Manufacturing Analytics in IoT- Challenges and Solutions | HCLTech

Manufacturing Analytics in IoT- Challenges and Solutions

Advent of IoT and Industry 4.0 have made it possible to have large amount of data generated and made available for analysis. With the amount of data, there are also more opportunities to build solutions that can bring value for organizations. Determining the right problems to build solutions with the data available remains a challenge. Predictive Maintenance has been an area where much interest lies with organizations and when combined with IoT data can be a good area to provide solutions for manufacturing industries. Having determined the problem area therein lies challenges that needs to be addressed for developing solutions.

In this whitepaper, the authors discuss these challenges and how to address them in the context of IoT manufacturing. Apart from popular machine learning-based approaches like regression, there exist other approaches, which are less popular but effective. These have been part of the manufacturing industry for a long and have become more relevant nowadays as processes for collecting and storing data are becoming mature and standardized.

These approaches, when applied suitably, can harness immense value for IoT manufacturing. They also address the industry needs like improving the efficiency of manufacturing units, finding if there is a developing fault condition in an asset or how one can optimize the downtime in a plant. On top of these, the approaches mentioned are practical since they are relatively easy to apply and generate a lot of value for the organizations. Download the whitepaper to continue reading.

Download the Whitepaper