Any business, be it in manufacturing, processing, or even environmental science can benefit from knowing about their past work and learning from any prior success or even failures. Data historian software, primarily used for manufacturing and plant productions, makes it simpler to see the rise and fall in your process and where you can specifically improve. If you are currently using a data historian system, here are a few ways that you can optimize that process to get more out of it.
Feed Data Regularly
The idea behind data historian processing is simple — the more information you put into your program, the more you’ll garner out of it. By connecting multiple data entry points in your system, you are giving the software more to work with. This will yield more patterns and information for your end result, giving you a better idea of what equipment is not operating at full capacity, which is being overworked, and any areas that are costing you more money than you thought.
Read Output Data on a Schedule
One of the keys to getting more out of your data is reading the output on a regular basis. Make it a habit to look at this information on a schedule. It may be useful to look at the whole operation on a quarterly basis while you look at more specific working parts of the whole more often, particularly if you are monitoring improvement in one area of your manufacturing line.
Collect Feedback About Data Results
You may have a team of data collection experts, project managers, and production managers on your side reading the results of the data. How you choose to proceed with your new information is entirely up to the discretion of your team. However, it can be useful to get feedback from those who regularly operate the machinery or are more hands-on about whether your actions are proving to be effective or not. Waiting for the next round of data results to see how things are going works as well, but it is often fruitful to get human feedback from those on the grounds.
Making your data historian software system work for you does not have to be difficult. Once you understand how the program works, you will essentially get out of it what you commit to putting in it. Work on putting in useful information, reading the results regularly, and getting feedback from others on your actions. All of this will improve and optimizing your data historian performance.