Affine - Predictive Maintenance Telecom Tower

Predictive Maintenance

The Predictive Maintenance solution enables you to predict when an intervention is necessary to preempt a business disruption.

Telecom Towers and it’s components often face downtime due to multiple internal and external factors such as unsuitable environment, fatigue, manufacturing defects etc. which leads to business disruptions and disgruntled customers. These unplanned outages demand immediate rectifications which may lead to high cost of maintenance.

To cater to the unpredictability of tower components and subsequent downtimes, Affine has developed a state of the art “Smart” product using proprietary Al and machine learning algorithms enabled by big data technology, to accurately predict the propensity of failure of these components, thereby converting unplanned maintenance to planned & scheduled maintenance, thus preventing business disruption.

BENEFITS

REDUCED UNPLANNED NETWORK OUTAGE

Proactive & automated checks of failure of components resulting in reduced network outages and a highly reliable network

HIGHER CUSTOMER SATISFACTION

Improved QoS and lesser call dropouts leading to highly satisfied customers thereby reduced churn

COST
REDUCTION

Cost of maintenance emerging from unplanned failures will be significantly reduced owing to better planning and scheduling

REDUCED REVENUE LEAKAGE

Lesser revenue leakage through call drops emerging from unexpected tower failures

DISTINGUISHED FEATURES

AUTOMATED ALERTS & RECOMMENDATIONS

Enabling faster actions Enabling
faster actions

HIGH PREDICTION
ACCURACY

Al and machine learning models
enabled by Big data technology

QUICK
DEPLOYMENT

Enabling faster output generation
and consumption

EASILY INTEGRATED INTO
EXISTING SYSTEM

Adding value in tandem with
existing decision support mechanism

CUSTOMIZATION

Providing tailored engine
to suit your needs

AUTOMATED MODEL
HEALTH KPIs

Reducing effort spent in
determining model life

ROI TRACKING

enables you to track Rol derived through the solution per components

PAY PER COMPONENT MODEL

You pay only for components
you want to track

DEPLOYMENT ROADMAP

TURBO-CUE deployment process ensures minimal disruption to day-to-day operations

CONFIGURE & INTEGRATE
2 – 4 WEEKS

Engine backend architecture set up as required cloud / on-premise
Data sources assessment and understanding
UI Deployment Feasibility Assessment
Data sampling, clean up and data flow automation

TEST & REFINE
2 – 4 WEEKS

Engine trains itself on provided training data
Affine’s Al team fine tunes the models to cater to various components
Stakeholders approve model performance and give a final go ahead

DEPLOY & MAINTAIN
(PHASED APPROACH)

Failure prediction and alert generation for action
UAT and Feedback enabled for department to highlight prediction errors and issues
Deployment in a phased manner to institutionalize the engine