Logistics optimization and operations management increasingly depend on artificial intelligence and quantum computing to address complex, time-sensitive planning problems. Existing research has focused mainly on algorithmic benchmarks or speculative quantum advantage, while offering limited insight into how Hybrid Quantum AI creates organizational value under current hardware constraints. This study advances theory by integrating Information Processing Theory (IPT) with the Dynamic Capabilities View (DCV), demonstrating how Hybrid Quantum AI architectures generate organizational value despite the current absence of quantum advantage. Using a case analysis of Malaysia Airlines Cargo (MAB Kargo), it is shown how combining AI-driven data structuring with quantum-enhanced optimization can close critical information-processing gaps and support the development of sensing, seizing, and transforming capabilities. Empirically, this hybrid system achieved an 83% reduction in planning time and a 5% increase in capacity utilization. These findings demonstrate that the current value of quantum computing in logistics lies not in outperforming classical solvers, but in enabling modular, future-ready architectures that improve operational fit today and build strategic agility over time. The study contributes to management and information systems research by explaining “quantum readiness” as an organizational capability enabled by modular digital infrastructure, offering practical guidance for digitization roadmaps and computational investment decisions in next-generation logistics.
Logistics optimization and operations management increasingly depend on artificial intelligence and quantum computing to address complex, time-sensitive planning problems. Existing research has focused mainly on algorithmic benchmarks or speculative quantum advantage, while offering limited insight into how Hybrid Quantum AI creates organizational value under current hardware constraints. This study advances theory by integrating Information Processing Theory (IPT) with the Dynamic Capabilities View (DCV), demonstrating how Hybrid Quantum AI architectures generate organizational value despite the current absence of quantum advantage. Using a case analysis of Malaysia Airlines Cargo (MAB Kargo), it is shown how combining AI-driven data structuring with quantum-enhanced optimization can close critical information-processing gaps and support the development of sensing, seizing, and transforming capabilities. Empirically, this hybrid system achieved an 83% reduction in planning time and a 5% increase in capacity utilization. These findings demonstrate that the current value of quantum computing in logistics lies not in outperforming classical solvers, but in enabling modular, future-ready architectures that improve operational fit today and build strategic agility over time. The study contributes to management and information systems research by explaining “quantum readiness” as an organizational capability enabled by modular digital infrastructure, offering practical guidance for digitization roadmaps and computational investment decisions in next-generation logistics.
The complexity of global supply chains places immense cognitive demands on human planners, creating a high-pressure environment where optimal decision-making is paramount yet increasingly difficult to achieve. This research addresses the central challenge of how to leverage quantum computing (QC) and artificial intelligence (AI) to augment human capabilities in solving complex, NP-hard logistical problems that are often intractable for human planners alone. It contrasts the theoretical underpinnings of hybrid quantum-classical algorithms with the practical, empirically verified results from an industrial case study of an AI-driven logistics solution at Malaysian Airlines' MAB Cargo. The analysis demonstrates that while a generalized 'quantum advantage' remains a long-term goal, significant value is created today through hybrid systems that function as collaborative partners for human operators. In this model, AI excels at structuring complex data and managing operational workflows, while quantum processors tackle specific, computationally intensive sub-problems, presenting optimized solutions to their human counterparts. The findings suggest that the most viable path forward is a synergistic human-machine integration, where quantum-AI components handle the heavy computations, thereby elevating the human role from tedious manual calculation to strategic oversight and exception management.
This study examines how quantum computing, quantum algorithms, and AI-quantum hybrid models enhance logistics efficiency and sustainability in e-commerce. Logistics optimization is analyzed to improve routing, scheduling, and resource allocation. The mixed-method design combines a cross-sectional survey of professionals with semi-structured interviews. Quantitative data were analyzed using structural equation modeling in SmartPLS, and qualitative data were thematically assessed. A perception-based analysis examined how professionals perceive quantum-based logistic models compared to traditional AI-driven approaches. Professionals believe that these models can enhance logistics optimization, increasing efficiency and sustainability. Respondents perceived that quantum models could outperform AI-driven approaches, particularly in routing and freight scheduling, but highlighted high implementation costs, limited expertise, and cross-industry collaboration. Logistic optimization mediates the relationship between quantum technology and performance outcomes. This study provides empirical evidence on industry perceptions and strategic guidance for firms considering quantum logistics. Quantum-enabled logistics enhance operational performance and support global sustainability goals. The findings underscore the opportunities and challenges of quantum logistics, offering guidance for research and adoption strategies.
