# How Amazon Uses Game Theory to Optimize Its Supply Chain Without Sharing Private Data
*Amazon combines mechanism design and distributed optimization to help vendors coordinate supply-chain decisions without revealing private costs.*
By [Rohit Lakhotia](https://scaleengineer.com/authors/rohit-lakhotia)
Published: 2026-10-26
Canonical: https://scaleengineer.com/blog/how-amazon-uses-game-theory-to-optimize-its-supply-chain-without-sharing-private-data
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Imagine Amazon needs to restock products across several fulfillment centers\. Amazon knows where customer demand is expected to be highest\. Its vendor knows how much it costs to manufacture products, how much inventory it can produce, and which fulfillment centers are cheaper to ship to\. Both have information the other could use\.

If Amazon knew the vendor's shipping costs, it might distribute purchase orders more efficiently\. If the vendor knew Amazon's demand forecasts and transportation costs, it could make better decisions about fulfilling those orders\. But there's a catch\. Neither side necessarily wants to reveal its private cost structure or business information\.

Amazon might optimize its own orders without knowing the vendor's full costs\. The vendor might optimize its own production and shipping without knowing Amazon's downstream costs\. Both make reasonable decisions independently, yet the combined plan can end up costing more than necessary\.

This is a classic problem in economics called coordination under asymmetric information: two parties need to coordinate their decisions, but each has information the other cannot see\.

Amazon's [Supply Chain Optimization Technologies](https://www.amazon.science/tag/supply-chain-optimization-technologies) organization explored a way to address this problem with Flo Pro, a system that combines ideas from economic mechanism design with a distributed optimization framework\. In a nine\-week pilot with a prominent consumer\-product manufacturer, the approach demonstrated real cost savings\. The key idea was to help Amazon and the vendor work toward a jointly optimized supply plan without requiring the vendor to disclose its underlying cost structure\. Let’s discover how they did this in this blog today\.

## The Problem: Both Sides Optimize, but Not Together

Amazon's existing process uses a just\-in\-time \(JIT\) ordering policy\. Amazon decides when to place an order, where products should be delivered, and how many units it needs\. The vendor then decides how much of that order it can fulfill\.

The problem is that these decisions are connected, but each side makes them with incomplete information\. For example, a vendor might be able to ship products much more cheaply to one fulfillment center than another\. If Amazon doesn't know about that cost difference, its purchase orders might not take advantage of it\. At the same time, the vendor doesn't necessarily know Amazon's downstream demand patterns or the cost of transporting products from fulfillment centers to customers\.

Both sides are optimizing, but they're optimizing their own part of the supply chain rather than the total cost\. Ideally, Amazon and the vendor could combine their information to find the most cost\-effective plan across production, inbound transportation, fulfillment, and outbound transportation\.

However, asking both sides to reveal all their costs and constraints isn't a practical solution\. That information is commercially sensitive\. The challenge was to coordinate decisions without requiring either party to disclose all its private information\.

## The Economic Idea Behind Flo Pro

To understand how Flo Pro works, we need to look at a concept from economics called mechanism design\. Mechanism design is about designing rules for interactions between parties so that, even when everyone acts in their own interests, the resulting outcome can still be efficient\.

Think of it as designing the rules of the game rather than assuming everyone will voluntarily share information or cooperate perfectly\. One of the foundational approaches in this field is the [Vickrey\-Clarke\-Groves \(VCG\) mechanism](https://en.wikipedia.org/wiki/Vickrey%E2%80%93Clarke%E2%80%93Groves_mechanism)\. VCG is commonly associated with auctions, but its underlying idea applies to broader coordination problems\. It aims to select an outcome that maximizes total welfare while making truthful reporting the best strategy for each participant\.

In Flo Pro, the mechanism considers the preferences of Amazon and the vendor, finds a jointly optimal supply plan, and calculates a payment to account for the effect that the plan has on Amazon's costs\. This payment is called a cost\-benefit transfer \(CBT\)\. In the arrangement described in the paper, the vendor compensates Amazon for the additional cost Amazon incurs when it departs from its preferred JIT plan to accommodate the proposed joint plan\. The payment structure is designed so that the vendor has no incentive to misrepresent its costs to obtain a better outcome\.

But there is a practical problem with applying VCG directly\. The mechanism requires participants to submit their complete utility functions\. In simple terms, it needs a detailed representation of how each participant values all the possible outcomes\. For a supply chain involving dozens of fulfillment centers, multiple products, and changing weekly plans, providing all that information directly becomes impractical\. That's where the second part of Flo Pro comes in\.

## How CPP Makes the Optimization Practical

Amazon combines VCG with its [Consensus Planning Protocol \(CPP\)](https://arxiv.org/pdf/2408.16462), a distributed, agent\-based optimization framework\. Instead of requiring Amazon and the vendor to reveal their complete cost structures upfront, CPP allows them to work toward a common plan through repeated exchanges\. The protocol is based on the [alternating direction method of multipliers \(ADMM\)](https://www.stat.cmu.edu/~ryantibs/convexopt/lectures/admm.pdf), an optimization technique that helps separate a large problem into smaller problems that can be solved independently\.

Here's the basic process:

1. The coordinator proposes a plan\. It sends a proposed supply plan along with a set of prices\.
2. Each participant evaluates the proposal\. Amazon and the vendor each determine their preferred plan in response to those prices, using their own local optimization problem\.
3. The coordinator updates the proposal\. Based on the participants' responses, it adjusts the plan and prices\.
4. The process repeats\. These iterations continue until the system converges on a consensus plan\.

