AI-Powered Maintenance Optimization

Introduction

Tata Elxsi implemented an AI-driven maintenance optimization and decision-support platform on AWS to enhance maintenance planning, operational efficiency, and SLA compliance across a large fleet of fuel-cell systems deployed across multiple customer sites. 

The initiative focused on building a centralized optimization ecosystem capable of integrating fragmented enterprise data sources, incorporating degradation models, and enabling scenario-based maintenance planning workflows. The platform brings together data, analytics, and optimization capabilities into a unified environment to support intelligent maintenance decision-making at scale. 

Challenge

The North American clean energy technology leader faced challenges managing maintenance planning across a large fleet of fuel-cell systems deployed across multiple customer sites under long-term service contracts. Operational data was fragmented across enterprise systems including Salesforce, MongoDB, Oracle, and OSIsoft PI, limiting centralized visibility and optimization-driven decision-making. Existing workflows relied on reactive and semi-manual maintenance planning approaches with limited ability to evaluate trade-offs between maintenance cost, SLA compliance, and fuel-cell degradation behavior. 

The customer required a scalable AWS-native platform capable of integrating distributed operational data, enabling optimization and simulation workloads, and providing centralized analytics and decision-support capabilities for proactive maintenance planning. 

Criticality:
Inefficient maintenance planning increased operational costs, SLA risks, and equipment performance degradation. Without modernization, the organization risked higher maintenance spend, reduced customer satisfaction, and limited scalability for future operational growth. 

Solution

Tata Elxsi designed and implemented a comprehensive AI-powered maintenance optimization platform built on AWS to enable intelligent, data-driven decision-making across the fleet. As part of the solution, the platform leveraged a comprehensive suite of AWS services to unify data, run large-scale simulations, and deliver actionable insights. Amazon API Gateway and AWS Lambda were used to integrate operational and maintenance data from multiple enterprise systems into the platform. Amazon EC2 supported compute-intensive optimization engines and simulation workloads, enabling scalable processing of complex scenarios. 

Amazon Redshift and Amazon RDS (PostgreSQL) provided a robust backend for analytics, reporting, and optimization data persistence, while Amazon S3 enabled scalable storage for maintenance history, degradation models, and simulation datasets. AWS Glue facilitated automated data ingestion and transformation workflows, ensuring consistent and reliable data pipelines. Secure and reliable access to the platform was ensured through Amazon CloudFront, Route 53, and AWS WAF, while Amazon CloudWatch, AWS CloudTrail, and AWS CodePipeline supported centralized monitoring, governance, and automated DevOps processes. 

At the core of the solution, a Gurobi-based optimization engine was integrated to support maintenance budget allocation, SLA-driven planning, and degradation-aware decision-making. This allowed the organization to prioritize maintenance activities more effectively and optimize resource allocation across distributed assets. A centralized data and analytics layer unified enterprise data into a scalable environment, improving operational visibility and enabling advanced analytics and reporting capabilities. 

To further enhance decision-making, an interactive scenario-based simulation system was developed. This allowed users to model and compare multiple maintenance strategies, evaluate cost-risk trade-offs, and perform “what-if” analysis before execution. The capability significantly improved strategic planning and operational efficiency.

Impact

The solution has reduced maintenance planning effort by approximately 50%, improved SLA compliance visibility by around 35%, and enabled a 25% improvement in maintenance budget utilization. Additionally, manual operational workflows have been reduced by nearly 60%.

These outcomes have enabled a scalable enterprise optimization platform, strengthened operational resilience with more proactive planning, supported faster strategic and scenario-based decision-making, and established a foundation for future AI-driven optimization and scheduling.

Services Rendered

• Optimization Platform Design
• Gurobi Engine Integration
• AWS Glue Data Pipelines
• Scenario Analytics Workflows
• Monitoring & Alerting Setup
• DevOps & Automation