The study presents the earliest effort to conceptualize SCM integration with quantum computing principles, providing dynamic capabilities for SCM. It proposes a unique interdisciplinary framework that demonstrates where hybrid quantum-classical approaches can realise value, and specifies the conditions that organizations must meet to adopt such methods. In this article, a conceptual framework has been proposed for HQCSCO. This framework facilitates key decision-making areas such as inventory optimisation, production scheduling, and logistics routing, where quantum-enhanced algorithms could supplement classical models to enhance performance. At the managerial level, the framework helps manufacturing leaders to determine viability, prioritize high-impact use cases, and develop future-ready digital capabilities that enhance cost efficiency, agility, and resilience. The research provides a conceptually-based baseline of quantum-facile supply chains and offers a research agenda to investigate the strategic, ethical, and operational implications thereof.
The study presents the earliest effort to conceptualize SCM integration with quantum computing principles, providing dynamic capabilities for SCM. It proposes a unique interdisciplinary framework that demonstrates where hybrid quantum-classical approaches can realise value, and specifies the conditions that organizations must meet to adopt such methods. In this article, a conceptual framework has been proposed for HQCSCO. This framework facilitates key decision-making areas such as inventory optimisation, production scheduling, and logistics routing, where quantum-enhanced algorithms could supplement classical models to enhance performance. At the managerial level, the framework helps manufacturing leaders to determine viability, prioritize high-impact use cases, and develop future-ready digital capabilities that enhance cost efficiency, agility, and resilience. The research provides a conceptually-based baseline of quantum-facile supply chains and offers a research agenda to investigate the strategic, ethical, and operational implications thereof.
Background: This paper investigates hybrid quantum–classical optimization approaches for addressing core supply chain management (SCM) problems. A unified hybrid framework is implemented and evaluated across five representative domains: vehicle routing, scheduling, facility location, inventory optimization, and demand forecasting. Methods: The framework integrates quantum algorithms—namely the Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing (QA), and the Variational Quantum Eigensolver (VQE)—with classical constraint-handling and local refinement procedures in an iterative workflow. Quantum solvers are employed for global solution exploration, while classical optimization ensures feasibility and convergence stability. Results: Experiments conducted on standardized synthetic benchmarks demonstrate that the proposed hybrid framework consistently outperforms classical-only and quantum-only baselines, achieving 12–18% reductions in operational costs and 20–35% faster convergence. In routing and fulfilment tasks, quantum-generated candidate solutions provide effective warm starts for classical refinement. Robustness analysis based on stochastic SCM simulations further indicates lower performance variance under uncertainty. Conclusions: These results demonstrate that hybrid quantum–classical optimization constitutes a practical and scalable strategy for near-term SCM decision-making under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints.
This investigation gives introduction of 'PRABAX' Quantum Supply Chain Optimization Model, using quantum optimization techniques in decision making processes of supply chains. By using the combination of the latest developments of quantum computing with operational practices for routing, scheduling, inventory, demand forecasting, the model covers the prevalent challenges relating to scalability, uncertainty and resilience. A systematic literature review based on PRISMA guidelines for synthesising both classical and quantum optimisation strategies. The study introduces Quantum Annealing (QA), Quantum Approximate Optimization Algorithm (QAOA), Quantum Machine Learning (QML), and Hybrid Quantum - Classical Methodology into a coherent concept. To capture the dynamics of the context, such issues as mediating variables such as the choice of algorithm and refining a hybrid model and of moderating factors such as the maturity of hardware and data integrity, and the integration constancy are investigated. The implementation of QE within the 'PRABAX' framework ensures the improvement of the resilience of the supply chains, improve forecasting accuracy, and reduce the computational efficiency. Empirical demos suggest that hybrid workflows that incorporate quantum capabilities with the practical constraints of current available hardware provide the most feasible short term trajectory. The proposed framework helps managers and policy makers to understand the effective adoption pathways. It advises a phased implementation approach with initial short routinely hybrid pilot initiatives, followed by the medium-term system integration, and long run transition to full quantum native functioning. The 'PRABAX' model is both an extension to the theory of optimisation and a combination of quantum concepts, which places the idea of hybrid frameworks as an emerging paradigm in the field of supply chain literature. It bridges the gap between developments in computational science to established theories and models about resilience, dynamic capabilities and resource-based views, and represents quantum optimisation as an important strategic asset. This study represents the first fully established framework for the optimisation of quantum enabled supply chains that clearly outlines the hypotheses that can be tested as well as clear strategies for their adoption, helping to guide future academic research and practices.
