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Ensuring RFT field Operations Through Computer Vision: Opportunities for UK Full Fibre Network Providers

2023-09-15 14:14:34

 Ensuring RFT field Operations Through Computer Vision: Opportunities for UK Full Fibre Network Providers



• RFT operations are fundamental for all operators building and running FTTP networks

• Computer vision is a game changing technology that can help operators enhance RFT operations

• Learn which are the immediate use cases that can benefit from computer vision

Market landscape

The race is on for UK FTTP operators to complete their network deployments, whilst juggling the challenges of build contractor shortages, network overbuild, rising cost bases and most recently consolidation, as market dynamics and insufficient scale proves unsustainable for some players.

Network deployments are often plagued with quality issues, leading to roll-out delays, additional costs and disruption for residents. In overbuild areas, delays lead to a detrimental impact on ROI when customers make the switch to full fibre using a competitor.

Once the network is ready for service (RFS), there is also an ever-increasing focus on delivering the best possible installation journey, not just to differentiate, but to better manage the cost base, accelerate time to revenue and ensure great Net Promoter Score (NPS) feedback when engaging with customers.

Enhancing RFT through computer vision

Right First Time (RFT) operations have become the key metric of choice in helping operators address these challenges. It measures whether tasks are correctly completed to the required standard in the initial attempt, without the need for rework. Whether applied to network build, customer install or resolving network faults, RFT reduces revisits, operational costs and time spent on each task.

Computer vision solutions are being deployed to help operators significantly increase RFT rates through the 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. Having successfully deployed with FTTP providers in Europe, we see immediate benefits for UK alt-nets.

Openreach PIA: Managing Openreach’s PIA compliance process is a pain-point for many FTTP providers reliant on access to Openreach’s ducts, poles, and chambers to deliver and build out their own networks. Similar to other markets, adherence to strict quality standards and compliance with photographic evidence is mandatory. Using computer vision, operators are able to capture noncompliant photos or sub-standard work prior to sending to Openreach, avoiding delays and unnecessary costs that come with resubmissions or additional site visits.

Network Build Audits: As-built network auditing and documentation is often incomplete, with many operators seeking to improve their quality control processes. Computer vision can be used to ensure build standards are met, 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.

Customer Installs: Issues with network availability, splicing, broadband speeds, reinstatement or even poorly mounted ONTs can result in service delays, issues or unhappy customers, where an additional site visit maybe required. Using computer vision to automate install health checks, operators can detect any corrective rework needed on the day of install, as well as guarantee the best possible install experience for their customers.

Inveniam AI – a better way to benefit from computer vision. Inveniam AI is a fully managed solution that integrates computer vision and machine learning capabilities to enhance field service operations, transforming raw data into real time actionable insights when engineers are in the field. With every stage of the computer vision life cycle covered, we build, deploy, and manage transformative computer vision solutions without our customers having to invest in their dedicated AI resources.

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