Real-world Supply Chain Research Articles

Adaptive routing in agricultural supply chains: Harnessing Q-learning for optimal decision-making in dynamic environments

In this study, the authors try to emphasize how Q-learning, a model-free reinforcement learning (RL) technique can be used for optimizing routing in a grid-based environment. This study aims to assess the efficacy of Q-learning in enhancing routing for agricultural supply chains, investigate its flexibility in dynamic environments, and compare its performance across several real-world scenarios. In this specific case of the banana chain, an agent is moving through various entities in the system - from local growers to small traders and warehouses. It models the routing problem as a Markov Decision Process (MDP) and the goal is to optimize cumulative reward. Several possible cases are simulated, e.g. the finding of an optimal route for a given visit sequence that optimizes charging time and non-drivable paths left over when unexpected blockages occur to avoid energy wear penalties as well as how to best save costs; These results demonstrate the adaptability and durability of Q-learning in dynamic environments to obtain near-optimal solutions across diverse settings. Indeed, the present study adds to a growing body of research on the application of RL in logistics and supply chain management, highlighting its potential to enhance decision-making in complex and variable environments. The findings suggest that Q-learning can effectively balance multiple objectives, such as minimizing distance, reducing costs, and avoiding high-wear areas, making it a valuable tool for optimizing routing in real-world supply chains. Future work will explore broader applications and other RL algorithms in similar contexts.

Supply Chain Analysis Based on Community Detection of Multi-Layer Weighted Networks

As the economic environment becomes more complex, improving supply chain resilience is critical for the effective operation and long-term sustainability of businesses. Real-world supply chains, which consist of various components such as goods, warehouses, and plants, often feature intricate network structures that pose challenges for resilience analysis. This paper addresses these challenges by proposing a framework for studying supply chains using multi-layer network community detection. The complex multi-mode supply chain network is transformed into single-mode, multi-layer weighted networks. A multi-layer weighted community detection method is proposed for identifying local clusters within these networks, revealing interconnected groups that highlight flexibility and redundancy in production capabilities across different plants and goods. An empirical study utilizing real data demonstrates that this clustering method effectively detects indirect capacity links between plants and goods. The insights derived from this method are useful for strategic capacity management, allowing businesses to respond more effectively to supply shortages and unexpected increases in demand.

Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning

Managing delivery risks is a critical challenge in modern supply chain management due to the increasing complexity and interdependencies of global supply networks. Existing methods often rely on correlation-based approaches, which fail to uncover the true causes behind delivery delays. This limitation makes it difficult for supply chain managers to identify actionable factors that can mitigate risks effectively. To address these challenges, we propose a novel method that integrates causal discovery with reinforcement learning to identify the root causes of delivery risks. Unlike traditional correlation-based methods, our approach uncovers both the direction and strength of causal relationships between variables, allowing for more accurate identification of the key drivers behind delivery delays. By applying causal strength quantification, we further measure the impact of each factor on delivery performance. Using real-world supply chain data, our results demonstrate that the proposed method reveals hidden causal relationships between factors such as shipping mode, order size, and delivery status. These insights enable supply chain managers to implement more targeted interventions, significantly improving risk mitigation strategies.

DYNAMIC DEMAND FORECASTING IN SUPPLY CHAINSUSING HYBRID ARIMA-LSTM ARCHITECTURES

Demand forecasting in supply chains is essential for efficient inventory management, production planning, and cost optimization. Traditional time series models, such as Autoregressive Integrated Moving Average (ARIMA), are effective in modeling linear relationships but often fall short in handling the nonlinear complexities characteristic of volatile demand patterns. Conversely, deep learning models like Long Short-Term Memory (LSTM) networks have proven adept at capturing intricate temporal dependencies, though they are computationally intensive and require substantial data. This study introduces a hybrid ARIMA-LSTM framework that leverages the linearity strengths of ARIMA in conjunction with LSTMs capacity for learning non-stationary, nonlinear patterns. The hybrid model decomposes the demand series, modeling linear trends with ARIMA and residual nonlinear components with LSTM. Empirical evaluations of real-world supply chain data reveal that this integrated architecture outperforms standalone ARIMA and LSTM models, achieving superior predictive accuracy and robustness in handling demand variability. Our findings underscore the hybrid models potential as an advanced predictive solution for dynamic, data-driven supply chain management.

