Every plant should have a robust SCADA (supervisory control and data aquisition system) to capture and store data about the manufacturing process to maximize output. The data from the process can be used in a many ways to improve the process and even implement Industry 4.0 features such as machine learning. Here are some of the top reasons to implement a SCADA system:
If You Can't Measure It, You Can't Improve it
Having accurate data for all aspects of a manufacturing process is critical for improving output. When data is sporadically, manually or inconsistently recorded it is not possible to make data-driven decisions on improvements. Changes made on a hunch, observation, or incomplete data often result in meager improvements to output at a great cost of implementation. When accurate data is used to drive improvements the outcome can typically be calculated before even investing any money at all. Each possible improvement can be evaluated based on cost and increase in output before implementation. This allows high-value projects to be prioritized, and minimal-value projects to be removed from consideration.
Statistics Are a Great Motivator
Speed or Quality statistics displayed in a manufacturing environment can be a very powerful motivator. Friendly competition between shifts can a drive employees to produce more or improve quality. Workers will be proud of their performance when they break records. Furthermore, high performance can be tied to incentives to push the boundaries further. Whether it’s a free reward pizza at lunch, a cash bonus, or a simple thank you from management, workers respond positively and will become even more effective.
Employees who are concerned about their output are also much more involved in improving the process. The operators of a machine spend more time interacting with the machine than anyone else. Therefore, they often have ideas on how to improve the process. Employees who are striving to increase output will help think of and bring forward ideas to improve the process further.
Having the data to measure and quantify performance and using it properly can motivate employees to increase output and quality.
Capturing Data is Straightforward, Even with Most Legacy Equipment
SCADA software used to capture data in manufacturing environments is straightforward to setup, even for most legacy equipment. Modern SCADA software can communicate with a wide variety of controllers. Most controllers already have many of the key statistics that need to be recorded stored locally in the machine. SCADA systems allow these statistics to be extracted and logged with a historian. Newer systems likely utilize an HMI (Human Machine Interface) platform which can be expanded to be used as a SCADA system without new hardware.
The captured data can be visualized through the SCADA platform to be viewed on any device connected to the network. These devices can range from a TV on the plant floor to the plant manager’s cell phone. In addition, scheduling detailed summary reports to be emailed regularly helps keep everyone in the loop. Once the data is being captured real improvement can begin.
Detailed Process Data Enables Analysis Using Machine Learning
Machine learning can be used to analyze captured historical machine data to improve the process. Machine learning can be used to predict many things including overall output, required maintenance and how machine setup parameters impact quality. The process data can be fed into software from a variety of vendors to develop and train the algorithms needed to predict improvements. However, machine learning typically requires a considerable amount of collected data to train the algorithms. Implementing data collection early is critical to being able to take full advantage of machine learning on complex processes.
Contact Maskine to get started on implementing a SCADA system to collect data or machine learning to drive improvement in your plant.