Abstract: A system is presented for predicting the amount of energy required by a manufacturing plant over a period of time, and then providing a recommendation on the best amount of energy to sell at each period, taking into consideration the energy requirements based on the production schedule, the price of energy, possible penalties for over selling energy, and other factors. A user interface is provided to present relevant information to a user, allow the user to plan the use of energy, and determine the energy offering for sale in each period. Based on a known future production schedule of a steel mill, implementations provide a forecast of electrical energy usage per hour over the same time horizon as the production schedule. Based on the forecast and electricity selling and billing rules, a recommended amount of energy to be sold to the market by the hour is presented with the corresponding offer price.

Abstract: A system is presented for predicting the amount of energy required by a manufacturing plant over a period of time, and then providing a recommendation on the best amount of energy to sell at each period, taking into consideration the energy requirements based on the production schedule, the price of energy, possible penalties for over selling energy, and other factors. A user interface is provided to present relevant information to a user, allow the user to plan the use of energy, and determine the energy offering for sale in each period. Based on a known future production schedule of a steel mill, implementations provide a forecast of electrical energy usage per hour over the same time horizon as the production schedule. Based on the forecast and electricity selling and billing rules, a recommended amount of energy to be sold to the market by the hour is presented with the corresponding offer price.

Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component are provided herein. A method includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each sub-time series comprises multiple data points of the input time series that correspond to measurements derived from the vehicular component; determining a weight applied to each of the sub-time series based on a pre-determined weight associated with the input time series; applying a failure or non-failure classification label to each of the sub-time series and the input time series; calculating a performance measure for the input time series; determining an updated weight associated with the input time series; and generating an estimate of failure risk for the vehicular component based on the classification label applied to each input time series and the updated weight.

Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series; generating a first failure status predicting function of a first selected sub-time series; deleting, from the first input time series, the portion of the data points that corresponds to the first selected sub-time series; repeating the preceding two steps for a second selected sub-time series; generating a second failure status predicting function of each selected sub-time series; applying each second failure status predicting function to a second input time series to calculate prediction of failure values; and identifying the largest prediction of failure value as an estimate of failure risk for the vehicular component.

Abstract: Methods, systems, and computer program products for generating wear-based indicators are provided herein.

Abstract: Methods, systems, and computer program products for generating a vehicular component replacement policy are provided herein.

Abstract: Methods, systems, devices and computer program products for planning for failures of vehicular components are provided herein. A method includes obtaining a first set of data of maintenance events recorded for multiple vehicular components across multiple vehicles in a fleet; obtaining a second set of data of maintenance work orders performed on the vehicular components; obtaining a third set of data of measurements taken in connection with the vehicular components; analyzing (i) the first set of data and (ii) the second set of data to identify component failure events associated with the multiple vehicular components; determining failure indicators for each of the vehicular components in each of the vehicles in the fleet based on the second set of data, the third set of data, and the identified component failure events; and outputting the indicators in multiple visualized forms, each representing one of multiple levels of granularity.

Abstract: Methods, systems, and computer program products for generating wear-based indicators are provided herein.

Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series; generating a first failure status predicting function of a first selected sub-time series; deleting, from the first input time series, the portion of the data points that corresponds to the first selected sub-time series; repeating the preceding two steps for a second selected sub-time series; generating a second failure status predicting function of each selected sub-time series; applying each second failure status predicting function to a second input time series to calculate prediction of failure values; and identifying the largest prediction of failure value as an estimate of failure risk for the vehicular component.

Abstract: Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component are provided herein. A method includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each sub-time series comprises multiple data points of the input time series that correspond to measurements derived from the vehicular component; determining a weight applied to each of the sub-time series based on a pre-determined weight associated with the input time series; applying a failure or non-failure classification label to each of the sub-time series and the input time series; calculating a performance measure for the input time series; determining an updated weight associated with the input time series; and generating an estimate of failure risk for the vehicular component based on the classification label applied to each input time series and the updated weight.

Abstract: Methods, systems, devices and computer program products for planning for failures of vehicular components are provided herein. A method includes obtaining a first set of data of maintenance events recorded for multiple vehicular components across multiple vehicles in a fleet; obtaining a second set of data of maintenance work orders performed on the vehicular components; obtaining a third set of data of measurements taken in connection with the vehicular components; analyzing (i) the first set of data and (ii) the second set of data to identify component failure events associated with the multiple vehicular components; determining failure indicators for each of the vehicular components in each of the vehicles in the fleet based on the second set of data, the third set of data, and the identified component failure events; and outputting the indicators in multiple visualized forms, each representing one of multiple levels of granularity.

Abstract: Methods, systems, and computer program products for generating a vehicular component replacement policy are provided herein.

Abstract: Methods, systems, and computer program products for generating wear-based indicators for vehicular components are provided herein. A method includes assigning a failure class label to each data point, from multiple data points derived from measurements associated with a vehicular component across a fleet of vehicles, that is within a pre-specified number of runtime hours of a replacement; assigning a non-failure class label to each data point not within the pre-specified number of runtime hours of a replacement and each data point associated with a component yet to be replaced; estimating a failure probability at each data point over a pre-specified future runtime of the component based on the assigned class label; determining a cumulative hazard function for the vehicular component based on the failure probability; and generating a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on the cumulative hazard function.

Abstract: Methods, systems, and computer program products for generating wear-based indicators for vehicular components are provided herein. A method includes assigning a failure class label to each data point, from multiple data points derived from measurements associated with a vehicular component across a fleet of vehicles, that is within a pre-specified number of runtime hours of a replacement; assigning a non-failure class label to each data point not within the pre-specified number of runtime hours of a replacement and each data point associated with a component yet to be replaced; estimating a failure probability at each data point over a pre-specified future runtime of the component based on the assigned class label; determining a cumulative hazard function for the vehicular component based on the failure probability; and generating a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on the cumulative hazard function.

Abstract: A system gathers real-time data about an enterprise over a network. A user device displays a dashboard showing a component business model map, a heat map, key performance indicators for components, key performance indicators for the enterprise, and gradients. The business is enhanced by making investments and changes to those operations having large gradients.

Abstract: A business enterprise is assessed by a consultant using a computer implemented tool for practicing component business modeling techniques. The tool builds a map of components, filters the map to form a heat map and identifies collaborations. Key performance indicators are evaluated.

Abstract: A system generates a product promotion dynamically personalized to a customer in a retail store. The customer is identified with a device coupled to a computer processor. A product interest device, also coupled to the processor detects the customer's interest in a product. Transaction history data and current promotion data is retrieved from online storage media. The system determines the profitability of the customer, the profitability of the product, and the business intention at that point in time. Along with the transaction history data and current promotion data the system creates a promotion for the customer and presents the promotion on a digital media display.

Abstract: A system gathers real-time data about an enterprise over a network. A user device displays a dashboard showing a component business model map, a heat map, key performance indicators for components, key performance indicators for the enterprise, and gradients. The business is enhanced by making investments and changes to those operations having large gradients.

Abstract: A system creates and maintains an industry business architecture model (IBAM) over a network. The IBAM has a component business model, process model, service model, and information model. It also may include a reference architecture, demos and other knowledge assets, and collateral.

Abstract: A system and dashboard are provided for managing business performance. Key performance indicators developed for a component business model map are computed and displayed along with the map in real time. A user may select a corresponding key performance indicator to display a heat map of components on the dashboard.