A global technology infrastructure company sought greater automation across network operations while retaining accountable control. Alarm patterns, topology, field accessibility and traffic growth made isolated model outputs difficult to act upon. Marketways connected anomaly detection, maintenance, capacity forecasting and AI reliance to the operating decisions made during normal service and disruption.
The engagement objective
Through the initial discovery, Marketways defined the objective: connect anomaly detection, maintenance, capacity and AI reliance.
How Marketways translated the problem
We began with a practical question: Which signals indicate a developing fault and which customers are affected? The first analysis used alarms, topology, configuration, performance, tickets and outages.
That evidence could not be read in isolation. Alarms are correlated and topology changes the meaning of an individual signal. Remote inspection quality and field accessibility vary by asset and environment.
Correlated alarms could multiply the apparent evidence for one failure. Topology, shared causes and operator response were modelled so automation did not turn duplicated signals into false confidence.
We did not judge each component by its isolated KPI. We examined how people, assets, decisions and constraints affected one another, then used the evidence to test whether an apparent improvement would strengthen the complete system or merely move cost, pressure or risk elsewhere.
How the engagement developed
The initial work on network anomaly and outage intelligence exposed dependencies with field maintenance and tower inspection, network demand and capacity forecast, ai-enabled operations reliance review. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Network anomaly and outage intelligence: Shorten detection and prioritise restoration by consequence.
- Field maintenance and tower inspection: Improve safety, coverage and restoration with fewer unnecessary visits.
- Network demand and capacity forecast: Time investment and optimisation around actual service demand.
- AI-enabled operations reliance review: Use AI where it improves the complete operating process.
Evidence we examined
- Alarms, topology, configuration, performance, tickets and outages.
- Asset condition, images, alarms, work orders, routes and skills.
- Traffic, cells, customers, devices, quality, geography and planned change.
- Use cases, test sets, production monitoring, overrides and incidents.
Industry conditions we accounted for
- Alarms are correlated and topology changes the meaning of an individual signal.
- Remote inspection quality and field accessibility vary by asset and environment.
- Aggregate traffic hides location, application, device and busy-hour constraints.
- A model can be accurate in testing but fail after product, traffic or language patterns change.
How our engagement contributed to business impact
We connected every method to a decision and a business measure. The organisation could assess the engagement through operating results as well as model performance.
- Network anomaly and outage intelligence
- Method: Machine Learning & Predictive Analytics, Data Foundations & Business Intelligence.
- Evidence: Alarms, topology, configuration, performance, tickets and outages.
- Decision supported: Shorten detection and prioritise restoration by consequence.
- Impact measure: Detection lead time, restoration, false alerts and customer impact.
- Field maintenance and tower inspection
- Method: Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation.
- Evidence: Asset condition, images, alarms, work orders, routes and skills.
- Decision supported: Improve safety, coverage and restoration with fewer unnecessary visits.
- Impact measure: Inspection coverage, fault lead time, travel and first-time fix.
- Network demand and capacity forecast
- Method: Forecasting, Risk & Optimisation, Data Foundations & Business Intelligence.
- Evidence: Traffic, cells, customers, devices, quality, geography and planned change.
- Decision supported: Time investment and optimisation around actual service demand.
- Impact measure: Service quality, utilisation, avoided congestion and capital efficiency.
- AI-enabled operations reliance review
- Method: Machine Learning & Predictive Analytics, Process, Workflow & Systems.
- Evidence: Use cases, test sets, production monitoring, overrides and incidents.
- Decision supported: Use AI where it improves the complete operating process.
- Impact measure: End-to-end benefit, drift detection, safe escalation and accountability.
Implementation
The engagement was structured as use-case pilots followed by staged automation. We connected the analysis to the decisions, operating constraints and measures that the organisation would continue to use.
How success was assessed
The overall assessment considered availability, restoration, operating cost and controlled automation. The supporting measures included:
- Detection lead time, restoration, false alerts and customer impact.
- Inspection coverage, fault lead time, travel and first-time fix.
- Service quality, utilisation, avoided congestion and capital efficiency.
- End-to-end benefit, drift detection, safe escalation and accountability.
Services and methods used
Services: Risk Detection, Predictive Maintenance & Reliability, Fleet & Logistics Optimisation, Sales Forecasting & Demand Planning, Business Feasibility Study, AI & Model Risk, Process & Workflow Analysis & Redesign.
Methods: Machine Learning & Predictive Analytics, Data Foundations & Business Intelligence, Forecasting, Risk & Optimisation, Process, Workflow & Systems.