The Role of AI-Driven Predictive Maintenance in Reducing Downtime in American Medicine Manufacturing
Keywords:
Predictive Maintenance, Reducing Downtime, Medicine ManufacturingAbstract
- Introduction
The role of AI-driven predictive maintenance in reducing downtime in American medicine manufacturing is an important research topic and is the subject of this essay. Manufacturing is a complex process that relies on many activities and functions. Over time, processes of discrete and continuous functions will become outdated and less efficient. A breakdown of machinery and equipment can result in major disruption and loss of productivity, impacting a company's bottom line. Large international organizations have invested millions in the upkeep of machinery and equipment. Proper maintenance is strategically needed to continue efficiency, reliability, and satisfactory performance. Downtime in manufacturing needs to be minimized. Advancements in artificial intelligence (AI) present possibilities for process and work automation, accuracy improvement, increased productivity, and understanding human behavior. AI has the potential to positively impact operating procedures that can significantly reduce downtime associated with the malfunction of machinery and equipment. A better understanding of how AI can mitigate and understand causes of downtime is needed. The background, purpose, and scope of the research topic are presented first. It provides context on the growing use of AI-based predictive maintenance systems in manufacturing organizations and outlines why it is important and how this need can be met. The significance of the issue is elaborated on in the motivation section and aims to raise curiosity about the topic. Predictive maintenance is introduced and the potential role and contribution of predictive maintenance to downtime are stated in terms of concerns about downtime and how this topic needs to be better understood. Finally, the methodology section presents an overview of the essay and the methods used in approaching the research topic.
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