Introduction

Company Background

•Industry: Poultry Equipment Manufacturing
•Location: Pune, India
•Size: 5 Plants
•Core Products: Poultry cages, feeding systems, watering systems, climate control systems, etc.

The Challenge

Problem Statement
The Poultry Equipment Manufacturing Company faced challenges in maintaining optimal productivity and efficiency across its manufacturing processes. The key issues included:
•Unplanned Downtime: Frequent breakdowns leading to production halts.
•Inconsistent Quality: Variability in product quality due to equipment malfunctions.
•Low Production Efficiency: Inability to accurately measure and optimize machine performance.
•Lack of Real-time Monitoring: Limited visibility into machine status and performance, leading to delayed maintenance actions.

Objectives

The company aimed to achieve the following goals through the implementation of an OEE solution:
• Increase Machine Uptime: Reduce unplanned downtime by identifying and addressing equipment issues proactively.
• Improve Quality Control: Enhance the consistency of product quality by monitoring machine performance in real time.
• Optimize Production Efficiency: Accurately measure and improve OEE by tracking availability, performance, and quality metrics.
• Implement Predictive Maintenance: Utilize vibration analysis and other predictive techniques to foresee and prevent equipment failures.

The Solution

Overview of the OEE Solution
The solution involved the deployment of an IoT-based OEE monitoring system across the manufacturing plant, including the installation of sensors, data collection, and real-time analytics to monitor machine performance.
Components of the Solution
•Sensors and Data Acquisition: Installation of rotary line encoders, proximity sensors, energy meters, and vibration sensors on key equipment like feeders, cutters, and rolling machines.
•IoT Gateway: Use of IoT gateways to collect data from sensors and transmit it to the central monitoring system.
•Network Infrastructure: Setting up a robust Wi-Fi network to ensure reliable data transmission across the plant.
•ThingsBoard Platform: Implementation of the ThingsBoard IoT platform for data visualization, analytics, and dashboard customization.
•Data Integration: Integration of sensor data with existing PLCs and databases for comprehensive monitoring and analysis.
•Vibration Analysis: Leveraging vibration data to predict and prevent potential failures in critical machinery.

Implementation Phases

Phase 1: Planning and Preparation
• Define Specific Goals: Identified key performance indicators (KPIs) such as machine availability, performance, and quality.
• Network and Infrastructure Assessment: Evaluated the existing network, determined Wi-Fi router placement, and set up virtual machines (VMs) for data processing.
Phase 2: Installation and Configuration
• Sensor and Hardware Installation: Installed sensors on machines and connected them to IoT gateways.
• Network Setup: Set up Wi-Fi routers, connected gateways, and tested network connectivity.
• ThingsBoard Installation: Deployed the ThingsBoard platform on a VM and configured it to receive and process data from sensors.
• Kafka Message Queue Implementation: Set up Apache Kafka as a message queue to efficiently ingest data from multiple sensors.
Phase 3: Data Integration and Testing
• Data Collection: Configured IoT gateways to collect data from sensors and integrated it with ThingsBoard.
• PLC Integration: Integrated PLC data for parameters such as speed, temperature, and pressure.
• Initial Testing: Conducted tests to verify data accuracy and ensure seamless data flow from sensors to the platform.
Phase 4: Pilot Testing and Optimization
• Pilot Test: Conducted a pilot test with a subset of machines and users to gather feedback and optimize data collection intervals and alerts.
• Dashboard Customization: Designed customized dashboards for different roles, including production managers, maintenance teams, and quality control personnel.
Phase 5: Full Deployment
• Full-Scale Deployment: Rolled out the OEE solution to all machines, ensuring that the entire production line was covered.
• Training and Support: Provided training sessions for staff on how to use the system and interpret the data.
• Ongoing Monitoring and Maintenance: Set up a continuous monitoring system to track performance and maintain equipment proactively.

Results

•Increased Uptime: Reduced unplanned downtime by 25% due to timely identification and resolution of machine issues.
•Improved Quality: Enhanced product quality consistency, reducing defects by 20%.
•Optimized Efficiency: Achieved a 15% increase in overall production efficiency by identifying and addressing bottlenecks.
•Predictive Maintenance Success: Successfully predicted and prevented potential equipment failures, leading to a 30% reduction in maintenance costs.

Conclusion

The implementation of the OEE solution significantly improved the Poultry Equipment Manufacturing Company's production capabilities. By leveraging IoT technology and advanced data analytics, the company was able to optimize machine performance, reduce downtime, and ensure consistent product quality. This case study demonstrates the effectiveness of OEE monitoring in a manufacturing environment and serves as a blueprint for similar industries aiming to enhance their operational efficiency.