NeenOpal AI Manufacturing Command Centre - Predictive Maintenance

## Key Features

• **AI-driven operational insights**: Real-time predictive analytics and equipment health monitoring powered by machine learning models.
• **Natural language interface**: Query production, maintenance, and telemetry data conversationally via Amazon Q - no SQL or dashboard navigation required.
• **IoT and OT integration**: Collect, process, and visualize sensor data with AWS IoT SiteWise and Timestream.
• **Knowledge retrieval with RAG**: Access SOPs, manuals, and historical repair logs for contextual recommendations.
• **Automated alerts and workflows**: Trigger notifications or corrective actions in ERP/CMMS systems automatically.
• **Customizable dashboards**: Pre-built visualizations in QuickSight and Managed Grafana for production monitoring and KPI tracking.

## How It Works - Example Scenario

Consider a discrete manufacturing plant monitoring CNC machines across multiple production lines. Vibration sensors detect an anomaly on a critical spindle motor. The AI model analyzes historical patterns and predicts bearing failure within 72 hours. A work order is automatically created in the connected CMMS system, and the maintenance technician receives a mobile alert with AI-recommended repair steps pulled from equipment manuals via RAG. The operator queries Amazon Q: "What is the current health status of Line 3?" and receives a plain-language summary with recommended actions - all without leaving the command centre dashboard.

## Benefits

• **Reduced unplanned downtime**: Predictive alerts and AI insights help prevent equipment failures before they occur.
• **Improved production efficiency**: Optimize workflows and OEE with data-driven recommendations.
• **Enhanced operational visibility**: Centralized real-time insights across all manufacturing processes.
• **Data-driven maintenance strategies**: Move from reactive to preventive and predictive maintenance.
• **Empowered workforce**: Operators and engineers make faster, informed decisions using natural language.
• **Rapid deployment**: Start with a fully functional PoC in under 60 minutes versus weeks with custom builds.

## Security and Architecture

The solution is **AWS Foundational Technical Review (FTR) validated** and deploys entirely within your AWS environment. Data remains in your account and VPC. AWS-native encryption is used for data at rest (via KMS-managed keys) and in transit (TLS 1.2+). IAM policies enforce least-privilege access controls across all components.

## Prerequisites

To deploy the accelerator, buyers need:

• An active AWS account with permissions to provision IoT SiteWise, Lambda, Timestream, S3, QuickSight, and Managed Grafana resources.
• IoT sensor data sources (existing or planned) with network connectivity to AWS.
• Maintenance logs, SOPs, or equipment manuals in digital format for RAG ingestion.
• A designated project lead on the buyer side for scoping and validation.

## Getting Started - Engagement Timeline

**Week 1 - Discovery and Scoping**: NeenOpal conducts a discovery workshop to define user roles, processes, data sources, and integration requirements. Deliverable: Scoping document and architecture design.

**Week 1-2 - Accelerator Deployment**: Deploy the pre-built accelerator in your AWS environment (under 60 minutes for core deployment). Connect IoT sensor data, maintenance logs, and SOPs. Deliverable: Working PoC with connected data sources and functional dashboards.

**Weeks 3-4 - Customization and Tuning**: Fine-tune AI models, customize dashboards for your KPIs, and configure integrations with ERP, MES, or CMMS systems. Deliverable: Production-ready command centre with operational playbook.

To get started, request a discovery call or pilot deployment through AWS Marketplace. NeenOpal's team will guide you from initial scoping through production-ready deployment.