Shell Boosts Reliability and Safety with AI Predictive Maintenance

Rishad Al Islam

•4 min read
a man in a hard hat is using a laptop

System Overview

Shell deployed an AI-driven predictive maintenance framework that combines IoT sensor data with advanced analytics to forecast equipment issues before they occur. The roadmap involved strategic partnerships and pilot programs, ensuring reliability before scaling across operations.

Core capabilities

  • Real-time IoT sensor monitoring of equipment health
  • Predictive analytics to detect early signs of failure
  • Pilot programs to validate models before rollout
  • Automated maintenance scheduling based on AI insights
  • Integration with enterprise asset management systems
  • Dashboards for engineers and operators to track risk levels
  • Alerts and notifications for critical events

Business problems solved

  • Reduce unplanned downtime of equipment
  • Increase asset life and optimize maintenance schedules
  • Lower operational costs by preventing failures
  • Improve safety and reliability in industrial environments
  • Scale predictive insights across global operations

Facing similar challenges in your operations? Let’s explore how predictive maintenance can work for your industry.

Industries served

Energy, oil and gas, utilities, heavy manufacturing, transportation.

Actor Identification

Primary actor: Maintenance engineer monitoring and servicing equipment.

Secondary actors: AI predictive models, IoT sensors, asset management systems, operations team, compliance officers.

Actor Goals

  • Engineer: Anticipate and resolve equipment issues before breakdown.
  • Operations Manager: Reduce downtime and improve cost efficiency.
  • AI System: Analyze sensor data, detect anomalies, and predict failures.
  • Asset Management Platform: Sync maintenance schedules and logs in real time.

Context and Preconditions

  • IoT sensors installed on critical equipment
  • Data pipelines integrated with predictive analytics models
  • Pilot programs executed to validate accuracy of AI predictions
  • Asset management system configured for automated scheduling
  • Compliance and safety approvals obtained for predictive maintenance workflows

Basic Flow (Successful Scenario)

  • IoT sensors continuously stream equipment performance data.
  • AI model analyzes data to detect early signs of wear or failure.
  • When risk is identified, AI generates a maintenance alert.
  • Asset management system schedules preventive maintenance automatically.
  • Engineers receive alerts and perform maintenance before breakdown occurs.
  • Pilot program insights are used to refine the model for full rollout.

Outcome: Shell reduced unplanned downtime by 20%, improved safety, and optimized asset usage by deploying predictive maintenance at scale.

Want to replicate Shell’s success? Talk to us about predictive maintenance tailored to your operations.

Alternate Flows

A1: False positive prediction: If AI predicts an issue incorrectly, engineers validate and recalibrate the model.

A2: Sensor failure: If IoT device stops transmitting, system triggers fallback alerts for manual checks.

A3: Asset management API downtime: If scheduling fails, alerts are sent directly to engineers until sync is restored.

A4: Pilot underperformance: If pilot results show low accuracy, AI models are retrained before scaling.

Cut downtime, extend asset life, and improve safety - just like Shell. Book a free consultation today.