Evaluating device readiness

Patent metadata

Field	Value
Publication number	US-2016019564-A1
Application number	US-201414337057-A
Country	US
Kind code	A1
Filing date	Jul 21, 2014
Priority date	Jul 21, 2014
Publication date	Jan 21, 2016
Grant date	—

Abstract

Systems and methods for forecasting device return rates are described. Some implementations include initializing a model representing a device's readiness for market based on one or more key performance indicators (KPIs) of the device, the model being represented by a curve, computing differences between measured value of KPIs of the device and values of KPIs fitted to the curve, identifying an inflection point of the curve based on the computed differences, interpreting the shape of the curve based on the identified inflection point, a readiness index and the curvature of the curve and determining a state of readiness of the device based on the interpreted shape of the curve.

First claim

What is claimed is:

1 . A method comprising:
initializing, using one or more processors, a model representing a device's readiness for market based on one or more key performance indicators (KPIs) of the device, wherein the model is represented by a curve; computing, using the one or more processors, differences between measured value of KPIs of the device and values of KPIs fitted to the curve; identifying, using the one or more processors, an inflection point of the curve based on the computed differences, wherein the inflection point represents a point on the curve at which curvature of the curve changes sign; interpreting, using the one or more processors, the shape of the curve based on the identified inflection point, a readiness index and the curvature of the curve, wherein the device readiness index is based at least on a KPI that takes the most amount of time relative to other KPIs to cross a manufacturing performance threshold represented in the curve; determining, using the one or more processors, a state of readiness of the device based on the interpreted shape of the curve; and based on the determined state of readiness of the device, providing, using the one or more processors, one or more instructions to supply chain components to adjust supply chain operations.

2 . The method of claim 1 , further comprising adjusting, using the one or more processors, the initialized model including the curve to conform to the state of readiness of the device, wherein the adjusting is triggered by conditions including a mean error rate of the KPIs.

3 . The method of claim 1 , further comprising:
traversing, using the one or more processors, each measured KPI for the device, to identify the KPI that consumes most time to cross the manufacturing performance threshold represented in the curve; and
generating, using the one or more processors, a report indicating the identified KPI.

4 . The method of claim 1 , further comprising:
classifying, using the one or more processors, a manufacturer of the device into an operational performance category based on the interpreted shape of the curve; and generating, using the one or more processors, a report indicating the operational performance category.

5 . The method of claim 1 , further comprising:
when the interpreted shape is determined to be a concaved downward logarithmic curve, generating a report indicating that a manufacturer of the device attempts to resolve performance issues with the device at an early stage in a manufacturing process.

6 . The method of claim 1 , further comprising when the interpreted shape is determined to be a concaved downward polynomial curve, generating a report indicating that a manufacturer of the device attempts to resolve performance issues close to a cut-off release date in a manufacturing process.

7 . The method of claim 1 , further comprising when the interpreted shape is determined to be an s-curve, generating a report indicating that a manufacturer of the device attempts to resolve performance issues in close accordance with a pre-determined schedule prior to a cut-off release date in a manufacturing process.

8 . An analytics engine comprising:
a communication interface configured to enable communication via a mobile network; a processor coupled with the communication interface; a storage device accessible to the processor; and an executable program in the storage device, wherein execution of the program by the processor configures the server to perform functions, including functions to:
initialize a model representing a device's readiness for market based on one or more key performance indicators (KPIs) of the device, wherein the model is represented by a curve;
compute differences between measured value of KPIs of the device and values of KPIs fitted to the curve;
identify an inflection point of the curve based on the computed differences, wherein the inflection point represents a point on the curve at which curvature of the curve changes sign;
interpret the shape of the curve based on the identified inflection point, a readiness index and the curvature of the curve, wherein the device readiness index is based at least on a KPI that takes the most amount of time relative to other KPIs to cross a manufacturing performance threshold represented in the curve;
determine a state of readiness of the device based on the interpreted shape of the curve; and
based on the determined state of readiness of the device, provide one or more instructions to supply chain components to adjust supply chain operations.

9 . The analytics engine of claim 8 , wherein execution of the program by the processor configures the server to perform functions, including functions to:
adjust the initialized model including the curve to conform to the state of readiness of the device, wherein the adjusting is triggered by conditions including a mean error rate of the KPIs.

10 . The analytics engine of claim 8 , wherein execution of the program by the processor configures the server to perform functions, including functions to:
traverse each measured KPI for the device to identify the KPI that consumes most time to cross the manufacturing performance threshold represented in the curve; and generate a report indicating the identified KPI.

11 . The analytics engine of claim 8 , wherein execution of the program by the processor configures the server to perform functions, including functions to:
classify a manufacturer of the device into an operational performance category based on the interpreted shape of the curve; and generate a report indicating the operational performance category.

12 . The analytics engine of claim 8 , wherein execution of the program by the processor configures the server to perform functions, including functions to:
when the interpreted shape is determined to be a concaved downward logarithmic curve, generate a report indicating that a manufacturer of the device attempts to resolve performance issues with the device at an early stage in a manufacturing process.

13 . The analytics engine of claim 8 , wherein execution of the program by the processor configures the server to perform functions, including functions to:
when the interpreted shape is determined to be a concaved downward polynomial curve, generate a report indicating that a manufacturer of the device attempts to resolve performance issues close to a cut-off release date in a manufacturing process.

14 . The analytics engine of claim 8 , wherein execution of the program by the processor configures the server to perform functions, including functions to:
when the interpreted shape is determined to be an s-curve, generate a report indicating that a manufacturer of the device attempts to resolve performance issues in close accordance with a pre-determined schedule prior to a cut-off release date in a manufacturing process.

15 . A non-transitory computer-readable medium comprising instructions which, when executed by one or more computers, cause the one or more computers to:
initialize a model representing a device's readiness for market based on one or more key performance indicators (KPIs) of the device, wherein the model is represented by a curve; compute differences between measured value of KPIs of the device and values of KPIs fitted to the curve; identify an inflection point of the curve based on the computed differences, wherein the inflection point represents a point on the curve at which curvature of the curve changes sign; interpret the shape of the curve based on the identified inflection point, a readine

Assignees

• Verizon Patent & Licensing Inc

Inventors

• Ouyang Ye
• Becht Carol
• Venkatasubramaniam Gopinath
• Schmidt Christopher M
• Lowe Benjamin

Classifications

• G06Q10/06393

  Score-carding, benchmarking or key performance indicator [KPI] analysis · CPC title

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title