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Pricing of Full‐Service Repair Contracts with Learning, Optimized Maintenance, and Information Asymmetry
Author(s) -
Huber Sebastian,
Spinler Stefan
Publication year - 2014
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12098
Subject(s) - information asymmetry , full service , service (business) , business , asymmetry , computer science , operations management , operations research , industrial organization , marketing , economics , finance , commerce , mathematics , physics , quantum mechanics
This article considers the optimal pricing of full‐service (FS) repair contracts by taking into account learning and maintenance efficiency effects, competition from service , and asymmetric information. We analyze on‐call service (OS) and FS contracts in a market where customers exhibit heterogeneous risk aversion. While the customers minimize their disutility over the equipment lifetime, the service provider maximizes expected profits arising from the portfolio of OS and FS contracts. We show that the optimal FS price depends inter alia on the customer's prior cost experience and on OS repair and maintenance costs. The optimal FS price is shown to increase as fewer OS customers are lost to competition, whereas improved repair learning enabled by FS reduces the optimal price. A numerical study based on data from a manufacturer of forklifts highlights the importance of learning in maintenance operations, which constitutes the key benefit of FS contracts; 81% of the customers select the FS option and are willing to pay an insurance premium of around 1.5% of total OS cost against volatility of repair costs.