Procurement is the process by which organizations acquire goods, services, or contracts from external suppliers, encompassing activities such as vendor selection, price negotiation, contract management, and order fulfillment. In AI research, procurement is studied as a domain for applying optimization, machine learning, and automated negotiation to improve cost efficiency, supplier risk assessment, and decision support. Applications include demand forecasting, spend analysis, and intelligent sourcing systems for enterprise and public sector contexts.

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How AI Can Transform the Way Companies Buy What They Need

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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.

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Automated Negotiation and Multimodal Time-Series Forecasting for Efficient Procurement

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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.

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