The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

Purpose: This research examines the intricate interplay between Business Intelligence (BI), Big Data Analytics (BDA), and Artificial Intelligence (AI) within the realm of Supply Chain Management (SCM). While the integration of these technologies has promised improved operational efficiency and decision-making capabilities, concerns about complexities and potential overreliance on technology persist. The study aims to provide insights into achieving a balance between data-driven insights and qualitative factors in SCM for sustained competitiveness. Design/methodology/approach: The research executed interviews with ten Arab Gulf-based consulting firms. These companies’ ability to successfully complete BI projects is well recognised. Findings: Through examining the interplay of human judgement and data-driven strategies, addressing integration challenges, and understanding the risks of excessive data reliance, the research enhances comprehension of the modern SCM landscape. It underscores BI’s foundational role, the necessity of balanced human input, and the significance of customer-centric strategies for lasting competitive advantage and relationships. Practical implications: The research provided information for organizations seeking to effectively navigate the complexities of integrating data-driven technologies in SCM. The research is a foundation for future studies to delve deeper into quantitative measurement methodologies and effective data security strategies in the SCM context. Originality: The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

The operational performance of container ports is crucial for efficient logistics and trade. However, there is limited understanding of how external integration through Customer and Supplier Integration (SCI-CI and SCI-SI) impacts port operational performance (POP), particularly in emerging markets like Oman. This study addresses this gap by examining the relationship between SCI-CI, SCI-SI, and POP, and explores the mediating role of supply chain management (SCM) practices in this context. Using the Resource-Based View (RBV) as the theoretical framework, the study employed a quantitative cross-sectional survey method. A total of 377 questionnaires were distributed to managers at Sohar and Salalah ports, with 331 usable responses obtained, representing an 88 percent response rate. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that SCI-CI and SCI-SI have significant direct and indirect positive effects on POP, and they directly influence SCM practices. SCM practices, in turn, significantly enhance POP. Notably, SCM practices partially mediate the relationship between SCI-CI and SCI-SI with POP. These findings underscore the strategic importance of external integration and SCM practices as internal resources for improving port performance. This research provides valuable insights for decision-makers and policymakers in optimizing port operations.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.