PROCESS AND SYSTEM FOR ESTIMATING RISK AND ALLOCATING RESPONSIBILITY FOR PRODUCT FAILURE

• US 20110276498A1
• Filed: 10/06/2010
• Published: 11/10/2011
• Est. Priority Date: 05/04/2010
• Status: Abandoned Application

• Current Assignee: Infernotions Technologies Ltd.
• Original Assignee: Infernotions Technologies Ltd.
• Inventors: Madhok, Varun
• Application Number:
  US12/898,803
• Publication Number:
  US 20110276498A1
• Time in Patent Office: Days
• US Class Current: 705/302
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/012 Providing warranty services
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

The invention is a process and a system for identifying the risk areas in a manufacturer'"'"'s logistic processes and for allocating responsibility for product unit failure to discrete events in the products'"'"' lifetimes. The system comprises one or multiple abuse sensors that are co-located with the product units or their containers, one or multiple readers for capturing sensor data, a data transfer utility for dispatching the recorded data to a database and an analysis module. The analysis module aggregates data across the product units returned to the manufacturer, measures the risk of product failure due to specific events of interest in the products'"'"' lifetime and estimates the associated costs.

Claims

1. A method of assessing product reliability associated with an event of interest on a given class of product, said method comprising the steps of:

retrieving warranty claim information and event data for said given class of product, whereby said event data are recorded by one or more sensors attached to said given class of product unit and said event data contain at least a timestamp associated with each recorded event;

assessing product reliability using a computer, based on warranty claim information, said recorded event data, and timestamp associated with each recorded event.

2. The method of claim 1, wherein said assessing step further comprises the steps of:

forming an analysis dataset from said warranty claim information and said event data for said given class of product, whereby the elements in said analysis dataset are associated with product units whose one or more sensors attached thereto recorded the event of interest; and

estimating product reliability based on said formed analysis dataset.

3. The method of claim 2, wherein said estimating step further comprises the steps of:

forming an observation {x₁, x₂, ..., x_n}, where each x_j, is the time to failure for the j^th element in said formed analysis dataset and n is the total number of elements in the formed analysis dataset;

estimating Weibull distribution shape parameter β and

scale parameter τ

from said observation {x₁, x₂, ..., x_n}; and

estimating the probability of product failure on or before a time D as Pr{x <

D };

wherein β

is obtained based on the solution of the following

4. The method of claim 1, wherein the assessing step includes estimating incremental cost associated with said event of interest.

5. The method of claim 4, wherein the estimating of incremental cost further comprises the steps of:

forming a first analysis dataset from said warranty claim information and said event data for said given class of product, whereby the elements in said first dataset are associated with product units whose one or more sensors attached thereto did not record the event of interest;

forming a second analysis dataset from said warranty claim information and said event data for said given class of product, whereby the elements in said second dataset are associated with product units whose one or more sensors attached thereto recorded the event of interest;

calculating an average time to failure of said first analysis dataset;

calculating an average time to failure of said second analysis dataset;

calculating an incremental cost based on the difference of the average time to failure of said first analysis dataset and the average time to failure of said second analysis dataset, a per unit production cost, a warranty period set by manufacturer, and the number of units under warranty.

6. The method of claim 5, wherein said calculating incremental warranty cost is based on nC₀W[1/T^N(1−

exp(−

W/T^N))−

1/T^T(1−

exp(−

W/T^T))],

where C₀ is the per unit production cost, W is the warranty period is, n is the number of units under warranty, T^N is the average time to failure of said first analysis dataset and T^T is the average time to failure of said second analysis dataset.

7. The method of claim 5, wherein said step of calculating average time to failure of said first analysis dataset further comprises the steps of:

estimating Weibull distribution shape parameter β

^N and Γ

^N from an observation {x₁, x₂, . . . , x_n}, where each x_j, is the time to failure for the j^th event in said first analysis dataset and n is the total number of elements in said first analysis dataset; and

estimating an average failure time as T^N=β

^Nσ

(1/Γ

^N +1) , where σ

(.) is a Gamma function.

8. The method of claim 5, wherein said step of calculating average time to failure of said second analysis dataset further comprises the steps of:

estimating Weibull distribution shape parameter β

^T and scale parameter τ

^T from an observation {y₁, y₂, y_n}, where each y_j is the time to failure for the j^th”

event in said second analysis dataset and n is the total number of elements in said second analysis dataset; and

estimating an average failure time as T^T=β

^TΓ

(1/τ

^T+1) , where Γ

(.) is a Gamma function.

