In the Sultanate of Oman, the logistics sector is considered one of the most significant industries due to the country’s strategic location at the crossroads of major international trade corridors. This paper examines the impact of logistics and supply chain management (LSCM) on enhancing Oman’s trade connectivity, with particular reference to logistics infrastructure, IT integration, policies, and best practices. A quantitative research design is employed, incorporating statistical techniques such as correlation and regression analysis, alongside the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, to identify and evaluate the causal relationships between variables. The findings highlight the need to address infrastructure gaps, strengthen technology adoption, and foster multi-stakeholder engagement to fully realize Oman’s potential as a logistics hub. Accordingly, developing and implementing targeted measures are essential to leverage Oman’s geographical advantage, improve trade facilitation, enhance competitiveness, and support economic diversification. The results of this study provide valuable insights for policymakers and industry stakeholders, offering recommendations to advance the performance of Oman’s logistics sector while contributing to the achievement of sustainable development goals.

Digital transformation plays a significant role in enhancing the efficiency of Oman’s supply chain, particularly in the logistics and transportation sectors, by adopting advanced technologies. This mixed-methods study investigates how digital technologies improve supply chain efficiency in Oman by identifying key technologies, analyzing implementation barriers, assessing current systems, and evaluating the benefits of digital transformation. Data were collected through a questionnaire administered to 50 respondents and semi-structured interviews conducted with six participants. The quantitative findings indicate strong relationships between digital transformation, organizational agility, and competitive performance, with organizational agility serving as a mediating factor and environmental uncertainty acting as a moderating factor. The qualitative findings highlight the adoption of technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), which contribute to cost savings of 10–20% and lead-time reductions of up to 30%. Despite these benefits, the study identifies several challenges, including financial constraints and shortages of skilled labor, which may hinder the effective implementation of digital technologies. Overall, the study concludes that digital transformation is essential for improving Oman’s supply chain efficiency and strengthening global competitiveness. It recommends increased investment in digital technologies, workforce training, and supportive policy interventions to overcome implementation barriers and support the goals of Oman Vision 2040.

Adopting artificial intelligence (AI) into sustainable cold chain management (SCCM) implementation is an important strategy to control real-time temperature, maintain product quality, and increase the cold chain performance and effectiveness. However, most previous research offers individual insights and lack of comprehensive research to support SCCM management implementation with AI integration. Therefore, this study build a AI-driven SCCM framework and explore the interrelationship among attributes to illustrate the important impact in practicing SCCM. Nevertheless, AI-driven SCCM implementation is a complex process that can challenge firms in sustainable practices and operating smart procedures. Thus, this study adopts diffusion of innovation theory to explore the dynamic impact on AI implementation in the SCCM process and improve firms’ performance. To increase the accuracy and dependability of results, this study employs combination of various methods, including content analysis, entropy weight method, fuzzy delphi method, exploratory factor analysis, fuzzy synthetic evaluation and decision-making trial evaluation laboratory. The findings provides a systematic analysis to design suitable strategies and increase the SCCM performance by identifying the causal aspects, including autonomous intelligent agent implementation, resilient supply chain planning, low-carbon network competence, and smart cold chain operations. Besides, practical implication is suggested based on the top important criteria, including green packaging, reinforcement learning, inventory optimization, predictive control, distribution planning.

Smart sensors, IoT connectivity, and predictive Machine Learning based Digital Twins can transform manufacturing and healthcare systems by enabling real-time data collection, analysis, and intelligent decision-making. For that purpose, training these ML algorithms on synthetic data generated from Discrete Event Simulation (DES) models is a highly promising solution. This paper presents two case studies in manufacturing and healthcare systems, demonstrating the capability of this technique to develop powerful ML surrogate models that can generate efficient estimates in near real-time. Results indicate that advanced preprocessing techniques are essential, particularly when the data exhibit strong skewness or multimodal behavior. The findings highlight that applying common data cleaning and transformation techniques to DES-generated synthetic data, already cleaned during input modeling, may hide valid variations crucial for preserving the dataset's integrity. These results emphasize the need for further research in designing effective preprocessing pipelines that preserve the structure and variability of DES-generated synthetic data.