Warranty Analysis Usage Format Example

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This example appears in the Life Data Analysis Reference book.

Warranty Analysis Usage Format Example

Suppose that an automotive manufacturer collects the warranty returns and sales data given in the following tables. Convert this information to life data and analyze it with a suitable distribution.

Quality In-Service Data

Quantity In-Service	Date In-Service
9	Dec-09
13	Jan-10
15	Feb-10
20	Mar-10
15	Apr-10
25	May-10
19	Jun-10
16	Jul-10
20	Aug-10
19	Sep-10
25	Oct-10
30	Nov-10

Quality Return Data

Quantity Returned	Usage at Return Date	Return Date
1	9072	Dec-09
1	9743	Jan-10
1	6857	Feb-10
1	7651	Mar-10
1	5083	May-10
1	5990	May-10
1	7432	May-10
1	8739	May-10
1	3158	Jun-10
1	1136	Jul-10
1	4646	Aug-10
1	3965	Sep-10
1	3117	Oct-10
1	3250	Nov-10

Solution

Create a warranty analysis folio and select the usage format. Enter the data from the tables in the Sales, Returns and Future Sales sheets. On the control panel, set the End of Observation Period 12/1/2010. Analyze the data using the 2P-Weibull distribution and RRX analysis method. The parameters are estimated to be beta = 1.315379 and eta = 102,381.486165.

The return data (accumulated mileage at time of failure and the date the failed unit was put in-service ( [math]\displaystyle{ DIS }[/math] )) is shown in the next figure.

The above figure also shows the calculation End Date ( [math]\displaystyle{ ED }[/math] ), which can be set on the Analysis page of the Control Panel. In this example, the warranty data were collected until 12/1/2010.

The user has the option to input either a constant value that describes an average of usage or a distribution with intervals to be considered in the analysis. In this example, the manufacturer has been documenting the mileage accumulation per year for this type of product across the customer base in comparable regions for many years. The yearly usage has been determined to follow a lognormal distribution with [math]\displaystyle{ {{\mu }_{T\prime }}=9.38 }[/math] , [math]\displaystyle{ {{\sigma }_{T\prime }}=0.085 }[/math] . Graphically, the distribution looks as follows.

The usage distribution is specified on the Usage page of the Control Panel, as shown next.

Note that the Interval Width was defined to be 1000 miles (as seen in the above figure). This interval facilitates the calculations of mileage usage for the suspensions in the data set.

In addition to 14 failures, the data set contains 212 suspensions spread according to the defined usage distribution. This data set contains 12 groups of suspensions that have the same age. To illustrate the analysis procedure explained above, let us use one suspension group, the first group. The first group contains 9 units that went in service on a given date; 1 unit failed from that batch. Therefore, 8 suspensions have survived from December 2009 until the beginning of December 2010, a total of 12 months. The calculations are summarized as follows.

The two columns on the right constitute the calculated suspension data (number of suspensions and their usage) for the first group. The calculation in the above table is then repeated for each of the remaining 11 suspension groups in the data set. We then add the resulting data and the failure data and proceed with estimating a failure distribution model.

The data set that is used to perform the estimation of the failure distribution model can be extracted and displayed by selecting Transfer Life Data to New Folio from the Data menu. The failures and suspensions data set, as presented in the Standard Folio, is partially shown next. (Note that the Folio contains 66 rows.)

The entered sales and return data set, the usage distribution and the interval width in the Warranty Folio can be used to derive a failure distribution. The manufacturer assumes that the observed failures are typically modeled by a lognormal distribution. The parameters of the distribution are estimated (by clicking on the Calculate button) to be:

[math]\displaystyle{ \begin{align} & {{\mu }_{T\prime }}= & 10.4747 \\ & {{\sigma }_{T\prime }}= & 1.1159 \end{align} }[/math]

The reliability plot (with mileage being the random variable driving reliability), along with the 90% confidence bounds on reliability, is shown next.