International Journal of Management Science and Engineering Management

In recent years, supply chain management is critical for industries due to its increasing complexity. The daily movement of goods and services through supply chains is vast and requires innovative technologies to track, monitor, and record the consumer data and supply chain end-to-end prediction data. The primary goal of utilizing technologies in the supply chain is to improve efficiency and optimization of production. There are numerous data starting from identifying consumer demands, raw materials, capital required, production quantity, and downtime. All these data can be made useful by extracting insights and valuable information using AI and Big data analytical technologies. While supply chains operate on tough profit margins, the businesses need to focus even on minor improvements as they could have a substantial impact on the bottom line business profit. Warehouse optimization and demand forecasting are two crucial segments of supply chains that need AI and big data to harness the process.

Artificial intelligence offers various advanced algorithms through machine learning and deep learning approaches enhancing the predictive analytics for demand and supply forecasting. To make warehousing effective, enterprises need to optimize the localized and centralized warehouses and inventories. Both these elements would have a direct impact on sales. The time-series forecasting models through predictive algorithms could improve the performance of warehousing and identify goods effectively to enable quick distributions from closest inventories. Predictive models use historical and transactional data to understand the patterns of risk and identify the opportunities for growth enabling the supply chain manufacturers to anticipate the future of goods and services. Hence a predictive solution serves to be an effective method to improve the supply chain values. A greater range of visibility is provided on the shipment lifecycle through predictive modeling to help business, logistics, and careers to improve monitoring and scheduling of shipments creating better opportunities for the future. Forecasting results from predictive modeling are proactive and help take better supply chain decisions. Moreover, transportation management systems are able to predict disruptions to help logistics manage their operations proactively and react according to uncertain scenarios. This special issue enumerates the importance of predictive modeling with AI and big data analytics to enhance supply chain and logistics management. We invite scholars to present more advances in predictive modeling and analysis adding value to this context.

Original research and review articles in this area are encouraged in the following topic areas, including but are not limited to:

• Role of predictive analysis on supply chain and logistics management
• Digital transformation and its impact on supply chain value
• Predictive analysis and modules of big data for logistics and freight distribution
• Influence of big data analytics to identify demand and supply chain movements
• Impact of predictive analysis of big data on demand forecasting
• The transition of logistics and supply chain to sustainable systems with predictive modeling
• Effective ways of AI implementations for supply chain predictive networks
• Genetic algorithms of ML and its influence on delivery systems
• Sourcing optimization and risk management with predictive modeling
• Predictive modeling systems for transport planning and inventory management in supply chains
• Responsiveness of supply chains to predictions of AI and big data analytics
• Supply chain predictive modeling and its influence on industrial economies

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