A tactical carcass optimisation tool for assisting boning room managers to increase boning room profit

In collaboration with Australian lamb supply chains, an integer linear programming model was developed for use by processors to optimally allocate carcasses to different cutting plans for implementation in a boning room on a daily or lot basis. This tactical carcass optimisation tool (TCOT) addresses the daily challenge of developing operational plans to process carcasses into products whilst maximising profitability subject to constraints on carcass availability, variability of carcass weight and fatness, number of product orders, product size and retail prices. Predicted weights for all available products were derived from cut-weight prediction algorithms established from the combination of carcass weight and fatness ranges. Carcass fatness was expressed as lean meat yield percentage (LMY%) and measured using dual-energy X-ray absorptiometry (DXA) of hot carcasses immediately after slaughter. The TCOT utilises a group-based labour cost model which closely aligns with real-world supply chain practices. The function and application of the optimisation tool were tested in two contrasting case studies. The first case study demonstrated the ability of the optimiser to allocate carcasses to optimal cutting plans based on carcass weight and LMY%. The second case study examined the potential value of the tool for processors supplying lamb into the Australian domestic lamb market and subject to the constraints applied, demonstrated opportunities to increase profitability by at least 1 % compared to current best practice. This work provides a pathway whereby objective carcass measurement technology, combined with carcass sortation and optimisation algorithms, can be leveraged to improve profitability for lamb supply chains.

GEPAF: A non-monotonic generalized activation function in neural network for improving prediction with diverse data distributions characteristics

The world today has made prescriptive analytics that uses data-driven insights to guide future actions. The distribution of data, however, differs depending on the scenario, making it difficult to interpret and comprehend the data efficiently. Different neural network models are used to solve this, taking inspiration from the complex network architecture in the human brain. The activation function is crucial in introducing non-linearity to process data gradients effectively. Although popular activation functions such as ReLU, Sigmoid, Swish, and Tanh have advantages and disadvantages, they may struggle to adapt to diverse data characteristics. A generalized activation function named the Generalized Exponential Parametric Activation Function (GEPAF) is proposed to address this issue. This function consists of three parameters expressed: α, which stands for a differencing factor similar to the mean; σ, which stands for a variance to control distribution spread; and p, which is a power factor that improves flexibility; all these parameters are present in the exponent. When p=2, the activation function resembles a Gaussian function. Initially, this paper describes the mathematical derivation and validation of the properties of this function mathematically and graphically. After this, the GEPAF function is practically implemented in real-world supply chain datasets. One dataset features a small sample size but exhibits high variance, while the other shows significant variance with a moderate amount of data. An LSTM network processes the dataset for sales and profit prediction. The suggested function performs better than popular activation functions when a comparative analysis of the activation function is performed, showing at least 30% improvement in regression evaluation metrics and better loss decay characteristics.

Global Mittag-Leffler Attractive Sets, Boundedness, and Finite-Time Stabilization in Novel Chaotic 4D Supply Chain Models with Fractional Order Form

This research explores the complex dynamics of a Novel Four-Dimensional Fractional Supply Chain System (NFDFSCS) that integrates a quadratic interaction term involving the actual demand of customers and the inventory level of distributors. The introduction of the quadratic term results in significantly larger maximal Lyapunov exponents (MLE) compared to the original model, indicating increased system complexity. The existence, uniqueness, and Ulam–Hyers stability of the proposed system are verified. Additionally, we establish the global Mittag-Leffler attractive set (MLAS) and Mittag-Leffler positive invariant set (MLPIS) for the system. Numerical simulations and MATLAB phase portraits demonstrate the chaotic nature of the proposed system. Furthermore, a dynamical analysis achieves verification via the Lyapunov exponents, a bifurcation diagram, a 0–1 test, and a complexity analysis. A new numerical approximation method is proposed to solve non-linear fractional differential equations, utilizing fractional differentiation with a non-singular and non-local kernel. These numerical simulations illustrate the primary findings, showing that both external and internal factors can accelerate the process. Furthermore, a robust control scheme is designed to stabilize the system in finite time, effectively suppressing chaotic behaviors. The theoretical findings are supported by the numerical results, highlighting the effectiveness of the control strategy and its potential application in real-world supply chain management (SCM).

Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method

The "Make or Buy” decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.

