US 11,775,911 B2
Method and apparatus for providing predictions of key performance indicators of a complex manufacturing system
Felix Baer, Fuerth (DE); Niklas Buerger, Nuremberg (DE); Hans-Henning Klos, Weigendorf (DE); Christoph Paulitsch, Karlsruhe (DE); and Jens Schnittger, Lonnerstadt (DE)
Assigned to Siemens Aktiengesellschaft, Munich (DE)
Appl. No. 17/609,852
Filed by Siemens Aktiengesellschaft, Munich (DE)
PCT Filed Mar. 27, 2020, PCT No. PCT/EP2020/058743
§ 371(c)(1), (2) Date Nov. 9, 2021,
PCT Pub. No. WO2020/224869, PCT Pub. Date Nov. 12, 2020.
Claims priority of application No. 19173418 (EP), filed on May 9, 2019.
Prior Publication US 2022/0230117 A1, Jul. 21, 2022
Int. Cl. G06Q 50/04 (2012.01); G06Q 10/0639 (2023.01); G06Q 10/0633 (2023.01)
CPC G06Q 10/06393 (2013.01) [G06Q 10/0633 (2013.01); G06Q 10/06395 (2013.01); G06Q 50/04 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A computer-implemented method for increasing production efficiency of a complex manufacturing system, the method configured for providing predictions of key performance indicators of a product variant of a product family to be manufactured by machines of the complex manufacturing system in a manufacturing process, which comprises the steps of:
providing a processing unit of the complex manufacturing system with a manufacturing operation model for each manufacturing operation type used to manufacture the product variant of the product family;
providing, via the complex manufacturing system, measured contributions to the key performance indicators, process context data and process execution data of manufacturing operations;
learning automatically, via the processing unit, model parameters of manufacturing operation models based on the process context data collected, the process execution data collected, and the measured contributions to the key performance indicators to update the manufacturing operation models, the learning of the model parameters of the manufacturing operation models includes an iterative adaption of the model parameters which uses a neural network, decision trees or a support vector machine;
evaluating an updated production efficiency model combining updated manufacturing models including updated manufacturing operation models to calculate predictions of the key performance indicators of the product variant to be manufactured by the complex manufacturing system depending on a product configuration of a respective product variant, wherein the key performance indicators predicted for different production variants of a same product variant are evaluated to select automatically by the complex manufacturing system, a most efficient production variant; and
manufacturing by the complex manufacturing system a product variant according to a selected production variant.