In the fast-paced world of manufacturing, efficiency is paramount. One of the key metrics in determining operational efficiency is cycle time, which represents the total time required to complete a manufacturing process from start to finish. Identifying and addressing bottlenecks in cycle time can lead to improved production rates, reduced costs, and enhanced overall equipment effectiveness (OEE).

By integrating IoT-driven data collection with machine learning algorithms, enterprises can transition from reactive to proactive cycle time optimization. This blog explores how AWS IoT Greengrass, MongoDB Atlas, Amazon Sagemaker and Amazon QuickSight can work together to reduce cycle time,  enhance manufacturing efficiency and provide deep insights of the various manufacturing plants.

The Challenge: Identifying Bottlenecks in Cycle Time

Equipment Bottlenecks:

Machine downtime, operator response delays, and inconsistent equipment performance. Issues such as unexpected breakdowns, suboptimal machine settings, and lack of preventive maintenance strategies can lead to severe disruptions in production flow.

Equipment Bottlenecks:

Machine downtime, operator response delays, and inconsistent equipment performance. Issues such as unexpected breakdowns, suboptimal machine settings, and lack of preventive maintenance strategies can lead to severe disruptions in production flow.

Resource Bottlenecks:

Fluctuations in the availability of utilities, labor, and raw materials. Supply chain disruptions, inefficient workforce allocation, and unpredictable demand for resources contribute to extended cycle times and higher operational costs.

Data Silos:

Lack of real-time visibility across production lines, preventing timely interventions. Many manufacturers operate with legacy systems that do not integrate seamlessly, making it difficult to gain a unified view of production processes.

Process Variability:

Inconsistent cycle times due to operator proficiency, manual interventions, and changing environmental conditions. Without a standardized approach, variability in processes can make it difficult to identify and address inefficiencies.

Delayed Decision-Making:

In the absence of real-time insights, decision-makers rely on historical reports, leading to reactive rather than proactive problem-solving. A lack of predictive capabilities results in Without real-time data insights and predictive analytics, manufacturers risk inefficiencies, increased costs, and production delays. The integration of IoT and AI-driven insights allows businesses to address these challenges systematically.

The Solution: High-Level Architecture

MongoDB Atlas Cloud Backend

MongoDB Atlas serves as the scalable cloud backend for storing and processing production data. Features like Atlas Triggers and functions ensure seamless data integration with other AWS services.

AWS IoT Greengrass with Local ML Models

AWS IoT Greengrass enables edge processing, reducing latency and allowing for real-time decision-making using locally deployed ML models to understand potential bottlenecks and in-efficiencies which may impact cycle time.

Amazon SageMaker

AWS SageMaker DataWrangler helps prepare and clean the data for training the target ML models. The ML models are trained with data on a continuous basis and prepared for deployment to the greengrass device in the manufacturing plant.

Amazon QuickSight 

Amazon QuickSight consumes the data in real-time from MongoDB Atlas and present detailed analytics and insights for each of the manufacturing plants and can further be drilled down into specific production lines as well to observe their performance.

Business Use Cases

Manufacturing Process Optimization

• Reducing equipment downtime by predicting failures before they occur.

• Identifying underperforming machines/operators and implementing targeted improvements.AWS IoT Greengrass with Local ML Models

Supply Chain Efficiency

• Analyzing material availability in real-time to avoid production delays.

• Optimizing labor allocation to maximize throughput while minimizing costs.

Quality Control and Predictive Maintenance

• Ensuring consistent product quality by identifying deviations in cycle time.

• Scheduling proactive maintenance based on historical data trends.

Conclusion

By leveraging AWS and MongoDB for cycle time optimization, manufacturers can enhance operational efficiency, minimize downtime, and make data-driven decisions in real-time. The seamless integration of IoT, AI, and predictive analytics enables a transition from reactive problem-solving to proactive efficiency enhancement.

To see these solutions in action, explore our GitHub repository for detailed implementation guidance. For further insights, contact us at partners@wekan.company and learn how to accelerate your manufacturing processes with cutting-edge AWS and MongoDB solutions.