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	Times-to-Failure Format

Example of a Times-to-Failure Warranty Analysis

A manufacturer sold two versions of a product. In addition, they plan to sell more of the product in the next few months. The objective is to analyze the data for both product versions and provide a return forecast for the next 10 months.

The following data sheet shows the data of the product. The return period (State End Time) is measured in months. The manufacturer logs the number of failures and suspensions for each product version at the end of each month.

In the Future Sales data sheet, they enter the number of units they plan to ship to stores in the next 9th and 10th months, as shown next.

To analyze each product version, select the Use Subsets option on the Main page of the control panel. Next, for both subset IDs, choose the 2P-Weibull distribution and the MLE analysis method. Click Calculate. The parameters for V1 are estimated to be: beta = 2.3819 and eta = 25.3976; for V2, the parameters are estimated to be: beta = 2.3207 and eta = 21.2829.
On the control panel, click the Plot icon. The following plot shows the probability of failure of the two product versions. As you can see, V2 has a higher probability of failure compared to V1.

Next, generate a warranty forecast to predict the expected number of product returns. To do this, return to the data sheet and then choose Warranty > Tools > Generate Forecast or click the icon on the control panel.

In the Forecast Setup window, enter a Start time of 1 and a forecast length of 10 periods, as shown next. Set the Increment to 1 and click OK.

On the control panel of the Forecast sheet, select the Show Subset ID check box. The following figure shows the results (showing only the first 8 columns).

The intervals in the warranty period are indicated by the Current Age plus the increment that was specified in the Forecast Setup window. In the sheet shown above, the first row in the data sheet tells us that 1108 (Number in State) units were operating in the field when the warranty period started and that these unit will have survived at least 1 month (Current Age) of operation. In the 2nd month of operation (Current Age+1), 2 units from that batch are expected to fail. After 4 months of operation (Current Age+3), 7 more units from the batch are expected to fail, and so on. Note that the last 4 rows of the Forecast sheet will show the expected number of returns from the future sales. (In this example, the returns are expected to start at Current Age+10.)

Next, click the Plot icon on the control panel of the Forecast sheet. In the Plot Type drop-down field, choose Expected Failures. The following plot shows the trend in the expected number of failures over the next 10 periods. This forecast analysis can help the manufacturer predict the failure behavior of each product version in the field, as well as plan for the warranty costs and service for each product version.