IT Tool Stack Optimization in Collaborative Projects: An Evaluation and Recommendation Framework

Abstract

In modern industrial engineering, configuring IT tool systems is fundamental to ensuring productivity and quality in collaborative projects. Small and medium-sized enterprises (SMEs) face distinctive challenges in selecting and adopting these systems—not only due to limited IT and AI expertise but also because of insufficient consideration of human perceptual factors such as technology acceptance and subjective practical experience. These challenges adversely affect overall quality, productivity, and technology adoption.This study proposes a user-centric framework for evaluating and recommending IT tools for system design. The framework employs standardized human-centric evaluation methods based on the Critical to Quality (CTQ) methodology. By integrating requirements engineering with machine learning (ML) models—including collaborative and content-based filtering techniques—the approach systematically analyzes data to identify similarities among users and project archetypes, thereby recommending the most effective tool configurations. Moreover, ML models are utilized to refine recommendations by matching individuals across projects and incorporating cognitive factors related to perceived tool usage efficiency. This systematic approach to IT tool stack configuration aligns with organizational objectives and project-specific requirements, ultimately enhancing collaborative capabilities, productivity, and technology adoption and acceptance rates in SMEs.

Keywords: IT Tool Stack, Collaborative Projects, Recommendation Systems, SMEs, Machine Learning Models, System Design

DOI: 10.54941/ahfe1006391