Dynamic Throughput optimization increases product throughput and yield. It enables you to maximize production by continuously adjusting controllable variables based on the sensor readings of non-controllable process parameters.
The complexity of most manufacturing processes makes it difficult to optimize production for increased efficiency and output while maintaining high product quality and reducing overall costs. An added layer of complexity comes from the fact that most manufacturers use a disparate mix of software applications and manual analyses to look into throughput optimization.
Throughput optimization is data-driven in essence, so the amount of data, its quality and relevance, and the robustness and maturity of collection mechanisms, all contribute to the achievable value of this solution. If you do not have the required data collection mechanisms in place or if they may benefit from a review, we can advise and guide you to kickstart or expand your systems via a more integral solution.
With production historical data in hand, we can maximize your production throughput via our dynamic center-lining, a prescriptive analytics approach that continually optimizes throughput. It involves machine learning-based estimation of key parameters and constraint-based parameter readjustment to continually keep throughput in optimal levels.
Our dynamic throughput optimization approach, based on the operationalization of state-of-the-art machine learning and data modelling techniques, has several advantages over traditional methods: