Abstract: A computer implemented method comprising receiving one or more predictive maintenance models each defining a time-based probability of failure for one or more components, receiving current performance data for the components, defining a failure function for each component from a predictive maintenance model for the component and the current performance data for the component, the failure function defining the probability of failure of the component in each of a set of time periods, defining a value loss function for each component from the failure function for the component and a time-based component cost, the value loss function defining the expected value loss due to a planned replacement of the component in a given time period before the component fails or reaches its scheduled end-of-life, receiving data defining one or more factors that have an impact on the cost of a maintenance option.

Abstract: A computer implemented method comprising receiving one or more predictive maintenance models each defining a time-based probability of failure for one or more components, receiving current performance data for the components, defining a failure function for each component from a predictive maintenance model for the component and the current performance data for the component, the failure function defining the probability of failure of the component in each of a set of time periods, defining a value loss function for each component from the failure function for the component and a time-based component cost, the value loss function defining the expected value loss due to a planned replacement of the component in a given time period before the component fails or reaches its scheduled end-of-life, receiving data defining one or more factors that have an impact on the cost of a maintenance option.

Abstract: A computer implemented method comprising receiving one or more predictive maintenance models each defining a time-based probability of failure for one or more components, receiving current performance data for the components, defining a failure function for each component from a predictive maintenance model for the component and the current performance data for the component, the failure function defining the probability of failure of the component in each of a set of time periods, defining a value loss function for each component from the failure function for the component and a time-based component cost, the value loss function defining the expected value loss due to a planned replacement of the component in a given time period before the component fails or reaches its scheduled end-of-life, receiving data defining one or more factors that have an impact on the cost of a maintenance option.

Abstract: A computer implemented method, computer program product and system for predicting an outcome of the execution of a schedule. The schedule, endogenous data relating to one or more activities or the schedule, and exogenous data relating to one or more activities or the schedule is processed in accordance with a machine learning algorithm-generated prediction model to predict at least one of: a performance measure, and a final condition that may result from executing the one or more activities according to the schedule.

Abstract: A computer implemented method comprising receiving one or more predictive maintenance models each defining a time-based probability of failure for one or more components, receiving current performance data for the components, defining a failure function for each component from a predictive maintenance model for the component and the current performance data for the component, the failure function defining the probability of failure of the component in each of a set of time periods, defining a value loss function for each component from the failure function for the component and a time-based component cost, the value loss function defining the expected value loss due to a planned replacement of the component in a given time period before the component fails or reaches its scheduled end-of-life, receiving data defining one or more factors that have an impact on the cost of a maintenance option.

Abstract: A system for handling a production disturbance/opportunity event includes a network having data processing units running a MES software for controlling/monitoring a production process operating production components. A production modeler defines a plant model of the production process, and a production scheduler generates an executable production schedule for the execution of operating procedures for the production components. A production executor executes the production process following the generated executable production schedule, and a production disturbance/opportunity event identifier detects a production disturbance event and identifies a production opportunity event which allows to maintain an efficiency of the production process at least as close as possible to an efficiency given by the executable production schedule as originally generated by the production scheduler.

Abstract: In a method for production planning in a manufacturing execution system of a shop floor, the following steps are performed: obtaining shop floor data from the shop floor; analyzing the shop floor data using detection logic to detect a disturbance and to provide an opportunity for a corrective action; and generating a production schedule based on the detected disturbance and opportunity for a corrective action by a scheduler.

Abstract: A system for handling a production disturbance/opportunity event includes a network having data processing units running a MES software for controlling/monitoring a production process operating production components. A production modeler defines a plant model of the production process, and a production scheduler generates an executable production schedule for the execution of operating procedures for the production components. A production executor executes the production process following the generated executable production schedule, and a production disturbance/opportunity event identifier detects a production disturbance event and identifies a production opportunity event which allows to maintain an efficiency of the production process at least as close as possible to an efficiency given by the executable production schedule as originally generated by the production scheduler.