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深圳长欣自动化设备有限公司
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The reason it is not that simple is because algorithms uncover correlation patterns that may be nothing but coincidental. Algorithms are not yet smart enough to uncover causation; those patterns which indicate the root cause of the missing production or lost downtime. Some data scientists may not yet have that specific problem-solving expertise. They often rely on domain advice from seasoned industry veterans.
Nearly a decade ago, it was projected that the US would need hundreds of thousands of data scientists by 2020. It did not happen. It’s unlikely there were even enough data science students in colleges and universities 10 years ago. Furthermore, it’s not realistic to think employing more data scientists is the only way to uncover causation. To appropriately address the issue, the valuable skillset of the industry’s limited data scientists must be complemented with deeply embedded engineering wisdom and AI applied directly within the business processes. The new direction begins in role-based applications that will eventually lead to the self-optimizing plant (SOP) that is self-learning, self-adapting and self-sustaining. Analysis and performance assessments do not appear from generalized AI workbenches and functionsWithout forgetting supply and delivery issues, the major decisions in a plant are always about asset performance management. This includes the total lifecycle return on assets; when and how hard you run them; when and how they are maintained; and the total expected productivity. Such decisions are intensely domain and engineering specific based on careful periodic assessment of asset operating conditions against expected performance. But decisions often need collaboration to understand real systems equipment interactions. For example, an apparent throughput constraint may be caused by an actual upstream feed issue rather than the equipment itself. Added to that, every decision must be underscored by a clear understanding of fundamental risks and costs, especially across equipment lifecycles.