- San Jose
- Electronics Manufacturing
- 32 weeks
- B2C
- Python, AWS, Azure, Ignition by Inductive Automation, industrial sensors and actuators
Project Brief
An electronics manufacturing company hired us to provide full Industry 4.0 consulting services to assist with their digital transition.
Client Background
The corporation made industrial and consumer gadgets and has many plants throughout the world. Rising manufacturing costs, supply chain interruptions, and a surge in consumer demand for bespoke items and speedy delivery all posed hurdles for the company.
Key Challenges:
Solution:
1. Discovery and Planning
We started a comprehensive evaluation of the manufacturing processes, IT setup, and staff preparedness of the firm. To fully grasp the concerns and goals of important stakeholders, such as production managers, engineers, and IT personnel, we held workshops and interviews with them. We used the information we gathered to create a thorough Industry 4.0 roadmap that detailed a staged approach to digital transformation, giving priority to fast wins and being in accordance with the strategic objectives of the organisation.
2. Development
In order to provide real-time data gathering and control, we closely collaborated with the manufacturer's technical team to retrofit their current equipment with sensors and actuators. In order to gather, handle, and evaluate this data, we created unique software programmes by utilising cloud-based systems such as Microsoft Azure IoT Hub and Amazon IoT Core. In order to detect trends in the data and anticipate possible faults, we also put machine learning algorithms into practice. This allowed us to do preventive maintenance and minimise downtime.
3. Implementation
We led the manufacturer through the Industry 4.0 solution's gradual adoption. To evaluate the technology and show its potential advantages, we began with trial projects on a few production lines. We expanded the use of the solution to the entire factory when the pilots were effective.
Tools & Technology Used
TensorFlow
ML Library
Python
Programming Language
Angular
Frontend
Azure
Cloud Services
Features:
Real-Time Monitoring & Control:
Data collection and real-time monitoring and control of production processes are made possible by sensors and actuators integrated into machines.
Predictive Maintenance
Predictive maintenance uses machine learning algorithms to analyse sensor data and identify possible equipment defects, reducing unplanned downtime and enabling proactive maintenance.
Quality Control
Automated quality inspection systems employ machine vision and other technologies to discover faults in real-time, leading to improved product quality and reduced waste.
Digital Twin
Before making changes to the production line, a digital twin can be used to mimic and improve operations.
Data Analytics & Reporting
Data-driven decision-making is enabled via configurable dashboards and reports that provide insights into production performance, equipment utilisation, and quality metrics.