9. The method of claim 1, wherein the assessing step further comprises:

forming an event time series from said event data containing said event of interest;

forming a warranty claim volume time series from said warranty claim information;

determining, using a statistical test, if the event of interest had an impact on product warranty claims;

whereby the statistical test detects a causal relationship between the event time series and the warranty claim volume time series.

12. The method of claim 1, wherein said event of interest is one of impact, drop, tip-over, extreme temperature or moisture seepage.

13. The method of claim 1, wherein at least one of said one or more sensors is a sensor selected from a group comprising accelerometer sensor, tilt sensor, temperature sensor, G-force sensor, shock sensor and GPS sensor.

10. The method of claim 10, wherein said causality test is based on the method of autoregressive time series analysis.

11. The method of claims 10 wherein said statistical test is based on principal component analysis.

14. A product reliability assessment system for use in estimating product reliability associated with an event of interest on a given class of product comprising:

a retrieving module for retrieving warranty claim information and event data for said given class of product unit, whereby said event data are recorded by one or more sensors attached to said given class of product unit and said event data contain at least a timestamp associated with each recorded event;

a forming module for forming an analysis dataset from said warranty claim information and said recorded event data, whereby the elements in said analysis dataset are associated with product units whose one or more sensors attached thereto recorded the event of interest; and

an analytical subsystem for estimating the product reliability based on said analysis dataset using at least the timestamp associated with each recorded event.

15. The system of claim 14, wherein said analytical subsystem further comprises:

a Weibull module for estimating Weibull distribution shape parameter β and

scale parameter τ

from an observation {x₁, x₂, . . . , x_n}, whereby each x_j, is the time to failure for the j^th element in the formed analysis dataset and n is the total number of elements in said formed analysis dataset; and

a probability module for estimating the probability of product failure on or before a time D as Pr{x<

D };

where β

is obtained based on the solution of the following

16. A product reliability assessment system for use in estimating product reliability associated with a product abuse event on a given class of product comprising:

a retrieving module for retrieving warranty claim information and event data for said given class of product, whereby said event data are recorded by one or more sensors attached to said given class of product and said event data contain at least a timestamp associated with each recorded event; and

an assessor for assessing impact of product abuse on subsequent warranty claims using said warranty claim information, said event data and timestamp associated with each event.

17. The system of claim 16, wherein said assessor further comprising.

a forming module for forming a first analysis dataset from said warranty claim information and said event data, whereby the elements in said first analysis dataset are associated with product units whose one or more sensors attached thereto did not record the product abuse event;

a forming module for forming a second analysis dataset from said warranty claim information and said event data, whereby the elements in said second analysis dataset are associated with product units whose one or more sensors attached thereto recorded the product abuse event; and

a first calculating module for calculating average time to failure of said first analysis dataset;

a second calculating module for calculating average time to failure of said second analysis dataset; and

an analysis sub-system for calculating an incremental cost based on the difference of the average time to failure of said first analysis dataset and the average time to failure of said second analysis dataset, a per unit production cost, a warranty period set by manufacturer, and the number of units under warranty.

18. The system of claim 16, wherein said product abuse event corresponds to the triggered measurement on one or more of accelerometer sensor, tilt sensor, temperature sensor, G-force sensor, shock sensor or GPS sensor.

19. The system of claim 17, wherein said first calculating module further comprises:

a first estimator for estimating Weibull distribution shape parameter a and scale parameter I from an observation {x₁, x₂, . . . , x_n}, where each x_j, is the time to failure for the j^th element in said first analysis dataset and n is the total number of elements in said first analysis dataset; and

a second estimator for estimating average time to failure T for said first analysis dataset;

whereby β

is based on

20. The system of claim 17, wherein said second calculating module further comprises:

a first estimator for estimating Weibull distribution shape parameter Γ and

scale parameter τ

from an observation {y₁, y₂, ..., y_n}, where each y_i is the time to failure for the j^th element in said second analysis dataset and n is the total number of elements in said second analysis dataset; and

a second estimator for estimating average time to failure T for said second analysis dataset;

whereby β

is based on