## 1 Introduction  
The digital transformation of manufacturing has progressed rapidly over the past decade, driven by the principles of Industry 4.0, which encompass automation, data exchange, and cyber-physical systems [1, 2]. While these advancements have enabled greater efficiency and traceability in large-scale enterprises, small and medium-sized enterprises (SMEs) often encounter structural, financial, and technical obstacles that impede the adoption of advanced digital tools. SMEs involved in small-batch, order-based production frequently operate with fragmented systems, manual data collection, and limited analytical capabilities, which restrict their ability to adapt flexibly to process variations and inefficiencies [3]. The emerging paradigm of Industry 5.0 introduces a complementary perspective, highlighting human-centric, sustainable, and resilient manufacturing systems [4, 5]. Instead of replacing humans with automation, Industry 5.0 aims to enhance human capabilities through digital tools that foster interpretability, collaboration, and adaptive decision-making. In this context, digital twins (DTs) have emerged as a key enabler, providing real-time representations of physical systems and establishing a foundation for simulation, optimization, and intelligent feedback through the sensors. Previous research has illustrated the potential of digital twins in high-volume manufacturing, particularly when integrated with artificial intelligence (AI) for predictive modeling and control. However, their application in SMEs remains restricted, primarily due to the complexity and cost of implementation, as well as the need for interpretable, human-aligned outputs [6]. This study is based on the principle that even lightweight, modular digital twin systems, if appropriately designed, can yield significant value in SME environments. The research was conducted as part of the AI and Robotics Estonia (AIRE) initiative, a national competence center at Tallinn University of Technology, that promotes the \"test before invest\" philosophy, allowing companies to experiment with digital solutions before full-scale deployment [7; 8]. The case presented in this paper focuses on a small to medium-sized enterprise (SME) in Estonia that specializes in the manufacture of custom sportswear. The project aimed to implement a real-time digital twin framework built on the Digital Manufacturing Support Application (DIMUSA), enhanced with cluster-based analytics and virtual simulation [9; 10]. The aim was to analyze workstation-level data, identify process bottlenecks, and assist production decisions in a format that could be easily interpreted by operators and managers. While digital twins have been widely applied in highly automated, high-volume environments, their practical use in SMEs with flexible, order-based production remains underexplored. Furthermore, this study utilizes a digital shadow, a one-way, real-time data display of production processes, rather than a full bidirectional digital twin, which is more suitable for SME conditions. This study aims to address this gap by implementing and evaluating an AI-supported digital shadow system for real-time decision-making in a custom sportswear SME. The primary objective is to identify and mitigate performance bottlenecks and to enhance responsiveness through simulation-based analysis and clustering of workstation-level performance data. The approach draws inspiration from Lean manufacturing principles and follows a DMAIC-style methodology (Define, Measure, Analyze, Improve, Control), enabling systematic analysis of production inefficiencies in a dynamic SME context [11; 12]. The paper begins by outlining the methodological framework, including the system architecture, data sources, and analytical tools used in the study. This is followed by a presentation of the implementation results and insights gained from the pilot case. The discussion then examines the implications of these findings for Industry 5.0 and digitalization in small and medium-sized enterprises.