AHFE Open AccessDesigning a Generative AI–Supported Modular Quotation Process for SMEs: A Design Science Research Approach
Abstract
Small and medium-sized enterprises (SMEs) remain structurally constrained by limited resources, insufficient digital maturity, and a strong dependence on tacit knowledge embedded in individual employees. These constraints become particularly visible in quotation and offer creation, which is often manual, weakly structured, and characterized by information gaps between customer requirements and internal assessment capabilities. This study explores how generative artificial intelligence (GenAI) can support the redesign of SME quotation processes through modularity principles to improve transparency, flexibility, and cognitive ergonomics during early requirements work. Following Design Science Research Methodology, the study applies the stages of problem identification, objective specification, and artifact development. The resulting artifact is a modular quotation framework operationalized via a dual-role GenAI assistant: (1) a customer-facing chatbot that elicits and structures requirements through natural-language dialogue and (2) an internal analytical assistant that decomposes requests into reusable modules and supports initial feasibility and estimation work under human oversight. The artifact was iteratively evaluated through structured self-testing and practitioner feedback from roles in software development and IT project management. Feedback indicates improved requirement clarity and more systematic decomposition of complex requests, while also highlighting limitations related to file processing, budget realism, and interaction guardrails. Overall, the paper contributes a replicable blueprint for AI-enabled modular quotation support in SMEs and derives design implications for human-AI collaboration in software and systems engineering contexts.
Keywords: SMEs, Generative AI, Quotation Process, Modularity, Design Science Research, Requirements Elicitation, human-AI Collaboration
DOI: 10.54941/ahfe1007688
Cite this paper
More from this volume
- Supporting Inspection of Structured Qualitative Team Task Analytic Data
- Integration of MBSE Elements and Automation with System Development Processes for Advanced Performance & Efficiency
- Analysis for an End-to-End MBSE Operational Architecture
- A method of increasing the electromagnetic immunity of the Spectrum Monitoring Sensor to UAV’s by using a shielded casing
- Annoyance Modeling in Cooperative Personnel Scheduling
- From Manual Patrols to Automated Detection: Leveraging Aerial Imagery, Computer Vision and Large Language Models for Wildfire Risk Mitigation
- Safety Predictive Model with Machine Learning and Its Application in DART Analysis in Utility
- Scalable Threat Detection in Customer Interactions Using LLMs and LLM-as-Judge Framework
- Human Performance Modeling in Virtual Factories: A Simulation-Driven Ergonomics Approach
- Optimization of Motion Capture Technology for a Human Digital Twin with Reduced Sensor Setups
- Application of 3D Neck Modeling in Ergonomic Product Design
- Simulating Force-Posture Co-evolution in Horizontal Pushing Task using a Digital Human Model