Skip to content 
Streamlining Manufacturing Excellence: Data-Driven Solutions for Optimized Production Lines, Supply Chain Management, and Quality Assurance.
"By supporting multiple data models with a single query language, ArangoDB allows us to scale through our software, not engineering time"
Get started with Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Read The Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.
Step 1
Product Line Monitoring
Use ArangoDB to collect and store real-time data from manufacturing production lines, including machine statuses, throughput, and error rates. ArangoDB's real-time data ingestion captures manufacturing production line data like machine statuses, throughput, and error rates. Its flexible schema aids efficient storage, enabling informed decisions and process enhancements.
Step 2
Predictive Maintenance
Leverage the real-time production line data from Step 1 as the foundation for predictive maintenance by providing insights into machine behavior and performance. ArangoDB will allow you to analyze historical data to predict machine failures in far fewer steps than competing solutions, more rapidly optimizing maintenance schedules and minimizing downtime.
Step 3
Quality Control & Defect Tracking
If you have more reliable machines from predictive maintenance (Step 2) you can implement higher quality control standards. Pair this data with inspection results to identify defects, track their root causes, and improve overall production quality. ArangoDB’s model flexibility will make this step exponentially easier and faster.
Step 4
Supply Chain Optimization
Accurate quality control data (Step 3) reduces defective products in the supply chain, improving resource utilization and demand forecasting accuracy. This data, combined with production, inventory, and supplier data, enables you to optimize the supply chain. In this way, ArangoDB can help to ensure the timely availability of raw materials and minimize bottlenecks.
Step 5
Smart Factory & Digital Twin
Imagine creating a smart factory environment with digital twins of production processes. Simulate and optimize operations with ArangoDB Graph ML, enabling real-time adjustments for increased efficiency. You can do this because once you optimize the supply chain (Step 4) you ensure the availability of resources supporting digital twins and smart factory precision.
Get started with Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Read The Case Studies
(no credit card required), and experience the shortest time to value for a hosted graph DB.