Industry
Client Overview
A global leader in industrial equipment manufacturing with over 200 facilities worldwide. The client was facing significant financial losses due to unplanned machinery downtime, which disrupted production schedules and led to costly emergency repairs. Their existing maintenance schedule was based on fixed time intervals, not the actual condition of the equipment, leading to both premature and late interventions.
Client Testimonial
"CIS didn't just give us a data model; they delivered an end-to-end, production-grade system that integrated with our existing factory floor software. Their CMMI Level 5 process was evident in the quality of the work and the seamless execution. The 30% reduction in downtime has had a direct, positive impact on our bottom line." - VP of Global Operations
Problem
The client needed to move from a reactive/preventive maintenance strategy to a predictive one. They needed a system that could analyze sensor data from thousands of machines in real-time to predict potential failures before they happened.
Key Challenges
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01
Data Silos : Sensor data was stored in multiple, incompatible legacy systems.
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02
Scalability : The solution had to be scalable to over 10,000 machines across the globe.
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Integration : The system needed to integrate with their existing ERP and work order management software.
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Accuracy : The predictive models had to be highly accurate to gain the trust of the factory floor engineers.
Our Solution
CIS designed and implemented a custom, cloud-based AI predictive maintenance platform.
Unified Data Platform : We built a central data lake on AWS, creating data pipelines to ingest and normalize sensor data (vibration, temperature, pressure) from all legacy systems.
Custom ML Models : Our data scientists developed a suite of machine learning models to detect anomaly patterns and predict the Remaining Useful Life (RUL) of critical components.
Intuitive Dashboard : We created a web-based dashboard that provided a real-time health score for every machine, highlighting at-risk equipment and providing clear, actionable maintenance recommendations.
ERP Integration : We built a two-way integration with their SAP system, automatically generating work orders for predicted failures.
Implementation & Execution
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Discovery & Requirements Mapping
Conducted a 2-week discovery phase to map all data sources and user requirements.
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Core Engineering Deployment
Deployed our "Big-Data / Apache Spark Pod" to build the data engineering backbone.
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Agile Prototyping
Used an agile methodology, delivering a functional prototype for a single factory within 3 months.
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Feedback & Model Optimization
Fine-tuned the ML models using feedback from the client's maintenance engineers.
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Phased Global Rollout
Developed a phased, global rollout plan, bringing 10-15 factories online each quarter.
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Training & Operational Handover
Provided comprehensive training and documentation to the client's internal teams.
Positive Outcome
1. 30% Reduction in Unplanned Downtime
The solution's primary goal was achieved within the first year of full deployment.
2. 20% Reduction in Maintenance Costs
By moving to condition-based maintenance, the client avoided unnecessary part replacements.
3. 15% Improvement in Overall Equipment Effectiveness (OEE)
Higher uptime directly translated to increased production output.
4. Creation of a Single Source of Truth
The unified data platform became a valuable asset for other business intelligence initiatives.
Why Choose Us
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Process Maturity
Our CMMI L5 approach ensured a complex project was delivered on time and on budget.
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Enterprise Integration
We have deep expertise in connecting modern AI with legacy ERPs like SAP.
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100% In-House Team
The same team of data scientists and engineers worked on the project from start to finish.
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Scalability
We architected the solution on AWS to handle massive data volumes from day one.
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Security
Our ISO 27001 certified processes ensured the client's sensitive operational data was secure.
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Full-Stack Capability
We built the data pipelines, the ML models, the frontend dashboard, and the API integrations.
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Domain Knowledge
We understood the unique challenges of the manufacturing environment.
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Transparency
The client had full visibility into our progress through shared project management tools.
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IP Ownership
The client owns the entire platform, giving them a long-term competitive advantage.
Conclusion
This project demonstrates CIS's ability to go beyond model development and deliver a complete, integrated, and scalable enterprise AI solution that drives tangible business results.