Apparatus, engine, system and method for predictive analytics in a manufacturing system
View Patent ↗A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system that may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor to execute code from a computing memory. The code may comprise an adaptor to push the received raw data to a database to processed data; an extractor to extract the processed data from the database; predictive analytics to receive the extracted processed data and apply thereto a predictive model comprised of target data for the at least one machine, and to provide feedback to the at least one machine to modify performance of the at least one machine based on the application of the predictive model; and a visualizer capable to provide at least a visualization of the feedback and the performance.
1 . A method of providing predictive analytics for a manufacturing system, comprising:
receiving, at a data input to a processing system including at least one processor which is associated with a computing memory which together are suitable for executing non-transitory code from the computing memory, an unstructured data output from a manufacturing machine operable to effect a manufacturing;
relationally forming, by one or more databases stored in the computing memory, the received unstructured data into processed structured data related, for purposes of the manufacturing, to the unstructured data;
extracting the structured processed data from the one or more databases upon preliminary identification of a type of a second manufacturing machine equivalent to the at least one manufacturing machine in relation to the manufacturing;
predictively modelling the extracted structured processed data by applying thereto at least one predictive model comprised of operational target data related to the manufacturing for the second manufacturing machine to thereby generate feedback related to the type of machine, including at least prospective first time failures for the type of machine, in performing the manufacturing;
modifying performance, including at least mitigation of the prospective first time failures, of the second manufacturing machine and the manufacturing machine based on the feedback from the predictive modelling;
displaying to a user at least a visualization of the feedback and the modified performance; and
model building for execution of the manufacturing by both the second and the manufacturing machines based on the visualization.
2 . The method of claim 1 , wherein the feedback comprises critical parameters predictive of failure by the type of machine to meet the operational target data.
3 . The method of claim 1 , wherein the operational target data to be met comprises relatively increased yield compared to that in the unstructured data output and reduced scrap compared to the unstructured data output.
4 . The method of claim 1 , wherein the modified performance comprises a modified build-of-materials.
5 . The method of claim 1 , wherein the operational target data comprises a level of line productivity.
6 . The method of claim 1 , wherein the predictive modelling and the feedback are iterative.
7 . The method of claim 1 , wherein the feedback comprises a pass or fail compared to the operational target data.
8 . The method of claim 1 , wherein the data input resides in a device layer.
9 . The method of claim 8 , wherein the device layer additionally comprises at least machine-language processing to, in part, provide the identification of the type of the manufacturing machine.
10 . The method of claim 1 , wherein the visualization results from a reporting engine suitable to generate one or more reports.
11 . The method of claim 1 , wherein the predictive modelling comprises applying a learning application that learns over repeated applications of the at least one predictive model.
12 . The method of claim 11 , wherein the predictive modelling iteratively changes over repeated generations of the feedback.
13 . The method of claim 11 , wherein the learning application comprises a supervised module.
14 . The method of claim 1 , wherein the at least one predictive model includes minimized yield loss.
15 . The method of claim 1 , wherein the displaying the visualization comprises a graphical user interface.
16 . The method of claim 15 , wherein the graphical user interface is a mobile device interface.
17 . The method of claim 1 , wherein the displaying further comprises intercommunicating the visualization between at least ones of cell managers, suppliers, operators, engineers and operations.
18 . The method of claim 17 , wherein the intercommunicating comprises indicating production line failures.
19 . The method of claim 17 , wherein the intercommunicating comprises publishing system alerts.