In the post-pandemic world, manufacturing enterprises face increasing uncertainties, especially with vulnerabilities in global supply chains. Although supply chain management has been extensively studied, the critical influence of decision-makers (DMs) in these systems remains underexplored. This study studies the inventory management problem under risk using the newsvendor model by incorporating DMs risk preferences. By employing the Quantum Monte Carlo (QMC) combined with Quantum Amplitude Estimation (QAE) algorithm, the estimation of probabilities or expectation values can be done more efficiently. This offers near-quadratic speedup compared to classical Monte Carlo methods. Our findings illuminate the intricate relationship between risk-aware decision-making and inventory management, providing essential insights for enhancing supply chain resilience and adaptability in uncertain conditions
In the post-pandemic world, manufacturing enterprises face increasing uncertainties, especially with vulnerabilities in global supply chains. Although supply chain management has been extensively studied, the critical influence of decision-makers (DMs) in these systems remains underexplored. This study studies the inventory management problem under risk using the newsvendor model by incorporating DMs risk preferences. By employing the Quantum Monte Carlo (QMC) combined with Quantum Amplitude Estimation (QAE) algorithm, the estimation of probabilities or expectation values can be done more efficiently. This offers near-quadratic speedup compared to classical Monte Carlo methods. Our findings illuminate the intricate relationship between risk-aware decision-making and inventory management, providing essential insights for enhancing supply chain resilience and adaptability in uncertain conditions
Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we use a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical Newsvendor Problem known to be NP-hard. Such problems provide the building block for supply chain management, particularly in inventory management and procurement optimization under risks and uncertainty.
In an era of global supply chain disruptions, SAP Materials Management (MM) remains critical for procurement, inventory, and logistics optimization. This research pioneers the integration of quantum machine learning (QML) to enhance predictive analytics in SAP MM, addressing challenges like demand volatility and supplier delays. By leveraging quantum algorithms, such as quantum support vector machines (QSVM) and variational quantum circuits (VQC), the proposed framework forecasts inventory needs, detects supply chain risks, and optimizes procurement with unprecedented accuracy. A synthetic dataset of 150,000 MM transactions simulates real-world complexities, including multilingual data and disruptions. The analysis achieves 95% prediction accuracy, reducing stockouts by 65% compared to classical ML methods. Visualizations, including risk heatmaps and performance graphs, guide resilient supply chain strategies. This framework tackles data privacy, computational scalability, and SAP integration, offering a transformative approach to supply chain management in volatile markets.
Resilience of the supply chain has turned out to be a critical national concern due to the revelations of vulnerabilities in the global networks of logistics that cannot be sufficiently managed by traditional optimization tools.Artificial intelligence and quantum computing can provide revolutionary answers to complex supply chain challenges, addressing them using new computational paradigms.Quantum algorithms use superposition and entanglement to search a solution space that is exponentially larger than the space available to classical computers, and Ireland enables optimal resource allocation in large networks.Hybrid quantum-classical systems are a strategic allocation of computational workload where quantum processors are processing combinatorial optimization kernels and classical systems are doing data preprocessing and solution validation.The quantum algorithms used to optimize the transportation network during disruption conditions are useful in optimizing the network due to the presence of multiple routing strategies in various circumstances of failures.Manufacturing coordination deals with the issue of production scheduling, inventory distribution, and supplier redundancy by quantum formulations that trade off conflicting operational goals.Existing hardware limits put a lower bound on the achievable practical implementation, but the current fast development of qubit quality, connectivity, and error correction implies that a practical quantum advantage soon will be reached in large-scale optimization of supply chains.Quantum computing and supply chain management integration create new principles of ensuring the preservation of critical resource flows in the case of emergencies, which proves the possibilities of increased national resiliency to pandemics, geopolitical conflicts, and natural disasters.
Resilience of the supply chain has turned out to be a critical national concern due to the revelations of vulnerabilities in the global networks of logistics that cannot be sufficiently managed by traditional optimization tools. Artificial intelligence and quantum computing can provide revolutionary answers to complex supply chain challenges, addressing them using new computational paradigms. Quantum algorithms use superposition and entanglement to search a solution space that is exponentially larger than the space available to classical computers, and Ireland enables optimal resource allocation in large networks. Hybrid quantum-classical systems are a strategic allocation of computational workload where quantum processors are processing combinatorial optimization kernels and classical systems are doing data preprocessing and solution validation. The quantum algorithms used to optimize the transportation network during disruption conditions are useful in optimizing the network due to the presence of multiple routing strategies in various circumstances of failures. Manufacturing coordination deals with the issue of production scheduling, inventory distribution, and supplier redundancy by quantum formulations that trade off conflicting operational goals. Existing hardware limits put a lower bound on the achievable practical implementation, but the current fast development of qubit quality, connectivity, and error correction implies that a practical quantum advantage soon will be reached in large-scale optimization of supply chains. Quantum computing and supply chain management integration create new principles of ensuring the preservation of critical resource flows in the case of emergencies, which proves the possibilities of increased national resiliency to pandemics, geopolitical conflicts, and natural disasters.
Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by IBM Qiskit and the qBraid system.
The continuous growth of financial markets dictates the need for its participants to seek new approaches to financial analysis to gain competitive advantages, including through the use of new approaches in the field of computing. Quantum computing can be used as a tool for obtaining these advantages over competitors. In particular, Monte Carlo modeling, although widely used in financial risk management, requires significant computing resources due to the large number of scenarios required to obtain an accurate result. To optimize this approach, quantum amplitude estimation algorithms are used, which accelerate this process if pre-calculated probability distributions are used to initialize input quantum states. However, in the absence of these distributions in existing approaches to this topic, they are generated numerically using classical computing, which completely negates the advantage of the quantum approach. This article proposes a solution to this problem by using quantum computing, including for the generation of probability distributions. The article discusses the creation of quantum circuits for modeling the evolution of risk factors over time for capital flows, interest rates, and credit risks, and presents the combination of these models with quantum amplitude estimation algorithms as an example of using the obtained algorithms for credit risk management. In conclusion, the article analyzes the possibility of using the obtained circuits in financial analysis.