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.
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.
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.
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.
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.
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.
Abstract The quantum supply chain transforms logistics and supply chain management by harnessing quantum technologies to tackle longstanding challenges in optimization, resilience, and real-time decision-making. Through qubits and superposition, quantum computers deliver exponential speedups for complex problems including route optimization, unit load device (ULD) and pallet configuration, disruption management, and large-scale predictive modeling. This paper investigates the integration of quantum computing—especially quantum annealing—into supply chain operations. It highlights practical applications such as rapid multi-source data analysis, dynamic alternative routing during disruptions, and greater efficiency in high-velocity logistics. Grounded in established resilience frameworks and emerging technologies, the research illustrates how quantum solutions can significantly cut costs, boost adaptability, and strengthen global supply chain robustness. Drawing from studies on resilience strategies, automotive battery advancements, and intelligent transportation systems, this work positions quantum computing as a vital enabler for next-generation logistics while discussing opportunities, limitations, and the shift from theoretical promise to real-world implementation. Keywords Quantum Computing, Quantum Supply Chain; Logistics Optimization; Quantum Annealing; Supply Chain Resilience; Hybrid Quantum-Classical Algorithms; Unit Load Device Optimization; Disruption Management Quantum Routing.
The research explores the application of quantum computing to manufacturing and supply chain optimization in an effort to increase productivity, reduce costs, and improve product quality. Quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), are developed and evaluated to solve complex optimization problems in these domains. Quantum computing approaches are contrasted with traditional optimization techniques to demonstrate the potential advantages of quantum algorithms in terms of solution quality and working time efficiency. Practical implementation considerations of data availability, algorithm scalability, and system integration are also discussed. This research shows that quantum algorithms can effectively optimize production scheduling, resource allocation, and supply chain management, resulting in shorter production schedules and improved operational performance. This research recognizes the limitations of current quantum hardware, the complexity of the problem domain, and the difficulty of implementation. Despite these limitations, this research lays the foundation for further investigation and innovation in quantum computing for manufacturing and supply chain optimization, highlighting the potential for long-term transformative effects on industrial operations.