Automated Negotiation is the use of computational agents to conduct bargaining or agreement-reaching processes on behalf of parties with potentially competing objectives. Grounded in game theory, multi-agent systems, and reinforcement learning, automated negotiation systems generate, evaluate, and exchange proposals to reach mutually acceptable outcomes across domains such as supply chain coordination, resource allocation, and autonomous trading. Research challenges include strategy design under incomplete information, multi-issue negotiation, and alignment with human preferences.
Procurement teams lose time and money to inaccurate demand forecasts and manual supplier negotiations. A new framework from NEC Corporation and NEC Laboratories America combines automated negotiation with multimodal AI forecasting to optimize both sides of the procurement process.
Procurement is a key function in supply chain management that involves acquiring goods and services to meet organizational needs. Efficient procurement is crucial for minimizing costs, ensuring timely delivery, and maintaining quality standards. This paper explores the integration of automated negotiation and multimodal time-series forecasting to enhance procurement processes. Automated negotiation can streamline interactions with suppliers, while multimodal time-series forecasting can improve demand prediction accuracy by leveraging diverse data sources leading to better negotiation outputs. By combining these approaches, organizations can optimize procurement strategies, reduce costs, and improve overall supply chain efficiency. We present two case studies using simulations based on real-world data for procurement that show the effectiveness of the proposed framework.