A value-at-risk approach to optimisation of warranty policy

In the real world, a manufacturer may produce many products, which may have common components installed. Consequently, the frequencies of the warranty claims of those products are statistically dependent. Warranty policy optimisation in the existing research, however, has not considered such statistical dependence, which may increase bias in decision making. This paper is the first attempt to collectively optimises warranty policy for a set of different products, produced by one manufacturer, whose failures are statistically dependent, using tools borrowed from financial mathematics (i.e., value-at-risk theory and copula). We prove the existence of the optimal solutions for different scenarios. Numerical examples are used to validate the applicability of the proposed methods

Warranty data analysis for quality improvement and economic benefits

This presentation discusses topics relating to warranty data analysis and warranty policy optimisation

Pricing service maintenance contracts using predictive analytics

As more manufacturers shift their focus from selling products to end solutions, full-service maintenance contracts gain traction in the business world. These contracts cover all maintenance related costs during a predetermined horizon in exchange for a fixed service fee and relieve customers from uncertain maintenance costs. To guarantee profitability, the service fees should at least cover the expected costs during the contract horizon. As these expected costs may depend on several machine-dependent characteristics, e.g. operational environment, the service fees should also be differentiated based on these characteristics. If not, customers that are less prone to high maintenance costs will not buy into or renege on the contract. The latter can lead to adverse selection and leave the service provider with a maintenance-heavy portfolio, which may be detrimental to the profitability of the service contracts. We contribute to the literature with a data-driven tariff plan based on the calibration of predictive models that take into account the different machine profiles. This conveys to the service provider which machine profiles should be attracted at which price. We demonstrate the advantage of a differentiated tariff plan and show how it better protects against adverse selection

Managing Risk for Auto Warranties

Purpose - This paper aims to (1) analyse the existing work of warranty risk management (WaRM); (2) develop a generic WaRM framework; and (3) design a generic taxonomy for warranty hazards from a warranty chain perspective. Design/methodology/approach – To understand the top warranty hazards, we designed a questionnaire, received 40 responses from the warranty decision makers (WDM) in the automotive industry in the UK and then analysed the responses. Findings – The assembly process capability at suppliers is the top contributor to warranty incidents from the suppliers’ and original equipment manufacturers (OEMs’) viewpoints. The human error at different stages of the product lifecycle contributes to the occurrence of warranty incidents. The collaboration among parties, particularly, the accessibility to warranty-related data between parties (i.e., suppliers, OEM and dealers), is limited. Customers’ fraud contributes more to warranty costs than warranty services providers’ (WSPs) fraud. The top contributors to customer dissatisfaction relating to warranty are the warranty service time and service quality. Research limitations/implications – The questionnaires were used to collect data in the UK, which implies the research outcomes of this paper may only reflect the UK area. Practical implications – The WaRM framework and taxonomy proposed in this paper provide warranty decision makers with a holistic view to identifying the top contributors to warranty incidents. With them, the decision makers will be able to allocate the required fund and efforts more effectively. Originality/value – This paper contributes to the literature by providing the first work of systematically analysing the top contributors to warranty incidents and costs and by providing a WaRM framework