Logistics

Background: The vehicle routing problem (VRP) is of great importance in the Industry 4.0 era because enabling technologies such as the internet of things (IoT), artificial intelligence (AI), big data, and geographic information systems (GISs) allows for real-time solutions to versions of the problem, adapting to changing conditions such as traffic or fluctuating demand. Methods: In this paper, we model and optimize a classic multi-link distribution network topology, including randomness in travel times, vehicle availability times, and product demands, using a hybrid approach of nested linear stochastic programming and Monte Carlo simulation under a time-window scheme. The proposed solution is compared with cutting-edge metaheuristics such as Ant Colony Optimization (ACO), Tabu Search (TS), and Simulated Annealing (SA). Results: The results suggest that the proposed method is computationally efficient and scalable to large models, although convergence and accuracy are strongly influenced by the probability distributions used. Conclusions: The developed proposal constitutes a viable alternative for solving real-world, large-scale modeling cases for transportation management in the supply chain.

Background: The primary objective of this study is to investigate the influence of big data analytics (BDA) on supply chain (SC) risk, SC ambidexterity, and SC resilience. It further examines the effects of SC risk and SC ambidexterity on SC resilience and explores their mediating roles in the BDA–SC resilience relationship. Despite growing interest in BDA and resilience, limited empirical research has addressed these linkages in pharmaceutical distribution, particularly in emerging economies such as Jordan. Methods: A quantitative research strategy was adopted, employing a survey-based methodology. Data were obtained from 204 managers in pharmaceutical distribution companies in Jordan. Results: The findings indicate that BDA reduces SC risk and positively influences SC ambidexterity and SC resilience. Furthermore, SC risk and SC ambidexterity positively affect SC resilience. Notably, both variables partially mediate the BDA–SC resilience relationship, with ambidexterity showing a stronger effect. Conclusions: Grounded in the resource-based view and the dynamic capability view, this study provides empirical evidence that BDA enhances SC resilience primarily by fostering ambidexterity and mitigating risks. By clarifying the distinct mediating roles of SC risk and SC ambidexterity, the research extends theory and offers practical insights for managers seeking to build more resilient pharmaceutical SCs.

Background: This study examines the degree to which cognitive supply chain management (CSCM) indirectly enhances supply chain risk management (SCRM), addressing the lack of specific empirical research concerning the underlying mechanisms of this relationship. Specifically, this study tests the CSCM-SCRM relationship using the mediating roles of supply chain forecasting (SCF), supply chain synchronization (SCS), and supply chain transparency (SCT). Methods: For this quantitative research, a survey was conducted among 287 respondents of pharmaceutical companies operating in Saudi Arabia. Convenience sampling was conducted, and the collected data were then analyzed via partial least squares structural equation modeling (PLS-SEM) through SmartPLS 4 software. The dynamic capabilities theory (DCT) and information processing theory (IPT) were integrated to develop the conceptual framework of this study. Results: The findings indicate that CSCM does not exert a direct impact on SCRM. Instead, CSCM significantly enhances SCF, SCS, and SCT. Among these, both SCF and SCT have a direct positive impact on SCRM and act as significant mediators in the CSCM–SCRM relationship. In contrast, SCS neither directly impacts SCRM nor plays a mediating role in this relationship. Based on this study, the positive outcomes of CSCM on SCRM come about via SCF and SCT rather than SCS. Conclusions: This study contributes to the literature by empirically validating a model that integrates CSCM, SCF, SCS, SCT, and SCRM in the context of Saudi pharmaceutical companies. It further contributes to the pharmaceutical practitioners by establishing that CSCM exerts an indirect positive effect on SCRM via information-intensive capabilities.