Example of Warranty Prediction

A reliability engineer wants to predict warranty claims that are caused by defective refrigerator compressors. The engineer collects and analyzes monthly failure data for the previous year.

The engineer knows that the future production schedule is for 1000 units to be shipped each month. The failure data can be modeled using a Weibull distribution. After reformatting the pre-process warranty data, the engineer uses warranty prediction to forecast future warranty claims.

  1. Open the sample data, CompressorFailures_preprocess.MTW.
  2. Choose Stat > Reliability/Survival > Warranty Analysis > Warranty Prediction.
  3. In Start time, enter Start time.
  4. In End time, enter End time.
  5. In Frequency (optional), enter Frequencies.
  6. Click Prediction. In Production quantity for each time period, enter 1000.
  7. Click OK in each dialog box.

Interpret the results

The results in the Summary of Current Warranty Claims table indicate that, of the 12,000 compressors in the field during the data collection period, 69 compressors failed. Based on the estimate obtained using a Weibull distribution, approximately 69 compressors were expected to fail during this time.

Using the Table of Predicted Number of Failures and the Predicted Number of Failures Plot, the engineer can conclude with 95% confidence that the number of additional compressors that are expected to fail within the next five months is within the interval from approximately 62 to 98 compressors.

Warranty Prediction: Start = Start time and End = End time

* NOTE * 22 cases were used; 2 cases contained missing values or zero frequencies.

Using frequencies in Frequencies

Distribution Parameters Distribution Shape Scale Weibull 1.26494 398.062 Maximum likelihood estimation method
Summary of Current Warranty Claims Total number of units 12000 Observed number of failures 69 Expected number of failures 68.5201 95% Poisson CI (53.2630, 86.7876) Number of units at risk for future time periods 11931
Production Schedule Future time period 1 2 3 4 5 Production quantity 1000 1000 1000 1000 1000
Table of Predicted Number of Failures Future Potential Predicted Time Number of Number of 95% Poisson CI Period Failures Failures Lower Upper 1 12931 13.1073 7.0000 22.3660 2 13931 27.4930 18.1933 39.8678 3 14931 43.1798 31.2722 58.1271 4 15931 60.1892 45.9516 77.4449 5 16931 78.5416 62.1373 97.9488

Predicted Number of Failures Plot

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