Analyzing Data For Manufacturing Processes In Minutes: 3 Features To Improve The Efficiency Of Data Historian Software

Managing a large enterprise or analyzing business data points is much easier with the help of a data historian software, which is basically a software program that stores, analyzes and retrieves process and production data by time using complex algorithms. They are so beneficial that studies have found that major companies are 3 times more likely to rely on this type of software for data analytics and mining if they are strong innovators. If you're tasked with the job of analyzing data needed to batch continuous and transitional manufacturing processes, a data historian software can help you go a long way. However, this type of software can take up a lot of space, so here are 3 features you should look for.

Integration of Cloud-Integrated Storage Space

Cloud-based technology will free up disk space on your network. If your manufacturing plant needs to analyze and store large quantities of data points at all times, choose a data historian software that offers cloud-integrated storage space that is compatible with your plant's operations. In particular, you'll want to choose a system that will allow you to easily access data points for each piece of equipment involved in the manufacturing process and the timeframe when the data points were collected. The cloud application platform must be compatible with all manufacturing equipment and their processing systems.

By storing data points in a cloud application platform, the load on the data historian software will be significantly reduced. This makes the entire operation much more scalable and will improve the speed at which the software can run.

Ability to Use Swinging Door Data Compression Algorithms

Data historian software with the ability to compress data points will free up disk space, which will allow for the program to run more efficiently. There are many different types of compression systems; however, swinging door compression algorithms tend to be best for manufacturing plants. The algorithm is relatively simple. The software will analyze various data points to determine whether specific data points should be saved based on whether the absolute bias of the points exceed the compression bias.

For manufacturing plants, not all data points need to be saved. For example, when monitoring the operation speed of specific equipment, the only important data points are ones that exceed the norm. If the data point falls within a normal range, the equipment is functioning properly. Saving this data point would simply be redundant for the system, as its presence will not affect the overall analysis of operation productivity.

Automatic Data Archives via Routine or Triggered Scheduling

You can also easily free up disk space and back up the files and data points you need for long-term storage with the help of automatic data archiving. Depending on the type of data historian software you choose, you'll be able to schedule archival at either specific times or when specific conditions happen. Archiving data points will also make analyzing them much easier.

If you are using the data historian software to analyze overall output, routine scheduling will normally be most appropriate. This way, you can tell how well the manufacturing process is running based each week or month. You can also set up the software to archive data points after each project. In these situations, you might set up the system to archive data points only when certain conditions are met.

Conclusion

With the computing abilities of data historian software, gathering and analyzing data involves with batch, continuous or transitional manufacturing processes will be easy. You can use the data collected to monitor the entire manufacturing process to determine whether there are any issues, such as the productivity of certain equipment, that need to be addressed.