Supply Intelligence

We examine supply chain scenarios in which obtaining complete information about suppliers is costly. In such scenarios, there is a trade-off between the costs of obtaining information and the benefits that accrue to the owners of such information. Moreover, there are alternate mechanisms available for extracting such information. In order to study the relative merits of the different ways information can be acquired, we construct a general model which builds on the models of Dasgupta and Spulber (1985) and Chen (2001), who use auctions to extract information. Our model is considerably more general in terms of its assumptions and information requirements. In particular, the model can handle general cost and revenue functions, can allow for multiple variables, and is not dependent on functional properties such as continuity or convexity. The model is also very sparse in terms of the information that is available to the different parties a priori. We show that despite the restricted assumptions, the buyer can still guarantee significant profit levels for herself, while at the same time induce the channel to perform efficiently, i.e., at its (centralized) optimum. We also show how the buyer can use audits, performed after the transaction is implemented, in order to increase her share of the channel profits, while at the same time maintaining the incentives for channel coordination. We show that the well known buyback contract for the newsvendor problem can be viewed as a special case of audit based contracts. Finally, we examine the behavior of audit-based auctions when the audits are biased. This allows us to examine situation in which effort can reduce costs, but where the cost of effort cannot be documented. Naturally, the results apply equally well in scenarios where the roles of the supplier and buyer are reversed. (Original version March 14, revised May 19 2003) 1 We gratefully acknowledge the comments of the participants of the seminar at the Stern School of Business and Fangruo Chen of Columbia University. 2 Sridhar Seshadri’s research is partially supported by grants under DMI-0200406.