Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet's superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression's performance on each preprocessed dataset. The model's interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting "Not Backorder" and 75% for predicting "backorder," outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.
Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet's superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression's performance on each preprocessed dataset. The model's interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting "Not Backorder" and 75% for predicting "backorder," outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.
Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.
Purpose The purpose of this study is to explore the potential for quantum computing to enhance applied machine learning tasks. The goal is to investigate whether quantum computing principles such as superposition, entanglement and quantum parallelism can improve predictive accuracy and efficiency in machine learning models. Design/methodology/approach To examine the potential, this study simulated a 4-qubit quantum processor on an Intel Core i5 8th generation central processing unit (CPU). The proposed model combines quantum principle and deep neural networks architecture involving quantum-forward and classical-backward processes; quantum part is in the forward pass, and the traditional neural network handles the backpropagation during model training. This model was tested using a real dataset to assess its practical applicability to machine learning tasks under real-world constraints. Findings The findings reveal the effectiveness of the quantum-classical hybrid approach which achieves an average area under the curve score of 92%, with a precision of 22.6% and a recall of 25.3%. These results suggest that, while quantum noise and Qubit limitations inherent to Noisy Intermediate-Scale Quantum systems impacted prediction accuracy and model generalizability, the quantum-forward process enhanced the model’s ability to identify true positive cases in a complex dataset. Compared to traditional approaches, this model addresses both the stability and predictive demands of highly imbalanced inventory data, although limitations remain in precision and scalability. Originality/value While many open questions remain about the real advantages of quantum computing in applied field of business, this study contributes to the feasibility of leveraging quantum machine learning for complex decision-making problems in supply chain management.
This chapter explores the application of quantum machine learning (QML) techniques for demand prediction in supply chain networks. Traditional demand forecasting methods often struggle to capture the intricate dynamics and uncertainties present in modern supply chains. By leveraging the computational power and probabilistic nature of quantum computing, coupled with the flexibility and adaptability of machine learning algorithms, organizations can enhance the accuracy and efficiency of their demand prediction processes. This chapter provides an overview of QML methodologies tailored specifically for demand prediction in supply chain networks, highlighting their advantages over classical approaches. Through case studies and practical examples, the chapter demonstrates how QML can enable organizations to make more informed decisions, optimize inventory levels, and improve overall supply chain performance.
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.
This chapter explores the potential of quantum machine learning (QML) to enhance demand forecasting in supply chain management. With the increasing complexity and data volumes in modern supply chains, traditional forecasting methods often fall short. QML leverages the principles of quantum mechanics, such as superposition and entanglement, to process data at unprecedented speeds and complexity. The authors discuss theoretical frameworks, focusing on quantum algorithms like quantum support vector machines and quantum neural networks, which can handle high-dimensional data more efficiently than classical counterparts. The chapter evaluates the feasibility, challenges, and prospects of implementing QML in real-world supply chain scenarios, suggesting that QML could significantly improve forecasting accuracy and operational efficiency, ultimately transforming supply chain management practices.
Abstract: Modern supply chains face intricate challenges demanding sophisticated optimization strategies.This research presents a pioneering approach, combining quantum computing and machine learning, to revolutionize supply chain optimization. A hybrid framework harmonizes the power of quantum computing, including quantum annealers and gate-based quantum devices, with classical machine learning techniques. This synthesis aims to surmount the limitations of conventional optimization methods. Through rigorous experimentation on real-world supply chain scenarios, our approach demonstrates substantial efficiency gains and enhanced solution quality in comparison to classical methods. The implications are far- reaching, promising substantial cost reductions, streamlined resource allocation, and sustainability improvements for industries relying on supply chain management. Our study contributes to the burgeoning field of quantum machine learning and offers immediate value to supply chain professionals grappling with complexity. As supply chains evolve, our research sets the stage for innovative solutions in the quest for optimization.