The invention relates to a method for producing a plurality of mechanical devices, in which each mechanical device comprises a defined number N of identical parts to be assembled, the parts to be assembled having been produced according to a set of specifications including at least one compliance specification, the parts that meet the compliance specification being compliant parts and the parts that do not meet the compliance specification being non-compliant parts, characterized in that production is controlled in such a way that the number of mechanical devices containing a number of non-compliant parts strictly higher than a threshold value n1 are in a proportion less than or equal to a proportion p1, the proportion p1 being non-zero and strictly lower than 1. The invention also relates to a method for repairing a mechanical device that has been produced with such a production method.

The invention pertains to a method of manufacturing a population of parts produced with a manufacturing device, based on the analysis of at least one statistical indicator representative of a characteristic dimension of the parts, according to which: a) a sample comprising a number n of parts is collected from among the parts produced with the manufacturing device; b) the characteristic dimension of each part of the sample is measured, and a measured value of the statistical indicator is calculated for the sample; c) a mathematical expectation of the proportion of parts which are noncompliant with respect to a specification regarding the characteristic dimension is calculated, said calculation being performed on the basis of the measured value of the statistical indicator for the sample collected and of the number n of parts of the sample; d) the mathematical expectation of the proportion of parts that are noncompliant calculated is compared with a threshold value of proportion of noncompliant parts; e) the manufacture of the parts is steered as a function of the results of the comparison of step d).

An intellectual quality management method is disclosed. A heatmap risk interface is created according to the required data and the parameter configuration which are calculated using a time dependent risk priority number (RPN) equation. An intellectual audit scheduling algorithm is defined via the heatmap risk interface to automatically generate at least one audit plan. An audit program corresponding to the audit plan is performed and a plurality of problem points are selected. Intellectual root cause category recommendation is performed to the questions points. intellectual corrective actions and preventive action recommendations are performed to the problem points according to the intellectual root cause category recommendation to obtain at least one optimum corrective action and at least one preventive action. Corrective actions are performed to each audit unit according to the corrective action to solve the problem points and prevention actions are performed to each audit unit according to the preventive action.

Aspects of the application relate to computer-implemented methods, process control devices, and a computer program. According to one aspect, a computer-implemented method for controlling a process in a plurality of first scale vessels via a first process control device is provided. Each of the first scale vessels contains fluid and the process is for producing a chemical, pharmaceutical, biopharmaceutical and/or biological product. The method comprises controlling, by the first process control device and at least partially in parallel, the process in each of the first scale vessels. The method can include periodically determining, prior to an assigning decision and at a first frequency, first sets of process parameter values for each of the process parameters from each of the first scale vessels.

An information processing device includes processing circuitry configured to obtain a plurality of sets of data related to a processing target, group relationships among the sets of data that are obtained, based on group information set in advance, calculate degrees of importance indicating strengths of cause-and-effect relationships among sets of data included in each group, and calculate, based on the degrees of importance, estimation values indicating the cause-and-effect relationships among sets of data.

An intellectual quality management method is disclosed. A heatmap risk interface is created according to the required data and the parameter configuration which are calculated using a time dependent risk priority number (RPN) equation. An intellectual audit scheduling algorithm is defined via the heatmap risk interface to automatically generate at least one audit plan. An audit program corresponding to the audit plan is performed and a plurality of problem points are selected. Intellectual root cause category recommendation is performed to the questions points. intellectual corrective actions and preventive action recommendations are performed to the problem points according to the intellectual root cause category recommendation to obtain at least one optimum corrective action and at least one preventive action. Corrective actions are performed to each audit unit according to the corrective action to solve the problem points and prevention actions are performed to each audit unit according to the preventive action.

A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

An abnormality score calculation apparatus according to an embodiment includes a processing circuit configured to: acquire first data concerning a status of a product or a manufacturing process; calculate based on the first data an abnormality score for a respective one of a plurality of abnormality modes or for a respective one of a plurality of pieces of the first data of various types; and convert a scale of a respective one of a plurality of abnormality scores including the abnormality score in such a manner that the abnormality scores become substantially equal in occurrence degree.

The present invention includes: (a) a method for improving data to be processed for bottleneck detection, by cleaning corrupt or outlier data; (b) a method for improved analysis of bottleneck data using a plurality of rules for categorization; and (c) a method for improved display and/or allowing improved user feedback for bottleneck data using multivariate analysis and display. These methods can be used alone, or preferably be combined in whole or in part together to improve performance of an industrial process. A system is also provided.

Disclosed is a method for determining a limit, comprising: providing a plurality of sample values, wherein the sample values define a sample value distribution, the sample values being values of a technical parameter related to a sample, wherein the sample items are parts of drug delivery devices, wherein the sample items are of the same construction, and wherein the technical parameter is limited by at least one technical limit value, depending on the technical parameter and/or the sample values, choosing a probability distribution function, using the technical limit value to determine a cutoff value for the probability distribution function, specifying a probability content for the tolerance interval, and providing the limit of the tolerance interval for the technical parameter based on a transformed probability content, wherein said transformed probability content is based on the cutoff value and based on the specified probability content.