Modelling optimal warranty price for lifetime policies taking into account the uncertainties in life measures

Abstract

Due to assurance of longer reliable service life and greater customer peace of mind, products with lifetime warranty are becoming more and more popular. Under such policies, both the manufacturer and the buyer are exposed to uncertainties and risks of warranty pricing and product performance since products lifetime are uncertain and are not defined well in these policies. Considering the uncertainties in the measure of lifetime (useful life), this paper extends the work of Rahman and Chattopadhyay (2010) to determine the optimal warranty price. Risk preference models are developed to find the optimal warranty price through the use of the manufacturer's utility function for profit and the buyer's utility function for repair costs. Sensitivity of the risk preferences models are analysed with numerical example with respect to the factors such as the buyer's and the manufacturer/dealer's risk preferences, buyer's anticipated and manufacturer's estimated product failure intensity, the buyer's loyalty to the original manufacturer/dealer in repairing failed product and the buyer's repair costs for unwarranted products. Analysis of the developed models reveals manufacturer's decisions on warranty price are directly related to useful life, failure intensity of the product, and manufacture's risk preference. On the other hand buyer's acceptance of a lifetime warranty offer depends on expected lifetime of the product, buyer's anticipated product failure intensity, anticipated repair costs and most importantly buyer's risk preference.

On the use of dynamic reliability for an accurate modelling of renewable power plants

“…In fact, they do not consider the performance deterioration occurring during the system lifetime and they do not account for the variability of the primary resource and its effects on the power plant availability. These properties affect the quality of the system from the design stage [7] and influence costs and performance predictions during the life cycle [8][9][10]. In renewable power plants, this is even more critical because the operating conditions are continuously influenced by the randomness of the renewable resource therefore the production plans and the maintenance strategies must be optimized in order to increase the continuity of service [11].…”

“…(8) and the actual energy produced by the power plant is described by Eqs. (9) and (10). To account for the effects of the stochastic model, the SHyFTA provides a mechanism of synchronization between the variables of the deterministic model and the stochastic events (the basic events) that determine the status of each component.…”