AI-Driven Inventory Digitization in Second-Hand Retail: Design and Evaluation of a Multi-Agent System

Abstract

Presently, the second-hand retail sector, which is one of the main pillars of the circular econ-omy, is going through a "scalability-sustainability paradox" situation. Simply put, the sector is caught in a paradox where the high labor costs of a manual process that aims at the digitali-zation of a heterogeneous inventory lead to low-value products being unprofitable for Small and Medium-sized Enterprises (SMEs). Micro-retailers are severely limited in terms of digital market access from this operational bottleneck. The master's thesis offers a solution to the problem by questioning how the multi-agent Artificial Intelligence (AI) architecture can be restructured to automate the digitization workflow, thus reducing transaction costs and facil-itating scalability in resource-constrained environments.

The research, which is based on Design Science Research (DSR) methodology, invents "AI-MAS-on-Serverless," an innovative artifact that employs event-driven multi-agent systems and multimodal Large Language Models (LLMs) on a serverless cloud infrastructure. The the-oretical framework combines the review of the three literatures identified, i.e. inventory digitization, multi-agent systems, and process automation, to not only develop a solution that is cost-effective and easy to access but also to tailor it to the second-hand sector's pecu-liar constraints.

The designed instrument was put to the test through a summative naturalistic evaluation in a Finnish second-hand retailer wherein an AI-assisted "Human-in-the-Loop" workflow was compared with the traditional manual practices across a sample of 50 diverse items. The outcomes indicate a statistically significant enhancement in the operational efficiency that is accompanied by an 88.4% reduction of the processing time (from 310s ±45s to 36s ±5s per item) and an 88% decrease in digitization costs (from €1.29 to €0.155). The research achieves a 94% accuracy level in the extraction of objective metadata and, therefore, becomes instru-mental in the provision of an automation design theory that is democratized and shows the manner in which accessible AI agents can put to an end the economic barriers to entry in the digital circular economy.