Active Pharmaceutical Ingredients (API) production faces many challenges, among them significant variability that impacts yield. Often, the root causes of low yield are not only unexpected, but unknown. To identify potential yield problems before they impacted performance, one manufacturer implemented an analytics system and a digital transformation of their plant using a SmartFactory Rx® solution.
The company worked closely with the SmartFactory Rx team to identify root causes of manufacturing process issues. The solution provided the transparency technicians needed to make quick, high-quality decisions in near-time and the team helped develop best practice instructions that could be shared at different sites across the enterprise.
More information about how this pharmaceutical manufacturer achieved a 25% improvement in manufacturing and millions of dollars of savings by implementing SmartFactory Rx solutions, read our article, “How a laser focus on yield improved one company’s API manufacturing.”
FAQs
Why is data preparation for AI considered a challenge in semiconductor manufacturing?
Data preparation for AI can be expensive in terms of time and resources, making it a barrier, especially for smaller companies. Historical data may also be insufficient due to evolving environments.
How does simulation help overcome data collection challenges for AI deployment?
Simulation allows for the creation of synthetic data, eliminating the need for extensive data cleaning. It provides an efficient way to generate diverse and high-quality data for AI training.
What are some practical benefits of using simulation in AI deployment?
What role does simulation play in scenarios like Reinforcement Learning (RL) and Machine Learning (ML) in semiconductor manufacturing?
Simulation plays a crucial role in RL by providing a detailed environment for agents to learn and make decisions. In ML, it allows models to be trained on rich datasets, leading to operational efficiency gains and KPI comparisons.