The important detail is that each participant can evaluate the proposal using its own information\. It doesn't have to send its complete cost structure to the other party\. The vendor communicates its preferences through its responses to the coordinator's queries, rather than disclosing every underlying cost and constraint\.

### How Does This Connect Back to VCG?

CPP provides the computational process for finding the jointly efficient plan\. VCG provides the incentive and payment structure\.

Flo Pro also performs a second CPP run with one participant removed\. This calculates the cost to the remaining participant under the relevant counterfactual, which helps determine the VCG transfer payment\. That combination is what makes the approach interesting: CPP handles the practical optimization, while VCG determines the incentives and transfer needed to support truthful participation\.

## Supply Chains Don't Stay Still

Even if Amazon and its vendor agree on an efficient plan today, the plan might not remain optimal next week\. Demand forecasts change\. Supply conditions shift\. Production and shipping decisions need to be revisited\.

Flo Pro therefore extends the mechanism to a rolling planning horizon\. Each week, Amazon and the vendor plan for the next six weeks\. The mechanism issues a purchase order for the current week, calculates what the resulting just\-in\-time policy would look like for future periods, and determines the associated cost\-benefit transfer\.

The transfer accounts for two things:

- The immediate cost of deviating from the current week's JIT plan\.
- The certainty\-equivalent cost of the effect that this deviation has on future periods\.

A certainty\-equivalent cost represents the amount Amazon would be willing to pay today to avoid an uncertain future cost\. This matters because a decision that looks cheaper this week could have consequences for later weeks\. The mechanism needs to account for those future effects instead of optimizing each week's order in isolation\.

The paper describes a one\-directional payment arrangement in which the vendor compensates Amazon when the proposed plan creates additional costs for Amazon\. Extending the design to two\-way transfers, where Amazon could also compensate the vendor, remains an open design challenge\.

## An Alternative: Let the Vendor Choose From a Menu

Not every coordination problem requires an iterative optimization process\. For situations where the decision space is smaller, the paper also presents a menu\-of\-contracts approach\.

Instead of repeatedly exchanging proposed plans and prices, Amazon generates a set of candidate supply plans and their associated prices\. Each price reflects the cost Amazon incurs under that plan\. The vendor then chooses the option that maximizes its own utility\. The mechanism is designed so that the vendor's preferred choice also corresponds to the socially efficient plan\. This approach has a practical advantage: the vendor sees concrete options and prices rather than participating in an iterative optimization process\.

The paper also describes how the menu can incorporate partial information\. For instance, if a vendor indicates a general preference for shipping more products to a particular region, Amazon can use that information to tailor the available options\.

In the numerical example presented in the paper, the menu approach recovers the first\-best outcome, with the vendor selecting the globally optimal plan from the options offered\.

## What Did Flo Pro Demonstrate?

Flo Pro was piloted over nine weeks with a prominent consumer\-product manufacturer, and the results showed real cost savings\.

The bigger takeaway is how it brings economic theory into a practical supply\-chain problem\. Instead of letting Amazon and its vendor optimize their decisions separately, Flo Pro helps them work toward a shared plan that reduces overall costs, without requiring either side to reveal its complete cost structure\.

There are still challenges to solve\. What happens when a vendor faces a supply shortage and cannot deliver the agreed quantity? And could mutual commitments help both sides make better decisions when demand forecasts are uncertain?

These are important next steps because finding an efficient plan is only part of the problem\. A useful coordination system must also account for the uncertainty and disruptions that come with running a real\-world supply chain\.

## The Bigger Picture

Flo Pro brings together two ideas that solve different parts of the same problem\.

Mechanism design determines the rules and incentives that encourage participants to report their preferences truthfully\. Distributed optimization provides a way for participants to work toward a joint plan without exchanging all their private information\. Together, CPP and VCG help Amazon and its vendors coordinate decisions that would otherwise be optimized separately\.

And supply\-chain planning is only one possible application\. The paper identifies vendor negotiations, collaboration with Fulfillment\-by\-Amazon sellers, and multiparty logistics planning as other areas where the same combination of interdependent decisions, private costs, and shared benefits could be useful\.

The broader lesson is that collaboration doesn't always require everyone to share everything\. Sometimes, the more important task is to design the right rules, incentives, and computational process so that independently acting parties can reach a better outcome together\.

## Key Takeaways

- The problem: Amazon and its vendors make interconnected supply\-chain decisions but have different private cost and demand information\.
- Mechanism design: VCG establishes incentives intended to make truthful reporting the best strategy for participants\.
- Distributed optimization: CPP lets participants iteratively work toward a joint plan using their own local information\.
- Privacy: The vendor communicates preferences through optimization responses rather than revealing its complete cost structure\.
- Rolling planning: Flo Pro plans over a six\-week horizon and updates the plan weekly to account for changing conditions\.
- Alternative approach: A menu of contracts lets vendors choose from candidate supply plans and associated prices\.
- Reported outcome: Flo Pro was piloted over nine weeks with a consumer\-product manufacturer, demonstrating cost savings\. The supplied source doesn't quantify those savings\.

Official blog from Amazon: [How mechanism design theory helps optimize Amazon\-vendor collaboration](https://www.amazon.science/blog/how-mechanism-design-theory-helps-optimize-amazon-vendor-collaboration)

By now, you must have had a clear idea of,** How Amazon Uses Game Theory to Optimize Its Supply Chain Without Sharing Private Data? **In a nutshell, Amazon's Flo Pro combines VCG mechanism design with its Consensus Planning Protocol to help Amazon and vendors optimize supply\-chain plans without revealing their full private cost structures\.

**Congratulations\! You've just advanced another step in your tech journey\. Keep progressing\!**