How the manufacturing industry uses big data

Big data is an important concept of information technology. Many enterprises are collecting big data and analyzing it with complex analysis tools to discover hidden patterns and associations. In the event of major changes to the manufacturing system, the production system can automatically identify and find the best operating conditions, such as equipment faults, changes in raw material properties, or changes in energy and labor costs, is it great? This is the significance of the existence of big data in the manufacturing field, and also why many manufacturing enterprises are launching Big Data projects for their production facilities.

If you are working on a big data project, there are four factors to remember.

1. data cannot be separated from the actual environment

The first thing to note is that the effect of data out of the actual environment will be greatly reduced. In the field of production and manufacturing, the so-called actual environment can be provided by work tasks or execution steps. Each piece of data must be associated with the ongoing task or the product itself being produced, and associated with the features of the task. This environment can be used to compare tasks and tasks to detect significant differences. The first step in using production to create big data is to collect environment or event information, which is then associated with historical factory data. Fortunately, major suppliers of historical factory data backup tools provide event and environment plug-ins to associate mes processes or execution system job steps with historical data.

2. Analysis Optimization

The second factor to consider is that although online historical data is a great tool to store data, it is helpless in data analysis. A good method is to use offline backup or database for analysis. Most historical databases in factories optimize data access. It usually takes a lot of time to extract a large amount of data from a running online system for big data analysis. A better strategy is to periodically back up historical data to an offline system or solidify the data into a database for Big Data Optimization analysis.

3. Consider the sample size

The third factor to remember is that you must select the correct data sample. To be persuasive, make sure that the sample size is large enough to discover the internal and causal relationships. A smaller sample size may have an incorrect internal relationship, making you feel different. Another important thing is not to confuse the internal relationship with the causal relationship, because the thing with the internal relationship does not necessarily have a causal relationship. Data analysis can discover internal relationships, but it still takes a lot of work to determine whether there is a causal relationship between things. The big data analysis project must introduce engineers or scientists to ensure that engineering analysis methods can obtain real causal relationships, so that data can maximize its value.

4. Encourage participants

The last thing to remember is that in some cases, it is more reliable to rely on people to discover patterns than to rely on System Automation. You can assign a person to query the database and find certain rules. Experienced operators generally have a deep understanding of production systems and their relationships, and they can discover hidden or hidden internal relationships.

Add environmental information to the stored data, use the analyzed and optimized data, objective statement, and sufficient sample size, and make a reasonable Summary of the internal and causal relationships, and the use of personnel for data mining, these are the key components of the production of big data items. Make sure that your project considers these aspects, and big data analysis is truly being implemented in your production workshop.

As an enterprise in the field of industrial automation, Nanjing nango Intelligent Technology Co., Ltd. is committed to providing advanced industrial automation control concepts and products, transform to a solution that increases the customer's business value. Nango has been engaged in equipment trade, pre-sales and after-sales services, technical support, Business Information consulting services and related training in the field of automation control.