ABC Manufacturing is a large discrete manufacturing company with many facilities in which the fabrication of complicated components takes place.
ABC Manufacturing collects information on its processes, machines, and facilities using data from line sensors, surveys, and outcome statistics. Each of its three factories gathered vast quantities of data. To manage and find value in all of this data, ABC Manufacturing employed a team of eight data scientists. The volume, complexity, and disparity of the data collected meant that ABC’s data scientists had to implement many different models using a variety of techniques. This process took a lot of time, cost a lot of man-hours, and yielded little results from the data.
Secondly, the nature of the data scientists’ approach was to use point-in-time historical data as a base for its modelling techniques. As the data scientists got to work modelling, more recent data was pouring in by the minute. Static data modelling is very labour intensive and can take many days, weeks, or even months to produce a working model. Due to the nature of the work, the data used to generate models and predictions could be a month old by the time the data scientists had produced satisfactory results and implemented their model.
ThingWorx Machine Learning was used to automate the modelling, pattern detection, and predictive intelligence for their manufacturing processes to improve yield. The technology uses proprietary artificial intelligence and machine learning technology to automatically learn from data, discover patterns, build validated predictive models, and send information to virtually any type of application or technology.
ThingWorx Machine Learning is built to create intelligent systems by tightly integrating into applications, processes, and technologies already in place. ThingWorx Machine Learning was implemented to convert overwhelming manufacturing data into clear, actionable patterns that are constantly monitored to detect and improve the overall business efficiency and quality.
ABC Manufacturing can now better detect, avoid, and manage potential inhibitors to successful production runs within their complex discrete manufacturing processes.
ThingWorx Machine Learning has enabled the company to detect what was causing unique yield faults within a manufacturing process of more than a thousand steps using its Profiling technology.
Profiling allows ThingWorx Machine Learning to not only deliver fault detections, but it provided specific conditions that explained failure and yield fault patterns. By quickly analysing billions of points of information, ThingWorx Machine Learning determined, for example, that a particular failure pattern occurred when product line 2 was running operation 4467ANX when the ambient temperature was between 77O and 79O.