Optimizing Production Schedules for Global Steel Manufacturing Company
Aimpoint Digital optimized steel production scheduling using advanced analytics and edge computing, enhancing efficiency and adaptability in real-time for a major steel manufacturer.
The Challenge
The client had many steel coordinators on the mill floor making decisions via heuristic knowledge and spreadsheet-based modeling. Operators leveraged simple averages to understand possible dwell and transit times, often overlooking many available details. The overarching goal was to keep the casting units running constantly and at maximum capacity. To do this, the plant floor operators needed optimal production schedules to maximize output.
Our Approach
The Aimpoint Digital team worked within the client’s Hadoop environment, leveraging Python, Spark, Pyspark, Dash, Docker, and FastAPI to use edge computing nodes to implement a Discrete Event Simulation that scaled to compute thousands of probability densities efficiently and quickly. This allowed the team to run a “what-if” style analysis to determine optimal schedules given production requirements. The Aimpoint team also included additional functionality for the client, allowing them to push files detailing scheduled machine downtimes to understand the effect of machine maintenance on production schedules.
Results
The client is now able to query their data lake viaAPI to easily return optimized schedules.
Schedules can be quickly adapted based on the changing facility status.
Thanks to the queries available, the client can runvarious “what if” scenarios to find the best solution.
Key Takeaways
In a production environment, optimizing output is crucial for efficiency. While manual processes work, they lead to wasted time and resources.
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