The rapid evolution of quantum technologies presents transformative opportunities for various industries, including the transport sector. As global supply chains grow increasingly complex and data-driven logistics become central to operational efficiency, quantum computing and quantum communication are emerging as potential enablers of next-generation transport systems. This research explores the intersection of quantum innovation and transportation, with a focus on enhancing efficiency, security, and decision-making processes in logistics and mobility. The study adopts a qualitative methodology grounded in an extensive literature review of scholarly publications, industry reports, and policy papers from the past decade. Sources were selected based on their relevance to quantum applications in transportation, including routing optimization, traffic flow prediction, secure communications, and fleet management. The review framework categorizes insights according to technological readiness, practical implementation, and strategic value in the transport sector. Findings from the literature suggest that quantum computing holds significant promise for solving large-scale optimization problems, such as dynamic route planning and freight allocation, which are currently limited by classical computational methods. Additionally, quantum key distribution (QKD) is highlighted as a secure communication solution for protecting sensitive data in autonomous and connected transport systems. Although real-world applications remain limited, pilot studies and simulations indicate measurable improvements in processing speed and security protocols. This research underscores the potential of quantum technologies to redefine competitive advantage in the transport sector. By integrating quantum solutions into existing digital infrastructure, companies may achieve faster, more secure, and more sustainable transport operations. The findings also call for interdisciplinary collaboration and proactive policy support to bridge the gap between theoretical potential and practical deployment.
Quantum computing offers transformative potential for addressing complex optimization challenges in logistics and supply chain management.This paper explores the practical applications of quantum annealing and hybrid quantum-classical algorithms within a stratified architecture spanning physical hardware, algorithmic middleware, and decision-support layers.We demonstrate how these technologies enable significant improvements in unit load device (ULD) and pallet configuration, dynamic routing under disruption, and real-time multi-objective optimization.Through analysis of real-world industry pilots, including DHL's quantum-assisted route optimization achieving substantial carbon emission reductions, Volkswagen's fleet efficiency improvements, and hybrid quantum solutions at major ports such as the Port of Los Angeles, we show measurable gains in operational efficiency, cost reduction, payload utilization, and supply chain resilience.The study integrates these advancements with established resilience frameworks and the author's prior contributions in supply chain resilience, automotive battery technologies, and intelligent transportation systems.While hardware limitations in the noisy intermediate-scale quantum (NISQ) era persist, hybrid approaches provide a viable pathway for near-term adoption.This work highlights the transition of quantum technologies from theoretical promise to practical deployment, offering a roadmap for more efficient, resilient, and sustainable global supply chains.Future directions include scaling hybrid systems and expanding applications across multi-modal logistics networks
Abstract: The accelerating convergence of artificial intelligence, quantum and quantum-inspired computation, digital twins, advanced sensing, secure connectivity and operations research creates a new design space for transforming economic systems, industrial production and service delivery. Yet the literature remains fragmented across manufacturing, infrastructure, networks, public administration and domain-specific optimization. This paper develops a unified Quantum-AI Innovation and Optimization for Transformation (QAIOT) framework that links data intelligence, quantum-enhanced search, multi-objective optimization, sectoral constraints, governance and measurable transformation outcomes. The study adopts a model-based analytical design and synthesizes only the references supplied in the source corpus. A mathematical formulation combines state-space dynamics, matrix coupling, constrained multi-objective optimization, Bayesian risk adjustment and a composite transformation index. The numerical demonstration uses normalized 0–10 values to show how productivity, resilience, sustainability, service quality, innovation capacity and institutional trust can be integrated without presenting the numerical outputs as empirical findings. The analysis indicates that the greatest conceptual value of Quantum-AI is not a universal computational speedup, but selective acceleration of difficult search, scheduling, allocation, network and simulation problems embedded within AI-driven decision pipelines. Sectoral benefits depend on data quality, interoperability, compute economics, cybersecurity, governance and human oversight. The paper contributes an extensible cross-sector architecture, a prior-art matrix, a transparent numerical scoring model and an implementation roadmap for testing Quantum-AI systems in manufacturing, energy, finance, logistics, healthcare, public services and digital service ecosystems. The framework is intended to support rigorous empirical validation and responsible deployment rather than unsupported claims of quantum advantage. Keywords: Quantum-AI; intelligent optimization; industrial transformation; service-sector transformation; digital servitization; quantum-inspired optimization; decision intelligence; multi-objective optimization; digital twins; autonomous systems; economic transformation; resilience; sustainability; AI-native infrastructure; governance
As organizations navigate increasingly complex operational environments, conventional risk management models often fail to capture the nonlinear, high-dimensional interactions that drive modern crises. The convergence of artificial intelligence (AI) and quantum computing offers a transformative framework for predictive risk assessment and adaptive crisis response. This study explores the design and application of AI–quantum hybrid models that integrate quantum-enhanced computation with machine learning to improve the accuracy, speed, and scalability of organizational risk forecasting systems. The proposed framework employs quantum machine learning (QML) algorithms—such as the Quantum Support Vector Machine (QSVM) and Quantum Neural Networks (QNNs)—in conjunction with classical deep learning methods for multi-layered risk prediction and decision optimization. By leveraging quantum superposition and entanglement, the system enhances the exploration of correlated variables in financial, operational, and reputational risk domains. Empirical simulations demonstrate that hybrid AI–quantum approaches outperform purely classical models in identifying latent crisis patterns, managing uncertainty propagation, and optimizing mitigation strategies under dynamic constraints. The study underscores the strategic importance of quantum-ready organizational resilience frameworks, emphasizing interpretability, data security, and ethical governance in hybrid model deployment. By merging predictive analytics with quantum optimization, this research establishes a pathway toward proactive, intelligent, and resilient organizational crisis management in an era of exponential technological change.