Transforming Business Management Practices for Increased Production Through Deep Learning and Cloud Computing Integration

In this research, the implementation of deep learning technologies into the process of predictive maintenance to optimize business management was explored. The deep learning models used to analyze the historical sensor data collected were Recurrent Neural Networks, Convolutional Neural Networks, Long Short-Term Memory Networks, and Autoencoders. When it comes to RNNs, the analysis revealed a 35% reduction in failure rates along with 30% decrease in downtime of equipment, as well as 25% decrease in maintenance costs and 20% increase in production. As for CNN s, they contributed to 40% reduction in failure rates as well as 25% reduction in downtime and 20% cut of maintenance costs along with 15% increase in production. LSTMs, in turn, generated the most significant results with 50% decrease in failure rates and 40% reduction in downtime. What is more, LSTMs allowed for 35% decrease in maintenance costs and 30% increase in production. Finally, Autoencoders showed 30% reduction in failure rates and 20% decrease in the downtime of equipment, along with 15% decrease in maintenance costs and 10% increase in production. As the results of the experiment demonstrated, deep learning had substantial effect on the predictive maintenance programs and the use of RNNs, LSTMs, and even CNNs became essential as they could contribute to the optimization of business management practices in organizational contexts.