Abstract: An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

Abstract: An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

Abstract: An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

Abstract: An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

Abstract: An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

Abstract: There is provided systems and methods for forecasting usage of one or more parts. Such systems may receive historical usage data, generate machine learning models, generate predictions using the machine learning models, and perform one or more actions based on the generated predictions.

Abstract: In one aspect, there is provided a computer-implemented loyalty commerce network for facilitating generation of an application via an application computing device for communicating with loyalty programs via corresponding loyalty program computing devices, the network comprising: an application programming interface for receiving a request for generating said application and for generating said application based on said request and pre-defined templates associated with at least one of said loyalty programs; a loyalty program interface configured for communicating between the loyalty commerce network and said at least one loyalty program, said loyalty program interface for redirecting said request to said loyalty program; said loyalty program interface for defining a plurality of pre-defined criteria for validating said application; and, a loyalty server processor for processing said request and for validating said application in dependence upon said criteria being met.

Abstract: In one aspect, there is provided a computer-implemented loyalty commerce network for facilitating generation of an application via an application computing device for communicating with loyalty programs via corresponding loyalty program computing devices, the network comprising: an application programming interface for receiving a request for generating said application and for generating said application based on said request and pre-defined templates associated with at least one of said loyalty programs; a loyalty program interface configured for communicating between the loyalty commerce network and said at least one loyalty program, said loyalty program interface for redirecting said request to said loyalty program; said loyalty program interface for defining a plurality of pre-defined criteria for validating said application; and, a loyalty server processor for processing said request and for validating said application in dependence upon said criteria being met.