Innovating the Manufacturing Process: How AI Transformed Efficiency for a Global Plastic Packaging Company

Our Client

A plastic packaging manufacturer with over 4 decades of experience in the domain.
Headquarters in California, USA, and has multiple manufacturing facilities around the globe.
Manufactures and supplies packaging materials to a range of industries.

Problem Statement

The client faced several challenges that hindered their manufacturing efficiency and profitability. The key issues included:

Unplanned Machine Downtime: Frequent breakdowns and unexpected machine failures resulted in significant downtime, leading to delays in production schedules and increased maintenance costs.
Inefficient Maintenance Practices: The client relied on traditional preventive maintenance schedules that were not optimized for machine health and performance. This approach often led to unnecessary maintenance tasks or overlooked critical issues, resulting in reduced equipment reliability.
Lack of Real-time Insights: By The client had limited visibility into the performance of their manufacturing processes and machinery. The absence of real-time data made it difficult to identify inefficiencies, bottlenecks, and anomalies, preventing timely interventions and process improvements.

Our Solution

To address the client’s challenges and enhance their manufacturing efficiency, we proposed a comprehensive solution that leveraged the power of Artificial Intelligence (AI) and Machine Learning (ML). The key components of the solution were as follows:

Data Collection and Integration: We set up a robust data collection infrastructure within the manufacturing facilities, incorporating sensors and data acquisition systems on critical machinery. These sensors collected operational and performance data in real-time, which were integrated into a centralized data platform.
Predictive Maintenance: Leveraging AI and ML algorithms, we implemented a predictive maintenance system. By analyzing historical data and identifying patterns, the algorithms could anticipate potential machine failures and schedule proactive maintenance activities. This approach minimized unplanned downtime, improved equipment reliability, and reduced maintenance costs.
Machine Downtime Detection: Real-time monitoring of machine data enabled the application of ML algorithms to detect and alert personnel about unplanned machine downtime events. This allowed for prompt response and minimized the impact on production schedules, ensuring timely interventions and resolutions.
Anomaly Detection: ML algorithms were employed to analyze the operational data and detect anomalies or deviations from expected patterns. By identifying abnormal behavior, equipment malfunctions, or process inefficiencies, the system provided early warnings, enabling immediate corrective actions and process optimizations.
Real-time Insights and Visualization: A comprehensive analytics dashboard was developed, powered by AI and ML models. This dashboard provided real-time data visualizations, performance indicators, and predictive analytics.

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Business Impact

The implementation of our AI-driven solution resulted in significant improvements in the client’s manufacturing efficiency and overall business outcomes. The key impacts achieved were as follows:

The predictive maintenance system and machine downtime detection algorithms led to a 12% reduction in unplanned machine downtime. This translated into increased production uptime, with an average of 5 additional production hours per week, improved throughput, and consistent delivery schedules.
The maintenance practices were optimized, resulting in a 9% reduction in maintenance costs. Unnecessary maintenance tasks were eliminated, and critical issues were proactively addressed. This optimization enhanced equipment reliability and extended the lifespan of machines by approximately 15%.
Real-time data collection and analysis empowered the client with valuable insights enabling the identification of bottlenecks, inefficiencies and quality issues. As a result, the client achieved a 7% reduction in production defects and a 10% increase in overall production efficiency.