Abstract: Techniques for qualifying a candidate supplier are presented. Such techniques may include obtaining a part produced by a candidate supplier and measuring a quantifiable property in each of a plurality of samples to obtain an empirical data set. Such techniques may also include selecting, based on the empirical data set, and fitting to the empirical data set, an appropriate linear mixed model for the quantifiable property. Such techniques may further include computing an acceptance parameter from a mean and standard deviation obtained from the appropriate linear mixed model. The acceptance parameter may include a process capability index or a tolerance interval bound. Such techniques may further include determining that the candidate supplier qualifies based on comparing the acceptance parameter to a threshold.

Abstract: Aspects of the disclosure provide solutions for modeling an efficacy of disease screening and testing strategies for an infectious disease. Examples include: identifying events for a disease timeline for the infectious disease, creating a model of test sensitivity as a function of the events, adaptively mapping the events to characteristics of the infectious disease unique to a simulated infected person, based at least on adaptively mapping, creating a unique disease timeline for the simulated infected person, and creating a numerical function specific to the unique disease timeline to model sensitivity as a function of the unique disease timeline.

Abstract: Techniques for qualifying a candidate supplier are presented. Such techniques may include obtaining a part produced by a candidate supplier and measuring a quantifiable property in each of a plurality of samples to obtain an empirical data set. Such techniques may also include selecting, based on the empirical data set, and fitting to the empirical data set, an appropriate linear mixed model for the quantifiable property. Such techniques may further include computing an acceptance parameter from a mean and standard deviation obtained from the appropriate linear mixed model. The acceptance parameter may include a process capability index or a tolerance interval bound. Such techniques may further include determining that the candidate supplier qualifies based on comparing the acceptance parameter to a threshold.

Abstract: Techniques for qualifying for use in an overall manufacturing process items produced by a bulk manufacturing process that has a plurality of batch effects are presented. The techniques can include obtaining a collection of items produced by a bulk manufacturing process that has a plurality of batch effects; measuring a quantifiable property of a sample of items from the collection of items; developing a linear mixed model for the quantifiable property based on the measuring; determining a statistical process control standard deviation for the collection of items based on the linear mixed model; computing a statistical process control parameter from the statistical process control standard deviation; determining that at least a portion of the collection of items conform to the statistical process control parameter; accepting at least a portion of the collection of items; and using at least a portion of the collection of items in the overall manufacturing process.

Abstract: Techniques for qualifying for use in an overall manufacturing process items produced by a bulk manufacturing process that has a plurality of batch effects are presented. The techniques can include obtaining a collection of items produced by a bulk manufacturing process that has a plurality of batch effects; measuring a quantifiable property of a sample of items from the collection of items; developing a linear mixed model for the quantifiable property based on the measuring; determining a statistical process control standard deviation for the collection of items based on the linear mixed model; computing a statistical process control parameter from the statistical process control standard deviation; determining that at least a portion of the collection of items conform to the statistical process control parameter; accepting at least a portion of the collection of items; and using at least a portion of the collection of items in the overall manufacturing process.

Abstract: A computing system, method and computer program product dynamically manage Monte Carlo simulations. In a method, convergence criteria conditions are identified and a set of outcomes is generated following the repeated performance of one or more functions upon randomly sampled input parameter distributions. The method also determines whether the convergence criteria condition is satisfied based upon a plurality of samples. In an instance in which the convergence criteria condition is not satisfied, the method supplements the set of outcomes by repeatedly performing the one or more functions upon randomly sampled input parameter distributions. In an instance in which the convergence criteria condition is satisfied, the method determines whether the set of outcomes has been supplemented prior to satisfying the convergence criteria condition and, if the set of outcomes has not been supplemented, the size of the set of outcomes to be generated during a subsequent Monte Carlo simulation is reduced.

Abstract: A computing system, method and computer program product dynamically manage Monte Carlo simulations. In a method, convergence criteria conditions are identified and a set of outcomes is generated following the repeated performance of one or more functions upon randomly sampled input parameter distributions. The method also determines whether the convergence criteria condition is satisfied based upon a plurality of samples. In an instance in which the convergence criteria condition is not satisfied, the method supplements the set of outcomes by repeatedly performing the one or more functions upon randomly sampled input parameter distributions. In an instance in which the convergence criteria condition is satisfied, the method determines whether the set of outcomes has been supplemented prior to satisfying the convergence criteria condition and, if the set of outcomes has not been supplemented, the size of the set of outcomes to be generated during a subsequent Monte Carlo simulation is reduced.