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How to read this page. The written overview is an AI-generated educational summary. Papers, references, costs and companies are verify-yourself links — we do not fabricate citations, prices or company lists.
PART 1Executive Overview
1Definition

Autonomous procurement involves the use of artificial intelligence (AI) to automate the negotiation process between buyers and sellers in a supply chain. This is achieved through AI agents, or 'bots', that can interact with each other autonomously to negotiate terms such as price, delivery time, and quality.

Category
Software
Best use
Supply Chain
Stage
NEAR
2Problem It Solves

Manual procurement processes are time-consuming, error-prone, and often result in suboptimal deals due to human biases or lack of information. Autonomous procurement aims to streamline this process by automating the negotiation phase, leading to faster, more efficient, and potentially better deals for both parties.

3Lifecycle / Journey Stage
near
PART 2Technical & Manufacturing
4How It Works

The system uses multi-agent reinforcement learning, where multiple AI agents learn from interactions with each other and their environment to find the best negotiation strategies. These agents represent both buyers and sellers in a simulated marketplace, continuously refining their tactics based on outcomes of past negotiations.

5Materials Used
6Manufacturing / Creation Process

The manufacturing aspect is primarily software development, involving the creation of AI algorithms, training datasets, and integration with existing supply chain management systems. No physical hardware is typically involved in the core technology.

7Build Process

Developing autonomous procurement systems involves creating a robust multi-agent system capable of learning through interactions. This includes defining negotiation protocols, implementing reinforcement learning algorithms, and integrating these into real-world supply chain scenarios for testing and validation.

8Energy Requirements

Field units draw low hundreds of watts; fabrication is energy-intensive due to vacuum baking. Overall operational power consumption is moderate but can be optimized through efficient algorithm design.

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PART 3Market & Industry
9Companies Involved
UiPathCelonis

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10Estimated Costs

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11Case Studies

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PART 4Academic References
12Scientific Papers / White Papers

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13Patents

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14Glossary
Multi-agent reinforcement learning
A type of machine learning where multiple agents learn to interact with each other and their environment to optimize a given objective, such as negotiation outcomes.
Supply chain management
The process of overseeing the movement of goods from suppliers to end customers. It involves coordinating various activities including procurement, production, inventory control, and distribution.
15References

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Related Technologies

Source: curated technology intelligence stream with tracked references.