How IBU work

Heavy Engineering - Industrial KPIs

1. Improve Quality

Organic and Data-driven Product Quality Improvements using Digital Twins and CoPilot Asstants across Heavy Engineering Production Process. The product quality can significally by with timely information with CoPilot with AI/ML inference pipelines.

2. Reduce Defect Ratios

Agile CoPilot frameworks to provide a robust data-driven pipelines to support computerized CNC production machine operations and maintenance. The final products and operational quality KPIs are key for better ROI and market reputation.

3. EHS Compliance

The Industrial Shop Floors are prone to Environment, Health & Safety (EHS) related accidents and incidents resulting in asset and human health issues and loss. The CoPilot Systems for EHS factors involves many preventive AI/ML and GenAI features.

4. Reduce Cycle-Times

Reduction of Operational and Supply-Chain related Cycle-Time reduction using CoPilot and AI/ML based processes. The data-driven management and business governance would faster and better outcomes.

5. Reduce Waste Streams

Manufacturing processes have residual waste materials, anciliary ouputs external to finished products. The real-time data and analytics of the production line based intelligence with CoPilot features for waste reduction and support recycling and circularly.

6. Predictive Maintenance

Process mapping and systemic physics based preventive maintenance AI/ML models. The real-time analytics and CoPilot functions for Preventive Maintenance inference alerts and scenario data inputs.

Leadership
VISIONTo lead and create a pioneering Industrial CoPilot System for L&T Heavy Engineering and for wider global market adoptions, with improved RELIABILITY, EFFECTIVENESS and RESPONSIVESS as a Smart Manufacturing ecosystem.
Industrial CoPilotwith Real-time Digital Twin, AI & GenAI

IBU APPROACH & SOLUTION

The Industrial CoPilot project covers the required Production process models,engineering/re-engineering work-flows for the functional User Divisions to adopt Industrial CoPilot enablement process. Centralized controls and management along with distributed manufacturing and operations includes GenAI & AI/ML pipeliens supporting AgenticAI, Robotic Process Automation (RPA) and Agentic Process Automation (APA). The integrations for CoPilot pipelines would include CRM, SRM, SCM, ERP, FICO and PLM functions and process management mostly using fixed VDC/PLC screens and on-premise mobile devices.

PROJECT OUTCOMES

● Create Industrial CoPilot Process & Management Model adoption and maturation.
● Create the Data Journey for the Industrial CoPilot applications across all operations.
● Create or develop the Static & Dynamic process & work flows as approved by the Client.
● Build the Backend AI/ML platform to manage the CoPilot functions and features
● Build APIs and Services for the platform to connect with all required data sources
● AI/GenAI related Compute infrastructure
● System Architecture Designs
● Programmable Logic Controller (PLC) Panels and Front-end UI/UX designs
● Workflow Process Models for CoPilot functions
● CI/CD integration for RPA & APA Agents
● Predictive data pipelines and DataFabric functions
● Access Control & Security envelopes
● Release & Deployment models
● BAU escalation and Support Models

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