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Industry 4.0 at ALPLA: Enhancing Factory Efficiency with IoT
Technology Category
- Analytics & Modeling - Machine Learning
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- Equipment & Machinery
- Packaging
Applicable Functions
- Human Resources
- Maintenance
Use Cases
- Personnel Tracking & Monitoring
- Real-Time Location System (RTLS)
Services
- Testing & Certification
The Challenge
ALPLA, a global leader in packaging solutions, faced several challenges as the complexity of their production machinery increased. The need for highly trained specialists in each factory led to higher personnel costs, difficulties in recruiting experienced talent at each location, and costly personnel turnover. Furthermore, less experienced operators running the machines sub-optimally impacted resource consumption and overall equipment effectiveness (OEE). ALPLA also faced the challenge of monitoring visual inspection systems in every line of their plants, which was almost impossible to do manually. In 2016, ALPLA decided to use data from the 900 different types of embedded sensors in each factory to address these issues. However, their initial choice of SQL Server as the data store for the sensor data proved inadequate, as it was unable to cope with their data requirements.
The Customer
ALPLA
About The Customer
ALPLA is a world leader in the area of packaging solutions, producing high-quality packaging for renowned brands such as Coca-Cola and Unilever, as well as other brands in the food, drinks, cosmetics, and cleaning industries. The company employs 20,900 employees at 181 locations across 46 countries. As a global leader, ALPLA is committed to innovation and efficiency in its operations, and is constantly seeking ways to improve its production processes and reduce costs. The company's move to leverage IoT technology to enhance its factory efficiency is a testament to its forward-thinking approach and commitment to operational excellence.
The Solution
ALPLA switched to CrateDB, a solution perfectly suited for IoT workloads. CrateDB offered several advantages including easy migration of data and code from SQL Server, a dynamic schema that allowed ALPLA to combine all their 900 different sensor readings into a single table for easier maintenance and faster querying, and easy and economical scaling on elastic clusters of inexpensive servers. Most importantly, CrateDB's real-time performance, enabled by columnar indexes cached in memory, allowed the solution to execute dashboard queries 250x faster than SQL Server. ALPLA integrated sensor data from multiple factories and production lines into a central 'mission control', staffed by experts who monitored production via interactive dashboards. These centralized experts could give direction to less experienced, lower-cost factory workers, thereby improving efficiency.
Operational Impact
Quantitative Benefit
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