Sprint Communications Comapny L.p.

Deandra T Cassone

Abhik Barua

Paul R Sapenaro

Richard Motilal

Jason Norwood Ward

Noel R Marchbanks

George R Kather

Joseph E Dudley

Paul Harris

Mark William Reiff

Douglas Matia

Mark Schmitz

Randy Tracy

John R Reboulet

Douglas Michel Rossier

James J Kalny

John P Andrews

Kerri M Stuecken

Emillia Angelova Kancheva

A method for aggregating mobile phone orders for shipping to a recipient comprising analyzing, by a server computer, orders placed via a plurality of ordering systems, wherein the orders may comprises a plurality of ordered items; identifying, by the server computer, recipient addresses with common destinations; unifying, by the server computer, the identified recipient addresses with common destinations; determining, by the server computer, criteria for when to release an order for shipment to a recipient; accumulating, by the server computer, ordered items for the recipient; combining, by the server computer, the accumulated ordered items into a shipment, wherein a shipment comprises at least one container; and releasing, by the server computer, the container for shipment when at least one of the criteria is met.

Shipment aggregation in mobile phone order fulfillment

An order fulfillment system is provided. The system comprises logistics substations comprising an entry and exit points. The system also comprises conveyors that adaptively couple the logistics substations in a serialized order fulfillment logistics workflow to convey an order from a selected one of the entry points of one of the logistics substations to a selected plurality of logistics substations where order fulfillment procedures are performed on the order and to a selected one of the exit points of one of the logistics substations. The system also comprises a computer system that receives batches of orders, that analyzes a trend of orders, that analyzes a current inventory, that analyzes a current topology of the serialized fulfillment logistics workflow, and that changes the topology of the serialized fulfillment logistics workflow by commanding the plurality of conveyors to change their coupling of the logistics substations, based on the analysis.

Order fulfillment logistics workflow

A system for inventory loss prediction is provided. The system comprises a processor, a memory, and an application stored in the memory that, when executed by the processor, receives a report associating electronic serial numbers of devices with a plurality of instances of inventory loss. The system also creates a list of device characteristics associated with the devices, creates a list of store characteristics associated with retail stores to which the devices were shipped, and determines a first statistical correlation of a device characteristic to inventory loss. The system also determines a second statistical correlation a store characteristic to inventory loss and predicts, based on the first statistical correlation and the second statistical correlation, a probability of inventory loss.

Inventory loss prediction

A system of inventory variance detection modeling is described. The system comprises a server computer, a processor, a memory, and an application stored in the memory that, when executed by the processor, receives a report associating electronic serial numbers of devices with a plurality of instances of inventory shrinkage. The system also assembles a list of electronic serial numbers of devices associated with instances of a first type of inventory shrinkage. The system also receives a report of the processing history of the devices associated with the electronic serial numbers contained in the list in a plurality of non-logistics and non-warehousing systems. The system also determines the statistical correlation of electronic serial numbers contained in the list with each of a plurality of exceptions processed by at least one of the non-logistics and non-warehousing systems.

Inventory variance detection modeling

A system for customer care complexity management is disclosed that comprises a processor, a customer care index forecast module, and a project launch capability assessment and threshold module. The customer care index forecast module establishes a customer care index forecast model based on a plurality of historical customer care data and a plurality of historical performance metrics data. The customer care index forecast module also forecasts a customer care index using the established customer care index forecast model and a plurality of projected performance metrics data. The project launch capability assessment and threshold module identifies a number of projects to be launched in a current project launch plan based on the forecast customer care index and a pipeline project complexity index for the project launch plan. The project launch capability assessment and threshold module also identifies an adjustment to the project launch plan.

System and method for customer care complexity model

A computer implemented method is provided for flexible project governance based on predictive analysis. Project complexity factors are analyzed to determine a project complexity index. Initial development gates are selected for a project based on the project complexity index. Project governance factors are periodically analyzed to determine a project governance index. Actions for project reporting and for the initial development gates are selected based on the project governance index.

Flexible project governance based on predictive analysis

A processor-implemented method executing on a computer for using customer behavior to implement product design is provided. The method comprises the computer identifying a set of behavioral profiles that captures at least about sixty percent of a customer base, identifying products from a current offering that substantially match behavioral traits associated with each profile, and studying usage patterns of the products by customers, wherein the patterns are those of a plurality of customers using the products. The method also comprises the computer refining the profiles of the customers using the products, identifying underutilized features of the products based on patterns and one of discontinuing and repositioning the underutilized features, and identifying features indicated as desirable based on usage patterns and adding the features to the identified products; wherein the products comprise at least one of hardware, software executing on the hardware, and services associated with the use of the products.

Habituation oriented product selection system

Systems and methods are provided for problem ticket resolution. A resolution component allocates a cause for each problem ticket, assigned by resolution teams to root cause clusters, to systems based on historical records that identify one of multiple historical systems as a cause for each historical problem ticket. If a corresponding number of causes allocated to a system is a predetermined amount greater than a corresponding historical number of causes allocated to the system, the resolution component may output a notification to a user interface to prompt a management action regarding the system, access a report associated with the system and output a notification to the user interface to prompt a management action regarding the system based on the report, or assign a set of problem tickets allocated to the system to a resolution team.

Problem ticket cause allocation

A method is provided for risk management in project management. A risk is identified for a project during a concept phase for the project prior to an analysis phase for the project. A probability for an identified risk and an impact severity for the identified risk are analyzed to determine a risk exposure for the project. A risk response plan is developed based on the risk exposure. The risk exposure is mitigated by executing a portion of the risk response plan.

Method for identifying risks for dependent projects based on an enhanced telecom operations map

Computer implemented methods and systems are provided for identifying high to high complexity projects. Some method embodiments include defining proposed project data for identified factors, calculating a planning team index for a project associated with the proposed project data, and identifying the project as complex when the planning team indicator falls on one side of a breaking point. The factors are those identified as determinative of project complexity.

Systems and methods for identifying high complexity projects

A computer implemented system for project prediction is provided. The system includes a data manager to obtain historical project data. The system also includes an analyzer to analyze the historical project data and an analysis cycle time to generate models for a proposed project cycle time. Additionally, the system includes a user interface to select one model for the proposed project cycle time, wherein the selected model includes linear sub-models corresponding to a historical data range, and apply proposed project data and analysis cycle time to one linear sub-model corresponding to a proposed data range to predict the proposed project cycle time. Furthermore, the system captures proposed project data and obtains additional project data to update the selected model. The models provide for the accurate prediction of cycle times, or project costs, in an enterprise development environment.

Enhanced project predictor

A computer implemented system for project prediction is provided. The system includes a data manager to obtain historical project data. The system also includes an analyzer to analyze the historical project data to generate models for a proposed project cycle time. Additionally, the system includes a user interface to select one model for the proposed project cycle time, wherein the selected model includes linear sub-models corresponding to a historical data range, and apply proposed project data to one linear sub-model corresponding to a proposed data range to predict the proposed project cycle time. Furthermore, the system captures proposed project data and obtains additional project data to update the selected model. The models provide for the accurate prediction of cycle times, or project costs, in an enterprise development environment.

Growing community of inventors

Overland Park, KS, United States of America

Deandra T Cassone