Malebo Suzan Mulaudzi , Natanya Meyer, Ziska Fields
Abstract
This systematic literature review examines simulation-optimisation strategies for SMEs in business management under uncertainty, addressing a critical research gap where SMEs face the most significant exposure to uncertainty while possessing the most limited analytical resources to manage these challenges effectively. Using the PRISMA methodology, 12 peer-reviewed articles published between 2001-2024 were systematically analysed from the Scopus database to investigate the effectiveness, implementation requirements, and performance improvement mechanisms of simulation-optimisation approaches in resource-constrained SME environments. The theoretical framework integrates the resource-based view, the dynamic capabilities theory, and the SME management theory to understand how analytical capabilities contribute to organisational performance under uncertainty. The systematic identification of only 12 relevant studies from comprehensive database searching reveals that simulation-optimisation research for SMEs represents a significantly underdeveloped field with substantial research gaps, rather than a limitation of our methodology. The reviewed studies demonstrate growing scholarly interest since 2019, geographic concentration in developed economies, and methodological diversity across applications, while highlighting critical needs for empirical validation of theoretical propositions and expanded research in developing economy contexts. Thematically, current research emphasises operational applications in manufacturing contexts, focusing on collaboration, project management, and entrepreneurial decision-making under uncertainty. Key managerial implications include adopting phased implementation approaches beginning with single-process optimisation, ensuring strong integration with existing systems, and leveraging external facilitation during initial phases. This review provides immediate practical value through evidence-based implementation guidelines while establishing the theoretical foundation for future empirical validation studies.