ML&AI Applications

5G Analytics &Services

MNOs need to know – in real time – how users and applications are impacting the 5G network, or how adverse network conditions or unusual device behaviour is impacting users and applications. They also need to constantly monitor service experience metrics for each type of 5G service. Ampere AI 5G Analytics offer an open, multi-vendor, full-featured 5G Analytics that will interoperate with all 3GPP-compliant 5G Core components. Performing statistical or predictive analytics on collected data and applying AI/ML algorithms
Real-time network Analytics : Measure network load and detect congestion or other types of network performance anomalies.
  • Network slice instance load level computation and prediction
  • Load analytics information and prediction for a specific NF
  • Network performance computation and prediction
  • Congestion information – current and predicted for a specific location
  • Quality of service (QoS) sustainability reporting and predicting QoS change
  • Device Analytics: human users and machines will be connected to the 5G network. 5G operations require monitoring device connectivity, mobility and communications patterns. Abnormal device usage or unanticipated behavior by human users or smart machines could have a negative impact on network performance.
    • User equipment (UE) mobility analytics and expected behavior prediction
    • UE abnormal behavior / anomaly detection
    • UE communication analytics and pattern prediction
    Service QoE: MNOs need to measure and track service experience to ensure that the 5G network is meeting user expectations and Service-Level Agreements (SLAs). NWDAF generated metrics will measure the current service experience by user, application type, device group or geographic location, and predict future changes.
    • Use cases:
    • Service experience computation and prediction for an application or UE group
    • Application service experience computation and prediction

    Ampere AI Digital twins – the future of Supply Chain manufacturing

    • Ampere AI Supply Chain Manufacturing Digital twins uses AI, machine learning, and augmented reality that helps to gain information on how to improve manufacturing processes.  It gives the ability to connect numerous machines, processes, and solutions into one working system. Digital twins generate highly valuable insights that help your organization to grow and meet industry challenges.
    • Challenge : The prediction of manufacturing lead time is a complex problem to solve which is influenced by constituents and constraints of the supply chain. Accurate and timely prediction of manufacturing lead time can assist in planning, streamlining operations, and increasing output and revenue. Poor assessment of manufacturing lead time can result in customer dissatisfaction, inventory mismatches, unutilized capacity and low resilience of the supply chain towards external factors.
    Solutions : The Solution provides a machine learning (ML) based approach to accurately predict lead time at various stages for orders in the manufacturing supply chain industry. This solution provides an accurate assessment of manufacturing lead time along with the intermediate process dates for sub-processes/stages. The solution is validated on data from a payment card manufacturing supply chain. Below are key benefits of the solution.
  • Material Planning: To determine which materials we would need to create a BOM on ERP. Once the BOM is created, this module will tell whether there is any shortage expected in any of the material.
  • Marketing and Sales: AI powered price and Demand forecast
  • Order management: Agile production planning and scheduling
  • Capacity Management : Real time monitoring of OM KPIs and capture potential bottlenecks while executing an order.
  • End- to –End Transparency and faster decision making
  • New order automation: You can run a new order in the digital twin to know expected delivery data, intermediate process dates for sub-processes/stages, material shortage and capacity constrain to improve overall efficiency
  • Customer Care Operation Streamline and automation

    Automate service operations for all touch points interactions

  • Convert touch points interaction to text,
  • Ampere AI model analyzes the text, understand and extract key contextual concepts of the conversation.
  • Match operational actions based on key concepts extracted
  • An action is triggered
  • Enable end to end automation for customer interactions
  • Predict ops flow and ticket resolution sequence
  • Machine learning based model enable right assignment to the right team at right sequence.
  • Enable automate ticket resolution, improve operational efficiency, reduce unnecessary operational time spent
  • Marketing AI

    Use Case:

  • Competitive intelligence for marketing mix Analyze subscribers using known competitors to mobile services and apps
  • Personalized/targeted marketing campaigns with effectiveness/adoption measurement Target subscribers with interest/intention-based personalized messages and offers (vs. socio-demographic campaigns
  • Benefits:

    • Optimize marketing programs/budget: service awareness, retention and conversion/adoption
    • Leverage preferred communication channels
    • Maximize product/service adoption
    • Minimize service adoption cost
    • Avoid spamming subscribers
    • Increase Revenue, reduce cost and improve customer experience

    Predictive Analytics Use Case

    Incident Prediction, Maintenance Prioritization & Fault Description

  • Real-time Fault Prediction via Behavior Outlier Detection
  • Real-time Service Impact Prediction
  • Behavioral Outlier Detection

  • Advanced Anomaly Detection
  • Identify faults
  • Identify abnormalities
  • Predictive Maintenance: Fault Prediction

  • Predict equipment failure
  • Predict the equipment failure location – CPE versus network
  • Predictive Maintenance: Repair Prediction / Proactive Asset Maintenance

    • Predict repair events
    • Identify missed repairs

    Behavioral Analytics

  • Real-time Personalized Targeting
  • eCommerce Attribution
  • Geometric (non-Collaborative Filtering) approach handles high-volume/velocity of data
  • Engagement Process

  • Business problem understanding
  • Use case and data definition
  • Success criteria definition
  • Data transfer complete
  • Data understanding Complete.
  • Data processing and data profiling
  • Exploratory data analysis
  • Feature Engineering
  • Model Building
  • Model tuning
  • ROI analysis
  • Results presentation
  • Align with stakeholders on business objectives
  • Document ‘as-is’ and expected ‘to-be’
  • Document business and technical use case
  • Detail data requirements
  • Define success criteria
  • Ensure technical, commercial, operational feasibility
  • Use case and success criteria discussion
  • Ensure availability of required data. Minimum 2 months of ‘good’ data needs to be available
  • Work with SMEs to understand data
  • Resolve all the data issues and ascertain feasibility of the use case
  • Develop data catalogue
  • Develop a project plan
  • SOW discussion and sign-off
  • Prepare data for analysis
  • Profile data, identify data trends and issues
  • Correlations and multi-collinearity analysis
  • Ensure sample stability
  • EDA results presentation
  • EDA feedback incorporation
  • Feature extraction
  • Identify key features
  • Work with SMEs to qualify features for modeling
  • Model building
  • Identify best-fit model
  • First-cut model discussions
  • Model tuning, incorporate model feedback, iterate working with SME
  • Finalize model
  • Build ROI or TCO models, operationalization model (PMO- FMO)
  • Final presentation Integration in customer environment