Operations Modelling and Analytics is dedicated to advancing the science and practice of operations research, data analytics, and computational modeling. The journal targets researchers, academics, and professionals in operations management, industrial engineering, data science, and related disciplines.

The journal publishes original research that develops innovative modeling techniques, analytical methodologies, and computational tools to address complex operational challenges. It welcomes contributions that push the boundaries of theoretical foundations, propose novel quantitative approaches, or demonstrate practical applications of operations modeling and analytics in diverse industries.

Key areas of interest include, but are not limited to:

• Development and application of mathematical modeling, optimization, and simulation techniques for operational decision-making.
• Advanced data analytics, machine learning, and artificial intelligence methods for process improvement and operational efficiency.
• Applications of operations modeling in supply chain management, logistics, manufacturing, healthcare, finance, and service industries.
• Integration of big data and predictive analytics in operational systems.
• Novel approaches to uncertainty modeling, risk analysis, and decision support systems.

The journal also encourages submissions that assess the state-of-the-art in operations modeling and analytics, including reviews of emerging trends, tools, and methodologies. Papers reporting real-world applications are highly valued, provided they demonstrate originality in problem formulation, methodological approach, or solution implementation, and offer generalizable insights applicable to broader contexts.

A core mission of Operations Modelling and Analytics is to foster global collaboration and knowledge exchange among scholars, practitioners, and industry leaders, promoting innovative solutions to operational challenges worldwide.