-powered visual automation solution is able to identify potential issues and propose step by step instructions to resolve, particularly helpful for complex issues or newer engineers that have less experience.

Inveniam AI – a better way to benefit from computer vision

Inveniam’s AI-powered managed solution integrates computer vision and machine learning capabilities to enhance field service operations, transforming raw data into real time actionable insights. With every stage of the computer vision life cycle covered, we make it easy for FTTP providers to deploy transformative computer vision solutions, without having to invest in their own dedicated AI resources. We provide both pre-built, packaged and custom use cases, specific to each customer requirements. Some examples of pre-trained cabinet detections are outlined below.

To support RFT transformation, Inveniam’s managed solution offers: 

1. Real-time or historical anomaly detection – By leveraging computer vision technology, we can immediately identify potential network issues. If it an issue can be detected by the human eye, it can generally be automated through our platform, enabling timely intervention and rectification.
2. Actionable insights – The AI-powered solution not only predicts potential problems but also provides actionable intelligence and corrective recommendations. Field technicians can access this information promptly, enabling them to address issues accurately and efficiently.
3. Elimination of human error – Visual automation minimises the reliance on human judgment and subjective assessments. Instead, it employs algorithms to analyse images objectively, reducing the risk of human error.

Closing thoughts

RFT is more than just a metric or concept — it encompasses a commitment to achieving the highest quality standards and fault-free operations. In an industry where seamless connectivity and minimal downtime are critical, RFT ensures it remains that way. Moreover, this concept emphasises proactive planning and quality control measures. This helps to significantly minimise expensive rework and accelerates the innovation in telco by looking for new ways to optimise, improve, and develop better, more efficient workflows and systems.