How Mechanism Design Theory Optimizes Amazon and Vendor Collaboration

Amazon and vendors each optimize inventory, production, and transportation costs separately. This tends to produce plans that are inefficient from an overall perspective, and the key to solving this is mechanism design premised on information asymmetry.

The representative solution, VCG (Vickrey-Clarke-Groves), guarantees both social efficiency and truthful incentives, but it is difficult to apply directly to problems with as many variables and constraints as large-scale supply chains. Amazon combined it with CPP (consensus planning protocol) to create Flo Pro, and confirmed actual cost savings in a 9-week pilot with a major consumer goods manufacturer.

CPP is a distributed optimization based on ADMM. A central coordinator proposes prices and a consensus plan, and each participant responds by computing only the optimal solution for their own region; this process is repeated to find a socially optimal plan. Since vendors don't need to disclose their entire cost structure, only the necessary information is exchanged.

• Run CPP once to obtain the jointly optimal plan.
• Run it again with one participant excluded to compute VCG's counterfactual cost.
• Settle the difference via CBT (cost-benefit transfer), creating a structure where vendors pay Amazon.

This structure is a rolling-horizon design that originates from the dynamic pivot mechanism described in 2010 and 2019. Each week, a plan looking 6 weeks ahead is established, and settlement reflects both the immediate cost incurred from deviating from the current week's JIT plan and the future certainty-equivalent cost. This one-directional transfer structure is simple and robust, but a two-directional transfer design in which Amazon compensates vendors remains an open challenge.

For low-dimensional decision spaces, a menu-of-contracts approach is also proposed. Amazon presents a bundle of candidate plans and prices, and the vendor simply chooses the option with the highest utility. In numerical examples, this approach also exactly recovered the socially optimal solution.

This framework can be extended to vendor negotiations, Fulfillment-by-Amazon (FBA) seller collaboration, and multi-party logistics planning. Remaining challenges include how to handle cases where committed volumes cannot be met due to supply shortages, and how to design mutual commitment structures under uncertain forecasting environments.