[Editor's Note: This article has been updated since its original publication to reflect a more recent version of the software interface.]
The costs and liabilities that companies incur in supporting their warranty policies could consume staggering percentages of their budgets. Therefore, warranty analysis is an important activity for a manufacturing company's financial planning. Continuous monitoring for emerging negative reliability issues is also extremely important. An ongoing comprehensive warranty analysis program helps in revealing the truth in warranty data and provides much useful and insightful information, such as warranty returns and related cost forecasts, optimum schedules of spares shipments and deviation between the actual and predicted warranty returns. In addition, a comprehensive warranty analysis program will look at warranty data from several different angles to help identify trends, warranty issues and possible corrective actions. These analyses should look at the big picture, or global view, and should also look at the customer’s perspective during the warranty period.
Some warranty analyses that can be performed on non-repairable systems (or at the component level if a failed component is replaced by a new one or repaired to perfect "as good as new" condition) involve using distribution analysis methods (commonly known as Weibull analysis). For repairable systems, however, distribution analysis is not valid as systems are typically not put back into a like new condition after repair. In general, the intervals between failures of a complex system do not follow the same distribution. Therefore, for systems that are repaired and not replaced when they fail during warranty, the analysis methods provided in ReliaSoft’s RGA software are generally appropriate. Some of the RGA methods, e.g., the Global Fleet Analysis, will also apply to systems that are replaced when they fail.
This article presents different types of warranty analysis for repairable systems that address different sets of warranty data and related questions. The total package of analyses gives an ongoing assessment of the reliability during the warranty period from several viewpoints. Warranty analysis can help in forecasting future return numbers and costs of fulfilling warranty claims, observing MTBF change over time and predicting changes in the future, verifying constancy in warranty performance and checking for wearout or infant mortality problems (quality problems) during warranty.
The Global Fleet Analysis uses the fleet warranty failures and total fleet operating hours over successive periods of calendar time to:
Sample Data and RGA Instructions
The data set for this type of analysis is to be grouped by a certain increment (e.g., months, quarters), which shows the accumulated hours (or other time unit such as mileage) of all the systems in the field and the number of fleet failures. This type of analysis uses the following data type and model in RGA:
In Table 1, the warranty data are grouped according to quarters. In each quarter we note the total number of fleet operating hours for all systems that are under warranty and the number of failures during the quarter. The results from this analysis were discussed in a recent Reliability HotWire article, see http://www.weibull.com/hotwire/issue64/hottopics64.htm.
Table 1: Global Fleet Data
Global Fleet Analysis with
Sample Data and RGA Instructions
As mentioned above, the data set is to be grouped by a certain increment (e.g., months, quarters), which shows the accumulated hours of all the systems in the field and the number of fleet failures. Failure modes are named and classified depending on the repair action type. This type of analysis uses the following data type and model in RGA:
In Table 2, the warranty data are grouped by months. In each month we note the total number of fleet operating hours for all systems that are under warranty, the number of failures during the month and the type of repair action received by each failure mode.
Table 2: Global Fleet with Failure Modes Data
Figure 1 shows the estimated effectiveness factors for the failure modes with delayed fixes (i.e., the BD modes). The effectiveness factor estimates the percent decrease in the item’s failure rate after the corrective actions are applied. The figure also shows a chart with the demonstrated MTBF of the population at the end of the 6th month, the Projected MTBF (after including the delayed repairs) and the Growth Potential MTBF (i.e., the maximum MTBF that can be attained with the current management strategy when all BD modes have been observed and fixed with an effectiveness equal to the average of the effectiveness factors that have been estimated for the observed BD modes).
Figure 1: Effectiveness Factors for the BD Failure Modes and Some Results from the Global Fleet Analysis
Within Warranty Cycle
This type of analysis uses the following data type and model in RGA:
Table 3 shows the failure times for each unit in a sample of 11 fleet systems; the end time is the last recorded known age when the analysis was performed. Figure 2 shows the probability (with 90% 2-sided confidence bounds) that a system that accumulated 2,000 hours of operation will operate for another 200 hours.
Table 3: Within Warranty Data
Figure 2: RGA QCP with Conditional Reliability
Across Warranty Cycle
Analysis (Fleet Analysis)
After entering the failure data, this analysis requires converting the timeline into grouped data. To accomplish this, a group interval is required. The group interval length should be chosen so that it is representative of the data and is sufficiently large to ensure that there are failures within each interval (this can also be determined automatically by RGA). Also note that the intervals do not have to be of equal length. This type of analysis uses the following data type and model in RGA:
Table 4 is a data sample for an automotive product that has a 10,000 mile warranty policy. The systems are sorted from the oldest configuration to the most recent one. The data set shows the entire failure record of each system during the warranty period. The group interval is defined to be an increment of 1,000 miles.
Table 4: Across Warranty Data
First, we verify the assumption that β is close to 1. As shown in Figure 3, using the RGA QCP, we find that we can say with 90% confidence that the interval on β includes 1. Therefore, the assumption is valid. Figure 3 also shows the estimated MTBF at 12,000 miles for this analysis.
Figure 3: Parameter Bounds and MTBF calculated by RGA for the Across Warranty Cycle Analysis
Across Warranty Cycle
Analysis (Fleet Analysis) With Failure Modes
Since this analysis studies the effect of future reliability improvements on the fleet, the only logical failure mode classifications are A (no action) and BD (delayed fixes); no BC failure mode classification should be used for this analysis since it represents failure modes that receive improvements during the observed operating period. Similarly to the previous analysis type, this analysis requires grouping the data by a certain interval increment. This type of analysis uses the following data type and model in RGA:
The data set in Table 5 was gathered from the failure logs of 27 military systems during their warranty period (5,000 miles). The manufacturer tracks the failures by mileage of occurrence and records the failure cause based on failure analysis performed when the manufacturer is requested to repair failures and decide whether the failure mode needs to be addressed in a future planned improvement (BD modes) or can be ignored (A modes).
Table 5: Across Warranty with Failure Modes Data
As shown in Figure 4, we first verify the assumption that β is close to 1; it is. Figure 4 also shows the accumulated number of failures per system at 5,500 miles.
Figure 4: Parameter Bounds and Number of Failures calculated for the Across Warranty with Modes Analysis
Note: If β < 1, this could be a sign that the environment in which the systems are operated is changing over time (e.g., less harsh use of the systems). In other words, even if no corrective actions were implemented, it is possible to see “improvement” in the systems. This, however, should not be interpreted as actual growth or configuration improvement. Rather, the failure times may be extended because the use conditions are becoming less stressful. If there are actual engineering changes that caused β < 1, the use of the Crow Extended model's BC modes classifications may be justified and the model may be used to evaluate the reliability improvement. If β > 1, the use of the Crow Extended model is not recommended. This is a negative trend, which means that the average warranty reliability is getting worse with each configuration build. In such cases, this issue should be corrected and future system configurations should be stabilized (β close to 1) before direct projection analysis may be applied using the Crow Extended model.