Optimal Price Design for Variable Capacity Outsourcing Contracts

Outsourcing of information technology (IT) infrastructure and business processes (BP) is a significant aspect of the business landscape. Recently attention has moved to variable capacity contracts linked to business or IT metrics for medium to long term (2-10 year) deals. This new emphasis on variability is accompanied by a growth in interest in the accompanying pricing schemes. A basic tension in the design of these pricing schemes occurs between the objectives of the outsourcing provider (hereafter Provider) and the company whose IT or BP are being outsourced (hereafter Client). In other words their utility functions on contract attributes differ. For example the Client may desire utility-style pricing whilst the Provider may be interested in the certainty with which it achieves a given margin. In this paper we characterize and solve the price design problem [PDP] for variable capacity IT/BP outsourcing contracts within a descriptive multi-attribute utility theory framework from the point of view of the bid team, i.e. they know costs and must negotiate prices. In these situations costs are usually granular, non-linear, and history dependent. We show that the price design problem, i.e. the general problem of choosing a price structure to optimize the joint utility function of the Provider and of the Client for arbitrary cost functions, can be formulated exactly as a stochastic program. The formulation imposes no restrictions on the form of the price functions a priori. We introduce a method based on decomposition of the event space (where events are, e.g. Client requirement histories) using a basis of linear functionals. The resulting, linear and quadratic, integer, formulations can be solved numerically using standard software. The solution yields Pareto-efficient outcomes with respect to the Provider and the Client. The frontier of Pareto-optimal designs serves as an appropriate space for practical contract negotiation.

By: Chris Kenyon

Published in: Journal of Revenue & Pricing Management, volume 4, (no 2), pages 124-155 in 2005

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