The accelerating complexity of global supply chains, particularly among small and medium-sized enterprises (SMEs), presents challenges that exceed the capabilities of conventional computational models. Quantum computing, with its inherent ability to process vast solution spaces through superposition and entanglement, offers a transformative approach to supply chain optimization. This study evaluates the feasibility and potential impact of applying quantum and hybrid quantum–classical algorithms—such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing—to logistics scheduling, demand forecasting, and inventory management in SMEs. It critically analyzes algorithmic efficiency, scalability, and cost–benefit ratios within limited computational infrastructures. The research integrates insights from operations research, quantum algorithm design, and management science to identify viable entry points for SME adoption. Findings suggest that while quantum computing remains in the NISQ (Noisy Intermediate-Scale Quantum) stage, near-term applications through cloud-based hybrid platforms could significantly enhance decision accuracy, resource allocation, and resilience against supply chain disruptions.
Quantum computing presents a transformative opportunity for optimizing supply chain and logistics operations by addressing complex combinatorial challenges beyond the capabilities of classical computing. Traditional supply chain optimization relies on heuristic and mathematical models, which struggle with large-scale problems such as vehicle routing, warehouse management, demand forecasting, and last-mile delivery. Quantum computing, leveraging qubits and principles like superposition and entanglement, enables exponential computational power and efficiency. This paper explores quantum computing's applications in logistics, including quantum annealing for route optimization, fleet management, and supply chain network design. Companies like IBM, Google, and D-Wave are advancing quantum algorithms, with hybrid quantum-classical approaches emerging as practical solutions. While large-scale commercial adoption is still in its infancy, research demonstrates promising results in reducing operational costs, improving sustainability, and enhancing decision-making. The study synthesizes over 80 published works on quantum computing's role in logistics, providing insights into emerging technologies, challenges, and future potential. As the field progresses, early adoption and pilot programs will be crucial for businesses to gain a competitive edge in an increasingly complex global supply chain landscape. Quantum computing has the potential to redefine efficiency, cost reduction, and real-time adaptability in logistics and supply chain management.
Sustainable supply chains are becoming increasingly important as businesses strive to reduce environmental impact while maintaining efficiency and profitability. One emerging solution is Quantum Intelligence (Quantum AI)—a powerful combination of quantum computing and artificial intelligence that can handle highly complex and data-intensive supply chain problems. Quantum AI leverages unique quantum principles such as superposition and entanglement to explore a vast number of possible solutions simultaneously. This enables better decision-making in areas like production scheduling, inventory management, and emissions forecasting. As a result, organizations can optimize delivery routes, reduce operational costs, and significantly lower carbon emissions, contributing to more sustainable and environmentally responsible supply chains. A key approach in this field is the hybrid quantum-classical model, which combines the data-processing strengths of classical computing with the advanced computational capabilities of quantum systems. This hybrid approach helps overcome current limitations in quantum hardware, such as noise, limited qubits, and high error rates, making Quantum AI more practical for real-world applications. In addition, integrating technologies like the Internet of Things (IoT), blockchain, and digital twins further enhances supply chain performance. IoT devices provide real-time data from sensors, blockchain ensures transparency and traceability, and digital twins allow simulation of supply chain operations to predict environmental impacts and test different scenarios. Overall, Quantum AI represents a promising step toward building smarter, more efficient, and sustainable supply chains that can adapt to modern challenges while supporting global environmental goals.
Quantum computing is a groundbreaking innovation that can resolve complex supply chain problems that traditional computing techniques are unable to manage. Given a focus on information flow, optimization, and potential future applications, this study explores how supply chain management could utilize quantum computing. The study used a mixed-methods approach, including scenario modeling, case studies of prominent companies, and literature reviews. The study intends to evaluate the function of quantum computing in dynamic route optimization, investigate how it can enhance supply chain resilience, and examine how it could optimize the flow of information for decision-making processes. Findings demonstrate that quantum computing offers unprecedented computational power for scenario analysis and decision-making and operates exceptionally well in activities like dynamic route optimization, parcel packaging, and reorganization during disruptions. For instance, companies like DHL and FedEx utilize quantum systems to improve efficiency substantially. However, constraints like high implementation costs, cybersecurity weaknesses, and technological infancy prevent widespread acceptance. Further research should investigate hybrid solutions that integrate quantum and classical computing while addressing these obstacles. This paper concludes that although quantum computing has the potential to transform supply chains by improving information flow, resilience, and efficiency, its wider adoption will require overcoming current financial and technological challenges.