AI-Powered Smart Factory Workflow Optimization

In the era of Industry 4.0, OneData empowers manufacturers with a Smart Factory Workflow Optimization solution that brings together IoT connectivity, digital twin modelling and reinforcement-learning analytics. Designed to optimise complex manufacturing operations, the platform simulates workflows, monitors equipment and guides autonomous improvements.

Key Capabilities

IoT & Real-Time Data Capture OneData deploys IoT sensors and device connectivity through AWS IoT Core to continuously stream data from machines, conveyors, production cells and equipment. This live data forms the foundation for real-time monitoring of manufacturing workflows.

Digital Twin & Simulation Using AWS IoT TwinMaker, the solution builds a virtual model (digital twin) of the factory floor – including machines, workflows, layout and process relationships. This twin enables scenario-based simulation of alternative production flows, bottlenecks and resource allocation.

Reinforcement Learning & AI-Driven Optimisation With AWS SageMaker RL, OneData applies reinforcement-learning models to the digital twin and live operational data to identify optimal workflow policies: how machines, resources and conveyors should operate to maximise throughput and minimise waste or idle time.

Predictive Maintenance & Equipment Health By integrating AWS Lookout for Equipment, the platform analyses sensor streams to detect anomalies, early-stage equipment wear and unexpected shutdown triggers, allowing proactive maintenance and minimised downtime.

Analytics & Insights Delivery Through AWS QuickSight dashboards and reporting, OneData presents key metrics such as cycle time, throughput, idle rate, downtime, resource utilisation and enables factory managers to visualise and act on optimisation recommendations.

Use Cases

• Automotive Manufacturing: Optimising assembly-line sequence flows, robot utilisation, conveyor hand-offs and minimizing changeover downtime.

• Electronics Manufacturing: Managing high-mix, small-batch workflows, test-station balancing, component flow optimisation and preventive maintenance of test equipment.

• Heavy Machinery Manufacturing: Simulating large equipment workflows, resource scheduling, crane or hoist utilisation, predictive maintenance of major assets to reduce unplanned stoppages.

Benefits

• Increased Throughput: By modelling and optimising workflow policies, the solution enables factories to produce more output per unit time.

• Reduced Downtime: Early detection of equipment issues and smart resource scheduling cut emergency stoppages and idle time.

• AI-Driven Workflow Improvement: The platform continuously learns and adapts, enabling the factory to evolve rather than stay static.

• Resource Efficiency & Cost Savings: Better utilisation of machines, people and materials yields lower operational cost and waste.

• Operational Agility: With a digital twin and simulation capability, manufacturers can test “what-if” scenarios (new line layout, increased product mix) before changes in the real world.