Predictive Asset Maintenance with Digital Twin

Predict equipment failure and forecast issues to prevent them. Reduce unforeseen downtime and total maintenance cost. Increase production by reducing instances of the unforeseen shutdown.

How C5i’s Predictive Asset Maintenance Solution Helps

Drive last-mile adoption & business impact through ease of consumption and role-based insights


  • MES data: Work order planning, maintenance, scheduling, and resource management
  • Historian data
  • Sensor data
  • Mobility integration data
  • Process data
  • LIMS


  • Equipment analytics: Survival analysis, Degradation model, Proportional Hazard model
  • Condition Monitoring analysis: Early alerts, time series, support vector machine, cluster analysis, classification models, vibration, oil, thermography
  • Process analytics: Process models, process simulation, ID causal factors, bad actors
  • Intelligence-based models: Neural, Bayesian, maintenance strategy
  • RCA, FMEA, Decision tree, reliability models


  • Equipment Failure predictions along with probabilities
  • Optimized Maintenance Schedule
  • Standard and customized reports on how the Maintenance function is performing


  • Manage and create effective maintenance plans
  • Raise alerts (statistical & non-statistical): Leading indicators or failure modes identified and monitored in order to intercede
  • Forecast issues before they would have been detected and reduce downtime
  • Prioritize maintenance based on the risk (probability) of a failure and the consequences of that failure

- Reduce maintenance costs by 15%–30%
- Increase Overall Equipment Effectiveness (OEE) by 4%–5%

Connected Intelligence with AI-powered Augmented Analytics Platform

Powered by Generative AI

Deliver relevant, actionable, and human-friendly insights across multiple consumption mediums and personas to create an insights-first culture that rewards data-driven decision-making

Automated insights generation from connected enterprise and external data

Descriptive, Diagnostic, Predictive, and Prescriptive Analytics driving actionable insights

Persona-based approach to provide contextual insights on near real-time basis

High adoption with curated natural language insights available on chat, voice, enterprise BI platforms, executive presentations, emails, Teams, Slack, etc.

Tracking of impact of decision-making on key performance indicators (KPIs)

Important Metrics

Speed-to-actionable insights reduced from days to seconds
45% increase in analytics adoption by use of generative and conversational AI
30% time savings with a single source of truth and Natural Language querying
~20% revenue impact with timely, data-driven decision-making

Recognition for Discovery