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
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