Industrial manufacturing in the 21st century means individualized products, short production cycles, and intense competition. One goal of the Industrial Internet of Things (IIoT) is to increase productivity and competitiveness continuously. Therefore, the factory of the future must first be able to do one thing: deliver top performance. For this reason, more and more companies are bringing Internet technologies into production to network individual machines, sensors, and devices on the shop floor. It creates unprecedented transparency across all steps of the entire production process.
But what is the best way to enter Industry 4.0? What is the first step towards the “transparent factory”?
The first step towards a digitally networked factory should be to connect the machines and sensors to an IIoT or analysis platform. Accordingly, if a company creates a connection, it will start to collect all production, and this enables comprehensive review and evaluation options. The question then arises of how the right measures can be derived from the wealth of information. Because with the digital integration of machines, we suddenly have an enormous amount of data at our disposal.
The resulting data sea allows for analysis by using the right key figures. To do this, you have to answer the question:
- What do you want to measure?
- Which key figures are critical to your success?
The analysis tools of the FORCE MES FLEX platform help you to evaluate your desired key figures and data and to examine your production down to the smallest detail. In this way, the potential for optimization can be identified in no time at all and help you to introduce the right measures to achieve rapid success. Yet, all this data can only lead to success if it is used correctly and to the full extent.
For this reason, another critical building block is the CIP-oriented management culture. Only if the culture of a continuous improvement process is at the center, and the managers act accordingly, it is possible to achieve long-term success. Companies must train their employees to use data collected in the production correctly and to derive the right measures.