Modern Supply Chain Research and Applications (MSCRA)

Modern Supply Chain Research and Applications (MSCRA) is a premier peer-reviewed journal in operations research, management, analytics, and general quantitative modeling, with special interest in applications covering materials management, logistics, supply chain management and strategic sourcing. It is the high-quality journal of Federation of Management Societies of China (FMS), and the official journal of International Federation of Purchasing and Supply Management (IFPSM). MSCRA is published in association with China Fortune Press. Modern Supply Chain Research and Applications (MSCRA) is a premier peer-reviewed journal in operations research, management, analytics, and general quantitative modelling, with a special interest in applications covering materials management, logistics, supply chain management and strategic sourcing. The journal considers papers with an emphasis on statistics and research review, analytics research, modelling, optimization, and case-based research. MSCRA is also a broad-based journal devoted to operations and analysis of problems in business, industry and government. Submissions that are most appropriate for MSCRA are papers addressing modelling and analysis of problems motivated by real-world applications; major methodological advances in operations research/management, analytics, and applied probability; and expository or survey pieces of lasting value. Areas represented include, but are not limited to: probability, statistics, simulation, optimization, data analytics, machine learning, statistical learning, game theory, with applications in supply chain, logistics, market design, revenue management, algorithmic economy, intelligent manufacturing, healthcare, scheduling, risk management, quality, reliability, maintenance, and decision analytics.

Modern Supply Chain Research and Applications (MSCRA) is :-

• International, Peer-Reviewed, Open Access, Refereed, Supply Chain Management, Logistics, Operations Research, Data Analytics, Optimization, Machine Learning, Risk Management, Industrial Engineering , Online , Quarterly Journal

• UGC Approved, ISSN Approved: P-ISSN E-ISSN: 2631-3871, Established: 2019,
• Provides Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE