An Interactive Multi-Agent System for Evaluation of New Product Concepts
This addresses product development decisions for enterprises by automating evaluation to reduce subjective bias and costs, though it appears incremental as an application of existing methods.
The study tackled product concept evaluation by proposing an LLM-based multi-agent system with eight virtual agents using RAG and real-time search, and a case study on display monitors showed its evaluation rankings were consistent with senior industry experts.
Product concept evaluation is a critical stage that determines strategic resource allocation and project success in enterprises. However, traditional expert-led approaches face limitations such as subjective bias and high time and cost requirements. To support this process, this study proposes an automated approach utilizing a large language model (LLM)-based multi-agent system (MAS). Through a systematic analysis of previous research on product development and team collaboration, this study established two primary evaluation dimensions, namely technical feasibility and market feasibility. The proposed system consists of a team of eight virtual agents representing specialized domains such as R&D and marketing. These agents use retrieval-augmented generation (RAG) and real-time search tools to gather objective evidence and validate concepts through structured deliberations based on the established criteria. The agents were further fine-tuned using professional product review data to enhance their judgment accuracy. A case study involving professional display monitor concepts demonstrated that the system's evaluation rankings were consistent with those of senior industry experts. These results confirm the usability of the proposed multi-agent-based evaluation approach for supporting product development decisions.