Journal of Management Analytics

Special Issue on AI for Scientific Management: Advancing Decision Science through Artificial Intelligence

Scientific Management has evolved from the early principles of Taylorism—focusing on empirical observation and workflow optimization—into a sophisticated domain of Decision Science and Operations Research. Today, the rapid advancement of Artificial Intelligence (AI), particularly Large Language Models (LLMs), Deep Reinforcement Learning, and Graph Analytics, offers a transformative opportunity to revisit the foundations of Scientific Management through the lens of modern Management Analytics.

The Journal of Management Analytics (JMA) focuses on the theory of data analytics and its applications in business and management, including accounting, finance, management, marketing, production/operations management, supply chain management, and healthcare management. This special issue seeks to explore the interface between advanced artificial intelligence and these core business functions. While traditional analytics often focus on descriptive or predictive insights, "AI for Scientific Management" emphasizes prescriptive intelligence—using AI to autonomously optimize organizational systems and bridge the gap between AI theory and management practice. We invite high-quality research that develops new analytical frameworks, provides empirical evidence of AI effectiveness, or introduces innovative simulation models that push the boundaries of how businesses are scientifically managed in the age of big data.

Topics of Interest

We welcome submissions that investigate the interface between AI-driven data analytics and various business disciplines. Areas of interest include, but are not limited to:

• AI in Production and Operations Management: Self-optimizing manufacturing systems, AI-driven predictive maintenance, and real-time workforce scheduling.
• AI in Supply Chain Management: Resilient supply chain design using deep learning, autonomous logistics optimization, and blockchain-integrated AI for transparency.
• AI in Finance and Accounting: Intelligent auditing systems, AI-based fraud detection, and the use of machine learning for high-frequency financial decision-making.
• AI in Marketing Analytics: Hyper-personalization via generative AI, customer lifetime value prediction using advanced neural networks, and sentiment-driven market analysis.
• Autonomous Agents and Multi-Agent Systems (MAS): Leveraging autonomous AI agents for distributed decision-making, multi-agent coordination in complex logistics, and agent-based modeling for organizational simulation and strategy testing.
• Methodological Innovations: Integration of Reinforcement Learning with Operations Research, Causal Inference in AI for management, and Explainable AI (XAI) for managerial trust.
• Human-AI Interaction in Management: The impact of algorithmic management on employee productivity, decision bias in AI-assisted management, and organizational governance of AI systems.