Ensures that you’re not just collecting data, but you’re collecting clean, relevant and actionable data
Before any analysis and modelling can happen, the data collected in a manufacturing plant needs to be “clean”. The right sensors must be placed in the right locations, transmitting their data and relaying the information back to a server where a database stores the records. Some manufacturers may collect data from hundreds of sensors but they use only a portion of the information.
The first step in the optimization process is to access the historical data kept on the server and analyze it. The analysis will determine if the data collected is “clean” for the purpose of optimization of the manufacturing process or whether any adjustments need to be made.
For example, the data assessment may uncover the need to replace a faulty sensor. or, it may discover a gap in the data collection that needs to be filled. The industry and manufacturing expertise at ProcessMiner helps to guide the analysis of the historical data and determine what’s needed to make sure the information is useful and trust-worthy.
Predicts performance measures and identifies improvement opportunities.
With the modelling in place, the process moves to the prediction phase where the machine-learning system begins using data from the real-life manufacturing to forecast what will happen during the actual production run.
Monitoring occurs with artificial intelligence running in the background that can predict the outcomes in the future as opposed to other systems that simply react to what has already occurred during the manufacturing process.
The difference allows ProcessMiner to provide plant owners and operators with real-time actionable recommendations to improve operational efficiency and product quality.
Easy to read suggestions for minimizing variability though improvement, preventative and corrective actions.
For recommendations, operators have access to web-based interfaces that display data generated by the ProcessMiner platform.
On the screen, the operators can view several key performance metrics along with suggestions for adjusting certain inputs to improve results. The recommendations are provided based on their potential impact and are regularly updated during the production run.
If the operator decides to implement the recommendation, the alert disappears once the system recognizes the change and observes an improvement in the manufacturing process.
With each observation, adjustment and recommendation, the ProcessMiner machine learning program gains more knowledge of the manufacturing process and uses this data to make future predictions and forecasts. In fact, it uses this knowledge to optimize the process over time.
Continual improvement that optimizers process performance and variability.
The longer its used, the more ProcessMiner improves. Its machine learning and artificial intelligence adjust for the constantly changing data that the sensors relay from the manufacturing process. Because the data and the manufacturing conditions must be ongoing if the system is expected to provide predictions for the future.
Otherwise, the system just reacts to what’s already happened instead of preventing problems before they happen.
Each improvement raises the bar for ProcessMiner. But, thanks to the unique manufacturing experience of the team, the platform is expected to continually evolve and improve. The finished state is a fully-integrated system that delivers ongoing solutions within a dynamic environment.
Applies advanced data science and machine learning techniques to your manufacturing process to find insights and impact ROI.
With good, clean data in hand, the next step is to learn from the data and create models for finding the relationship between manufacturing process variables and parameters, machine settings, and key measures using machine learning techniques.
The modeling permits analysis of the manufacturing process without altering the real-life operations. It also allows data scientists to test theories of how the manufacturing process might change if certain elements such as temperature and pressure are adjusted. It can get complicated very quickly, especially when multi-response modeling is applied to the predictive analysis of manufacturing systems.
Often, the modeling will focus on a specific issue with a potential for a sizable return on investment. Sometimes, the business wants to use fewer raw materials in the manufacturing process, thereby reducing costs. Or, it may want to improve the quality scores for its products, which can lead to an increase in volume to be sold.
What separates the modeling at ProcessMiner from the competition is the integration of data-driven and domain-expertise solutions. The platform leverages deep industry knowledge and subject matter expertise obtained from decades of manufacturing process experience – it adds a healthy dose of human intelligence to the artificial kind obtained from data. In practice, this leads to better insights and more focused optimizations that only people with specialized industry knowledge can offer.
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