Comparisons of warranty costs and temporal periods are made amongst different warranty policies.
According to this table, offering warranty achieves statistically significant savings in holding, backorder, disassembly, disposal, remanufacturing, transportation, warranty and number of warranty claims.
According to Table 6, offering MBG warranty with PM during the warranty period for remanufactured MOs helps achieves statistically significant savings in holding, backorder, disassembly, disposal, remanufacturing, transportation, warranty, PM costs and number of warranty claims.
One of the key points in providing an appropriate policy of warranty is to estimate the optimized warranty coverage.
This study proposes a strategy that offers customers a two-dimensional warranty menu with a number of warranty choices, called a flexible warranty policy.
Murthy and Blischke (2005) studied the terms of warranty obligations of cost models and warranty models based on different platforms.
This results into a customer-rush near the warranty expiration limit leading to an occurrence of 'spikes' in warranty claims towards the end of warranty period and thereby introducing a bias into the dataset.
Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs.
This implies that the numbers of warranty claims of different products may be related, and optimisation of warranty policies for each individual product may therefore cause biased decisions.
Traditionally, efforts in this area have been limited to analysis of warranty data for evaluating warranty policies, estimating field reliability, predicting failures etc.
In earlier researches, different types of warranty policies have been offered to cover specific types.
