Template:Warranty prediction - Revision history

Kate Racaza: Redirected page to Warranty Data Analysis

2012-06-29T07:46:11Z

Redirected page to Warranty Data Analysis

← Older revision		Revision as of 07:46, 29 June 2012
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	~~===Warranty Prediction===~~		#REDIRECT [[Warranty Data Analysis]]
	Once a life data analysis has been performed on warranty data, this information can be used to predict how many warranty returns there will be in subsequent time periods. This methodology uses the concept of conditional reliability (see Chapter [[~~Basic Statistical Background~~]]) to calculate the probability of failure for the remaining units for each shipment time period. This conditional probability of failure is then multiplied by the number of units at risk from that particular shipment period that are still in the field (i.e. the suspensions) in order to predict the number of failures or warranty returns expected for this time period. The next example illustrates this.


	~~'''Example 3:'''~~
	~~{{Example: Warranty Analysis Prediction Example}}~~

Harry Guo: /* Warranty Prediction */

2012-02-21T23:03:17Z

Warranty Prediction

← Older revision		Revision as of 23:03, 21 February 2012
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	===Warranty Prediction===		===Warranty Prediction===
	Once a life data analysis has been performed on warranty data, this information can be used to predict how many warranty returns there will be in subsequent time periods. This methodology uses the concept of conditional reliability (see Chapter 3) to calculate the probability of failure for the remaining units for each shipment time period. This conditional probability of failure is then multiplied by the number of units at risk from that particular shipment period that are still in the field (i.e. the suspensions) in order to predict the number of failures or warranty returns expected for this time period. The next example illustrates this.		Once a life data analysis has been performed on warranty data, this information can be used to predict how many warranty returns there will be in subsequent time periods. This methodology uses the concept of conditional reliability (see Chapter [[Basic Statistical Background]]) to calculate the probability of failure for the remaining units for each shipment time period. This conditional probability of failure is then multiplied by the number of units at risk from that particular shipment period that are still in the field (i.e. the suspensions) in order to predict the number of failures or warranty returns expected for this time period. The next example illustrates this.


	'''Example 3:'''		'''Example 3:'''
	{{Example: Warranty Analysis Prediction Example}}		{{Example: Warranty Analysis Prediction Example}}

Harry Guo: /* Warranty Prediction */

2012-02-21T20:50:05Z

Warranty Prediction

@@ Line 2: / Line 2: @@
 Once a life data analysis has been performed on warranty data, this information can be used to predict how many warranty returns there will be in subsequent time periods. This methodology uses the concept of conditional reliability (see Chapter 3) to calculate the probability of failure for the remaining units for each shipment time period. This conditional probability of failure is then multiplied by the number of units at risk from that particular shipment period that are still in the field (i.e. the suspensions) in order to predict the number of failures or warranty returns expected for this time period. The next example illustrates this.
-=====Solution to Example 4=====
+'''Example 3:'''
-We use the Weibull parameter estimates found in Example 2 to determine the conditional probability of failure for each shipment time period, and then multiply that probability with the number of units that are at risk for that period as follows. The equation for the conditional probability of failure is given by:
+{{Example: Warranty Analysis Prediction Example}}

Nicolette Young: Created page with '===Warranty Prediction=== Once a life data analysis has been performed on warranty data, this information can be used to predict how many warranty returns there will be in subseq…'

2012-01-04T23:43:46Z

Created page with '===Warranty Prediction=== Once a life data analysis has been performed on warranty data, this information can be used to predict how many warranty returns there will be in subseq…'

New page

===Warranty Prediction===
Once a life data analysis has been performed on warranty data, this information can be used to predict how many warranty returns there will be in subsequent time periods. This methodology uses the concept of conditional reliability (see Chapter 3) to calculate the probability of failure for the remaining units for each shipment time period. This conditional probability of failure is then multiplied by the number of units at risk from that particular shipment period that are still in the field (i.e. the suspensions) in order to predict the number of failures or warranty returns expected for this time period. The next example illustrates this.

====Example 4====
Using the data from Example 2, predict the number of warranty returns for October for each of the three shipment periods.

=====Solution to Example 4=====
We use the Weibull parameter estimates found in Example 2 to determine the conditional probability of failure for each shipment time period, and then multiply that probability with the number of units that are at risk for that period as follows. The equation for the conditional probability of failure is given by:

::<math>Q(t|T)=1-R(t|T)=1-\frac{R(T+t)}{R(T)}</math>

For the June shipment, there are 89 units that have successfully operated until the end of September ( <math>T=3</math> <math>months)</math> . The probability of one of these units failing in the next month ( <math>t=1</math> <math>month)</math> is then given by:

::<math>Q(1|3)=1-\frac{R(4)}{R(3)}=1-\frac{{{e}^{-{{\left( \tfrac{4}{6.70} \right)}^{2.49}}}}}{{{e}^{-{{\left( \tfrac{3}{6.70} \right)}^{2.49}}}}}=1-\frac{0.7582}{0.8735}=0.132</math>

Once the probability of failure for an additional month of operation is determined, the expected number of failed units during the next month, from the June shipment, is the product of this probability and the number of units at risk ( <math>{{S}_{JUN,3}}=89)</math> or:

::<math>{{\widehat{F}}_{JUN,4}}=89\cdot 0.132=11.748\text{, or 12 units}</math>

This is then repeated for the July shipment, where there were 134 units operating at the end of September, with an exposure time of two months. The probability of failure in the next month is:

::<math>Q(1|2)=1-\frac{R(3)}{R(2)}=1-\frac{0.8735}{0.9519}=0.0824</math>

This value is multiplied by <math>{{S}_{JUL,2}}=134</math> to determine the number of failures, or:

::<math>{{\widehat{F}}_{JUL,3}}=134\cdot 0.0824=11.035\text{, or 11 units}</math>

For the August shipment, there were 146 units operating at the end of September, with an exposure time of one month. The probability of failure in the next month is:

::<math>Q(1|1)=1-\frac{R(2)}{R(1)}=1-\frac{0.9519}{0.9913}=0.0397</math>

This value is multiplied by <math>{{S}_{AUG,1}}=146</math> to determine the number of failures, or:

::<math>{{\widehat{F}}_{AUG,2}}=146\cdot 0.0397=5.796\text{, or 6 units}</math>

Thus, the total expected returns from all shipments for the next month is the sum of the above, or 29 units. This method can be easily repeated for different future sales periods, and utilizing projected shipments. If the user lists the number of units that are expected be sold or shipped during future periods, then these units are added to the number of units at risk whenever they are introduced into the field. The warranty analysis tool in Weibull++ performs this type of analysis for you.

Note that Weibull++ can perform this step automatically by using the `Generate Forecast' functionality.