Optimizing workflows and resource allocation in modern manufacturing plants proves challenging, with inefficiencies and high operational costs often resulting from such endeavors.
This project involves the development of a smart factory system that employs AI and IoT data to enhance operations, improve predictive maintenance, and facilitate more informed decision-making.
The vision is for a factory that operates autonomously, dynamically adapting to changing production demands.
Week 1-2: Requirement Analysis and Planning
Activities: Identify key workflows, resource allocation needs, and predictive maintenance requirements.
Deliverables: Detailed project requirements and implementation roadmap.
Week 3-4: AI Model Development
Activities: Develop and train AI algorithms for workflow optimization and predictive maintenance.
Deliverables: Functional AI models with initial performance benchmarks.
Week 5-6: IoT Integration and Data Pipeline Setup
Activities: Integrate IoT sensors and set up real-time data pipelines.
Deliverables: Connected IoT ecosystem and live data streams.
Week 7-8: System development and testing
Activities: Build and test workflow optimization engine and scheduling system
Deliverables: Prototype ready for functional testing Week 9-10: Training and User Interface Development
Activities: Design user-friendly dashboards, train industry users
Deliverables: Operational dashboards and trained factory staff Week 11-12: Deployment and Go-Live
Activities: Deploy the system, monitor and improve algorithms
Deliverables: Fully operational smart factory system
