Industrial production is pushed on by the constantly changing market requests and global competition. To keep up with these demands and thrive in a furiously competitive market, rapid advances in current manufacturing technologies are required. Automation is the trendsetter among the technologies currently in operation. To aid in the development of production methods, the idea proposed is predictive maintenance and asset tracking. Predictive maintenance is a revolution in the way machines that are in continuous operation can be constantly monitored to detect an anomaly before it blows up into a full-fledged problem. The device is kept under constant monitoring and readings of different parameters, for example, temperature and vibrations are tabbed. Any reading that strays from the regular pattern could indicate a flaw in the device. By predicting this, downtime for maintenance can be reduced. Asset tracking is another revolutionary method to speed up efficiency in the industrial sector. Using different technologies like Wireless Sensor Networks (WSNs), the assets and their locations can be viewed using a remote device. The benefit of the same lies in the fact that often an asset whose location is unknown, wastes production time of the team by unnecessarily having to look for it. Ultimately, the idea is to implement these technologies using the modern concepts of Machine Learning, Data Visualization, Cloud Computing and the Internet of Things. This paper provides a brief introduction to the architecture of such a system followed by a detailed rundown of the above methodologies for real-time applications. © 2020 IEEE.