🤖 AI Summary
This study addresses the lack of a systematic literature review on data-driven prescriptive analytics (DPSA) in business decision-making. Through a rigorous analysis of 104 peer-reviewed publications, it proposes— for the first time—a unified multidimensional taxonomy of DPSA, encompassing ten application domains, five core methodological categories (mathematical optimization, machine learning, probabilistic modeling, domain knowledge integration, and simulation), their compositional patterns, and two generic automated workflow archetypes. The analysis reveals pronounced trends toward methodological synergy and domain-specific adaptation, and identifies four critical frontiers requiring advancement: enhanced interpretability, human-in-the-loop optimization, cross-domain transferability, and real-time dynamic decision support. By establishing the first structured knowledge graph for DPSA, this work provides a foundational theoretical framework and empirical grounding to advance both academic research and industrial implementation.
📝 Abstract
Prescriptive Analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a comprehensive survey of existing applications within PSA in terms of their use cases, methodologies, and possible future research directions. To ensure a manageable scope, we focus on PSA applications that develop data-driven, automatic workflows, i.e., Data-Driven PSA (DPSA). Following a systematic methodology, we identify and include 104 papers in our survey. As our key contributions, we derive a number of novel taxonomies of the field and use them to analyse the field's temporal development. In terms of use cases, we derive 10 application domains for DPSA, from Healthcare to Manufacturing, and subsumed problem types within each. In terms of individual method usage, we derive 5 method types and map them to a comprehensive taxonomy of method usage within DPSA applications, covering mathematical optimization, data mining and machine learning, probabilistic modelling, domain expertise, as well as simulations. As for combined method usage, we provide a statistical overview of how different method usage combinations are distributed and derive 2 generic workflow patterns along with subsumed workflow patterns, combining methods by either sequential or simultaneous relationships. Finally, we derive 4 possible research directions based on frequently recurring issues among surveyed papers, suggesting new frontiers in terms of methods, tools, and use cases.