A Systematic Review of Human‐Aware Systems in Manufacturing and Logistics: Overcoming Obstacles With Advanced Modeling Techniques

This paper presents a systematic literature review of human‐aware systems in manufacturing and logistics within the framework of Industry 4.0 and the transition toward Industry 5.0. Following PRISMA guidelines, we analyzed 64 studies published between 2014 and 2024, identified through Scopus, Web of Science, IEEE Xplore, SpringerLink, and other indexed databases. The review shows that artificial neural networks (ANN) and federated learning (FL) dominate current applications, while promising techniques such as natural language processing (NLP), Markov decision processes (MDP), and Tabu search (TS) remain underexplored. Existing research is concentrated on optimization and modeling, with major gaps in empirical validation, ergonomics, inclusivity, and human‐in‐the‐loop design. The contributions of this study are threefold: (1) it provides the first systematic mapping of human‐aware systems across manufacturing and logistics; (2) it identifies methodological and thematic gaps in the literature; and (3) it proposes a future research agenda that emphasizes human‐centric approaches that are consistent with Industry 5.0. Thus, this study offers both a comprehensive academic synthesis and a practical roadmap for advancing human‐aware systems.