Assessing Supply Chain Quality Management Indicators: A Roadmap to Operational Excellence at Al-Zawraa General Company's Altahady Site

This research endeavors to assess and enhance supply chain quality management at the Al-Tahady Site of Al-Zawraa General Company. The primary objective is to evaluate a comprehensive set of supply chain quality management indicators and provide a strategic roadmap for achieving operational excellence. The research problem addressed in this study revolves around the need to improve supply chain quality management practices. Despite the inherent challenges in real-world supply chain research, particularly in data acquisition and information gathering, this study is resolute in its mission. Beyond analysis, the researcher seeks to propose practical application mechanisms to drive tangible benefits for the company in attaining its operational goals. The examination of key supply chain quality management indicators reveals significant insights. Notably, the compliance rate consistently falls within the range of (1.04%) to (1.24%), affirming unwavering commitment to meeting purchase orders and contract standards. Transmission accuracy exhibits a parallel consistency, ranging between (1.04%) and (1.24%), signifying a remarkable precision in deliveries. The punctuality commitment is evidenced by on-time delivery rates, fluctuating from (91%) to (93%). Additionally, this study unveils fluctuations in supplier lead times, varying from (0.16%) to (0.55%). These variations serve as valuable signals, highlighting areas ripe for supplier management improvement. Furthermore, the prudent cost management practices implemented by the company are substantiated by total procurement costs ranging from (239,400,000) to (277,200,000). this research constitutes a concerted effort to elevate supply chain quality management at Al-Zawraa General Company's Al-Tahady Site. Its findings provide actionable insights aimed at enhancing supply chain performance, boosting customer satisfaction, and fortifying competitiveness.

Optimizing Supply Chain Network Design Under Uncertainty: A Practical Methodology for Sustainable Value Creation

Evaluating supply chain network (SCN) designs is critical for organizations striving to optimize operations and achieve sustainable value creation. However, conventional models often oversimplify, failing to account for the complexities inherent in real-world supply chain environments. In this study, we propose an advanced approach to SCN evaluation that strikes a balance between practicality and sophistication, leveraging real-world data to inform decision-making. Our methodology aims to bridge the gap between theoretical models and practical implementation, offering a pathway to sustainable value creation in SCN design. By incorporating risk analysis, resilience modeling, and solution methods tailored to uncertainty, our approach provides a comprehensive framework for addressing the challenges of SCN design under uncertainty. Simulation results validate the efficacy of our methodology in facilitating informed decision-making and strategic planning within organizations.

Assessing resiliency in scale-free supply chain networks: a stress testing approach based on entropy measurements and value-at-risk analysis

Supply chain (SC) resiliency and risk management have garnered increasing attention recently. While several studies have explored the use of scale-free network models to design and optimise SC networks, there remains a lack of a generalised stress-testing method that can be applied to various types and sizes of SCs. To address this, we propose a novel approach to assess the resiliency of scale-free SC networks based on entropy measurements. By considering various sources of disruptions, including demand fluctuations, capacity constraints, and transportation disruptions, we create a stress test that measures the network's resiliency. Our method categorises the SC into plants, ports, and customers and analyses the impact of disruptions on individual nodes within similar categories. We utilise real-world SC logistics data and map it into a scale-free network representation. By calculating the global vulnerability of the entire SC network and the localised impact of node vulnerability, we gain insights into how disruptions propagate and affect interconnected nodes. We use value-at-risk (VaR) and conditional-VaR (CVaR) to identify high-risk nodes, highlighting nodes at risk of extreme performance fluctuations or failures. The analytical results have led to developing six propositions, which serve as references for practitioners and help generalise the findings.

A Multi-Objective Method Based on Tag Eigenvalues Is Used to Predict the Supply Chain for Online Retailers

E-commerce has grown quickly in recent years thanks to advancements in Internet and information technologies. For the majority of consumers, online shopping has emerged as a primary mode of shopping. However, it has become more challenging for businesses to satisfy consumer demand due to their increasingly individualized wants. To address the need for customized products with numerous kinds and small quantities, businesses must rebuild their supply chain systems to increase their efficiency and adaptability. The SI-LSF technique, which employs boosting learning in the target-relative feature space to lower the prediction error and enhance the algorithm's capacity to handle input-output interactions, is validated in this study using a genuine industrial dataset. The study successfully identifies the relationship between sales and sales as well as target-specific features by applying the multi-objective regression integration algorithm based on label-specific features to a real-world supply chain demand scenario.

Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks.

Solving the guaranteed-service time inventory model for real-world supply chains by implementing it in commercial optimisers

The guaranteed service time inventory (GSTI) model aims to satisfy customers’ demand in an assured time. As multi-echelon supply chains (SC) grow in size and complexity, there is a need to develop exact algorithms to compute the amount of safety stock to deliver products to customers in the guaranteed service time. This work proposed an algorithm to minimise the safety stock in real–world SCs in minutes. The application reads the data set provided in [31]; then a continuous initial solution is computed in seconds using the software library (IPOpt); finally, that solution is used as the initial solution to run the branch–and–cut (B&C) algorithm implemented in a state-of–the–art commercial solver. In the proposed application, the GSTI model is modified to be implemented in commercial solvers when the demand is normally distributed. The reader can download the application’s source code from [24] to analyse the results of the 38 instances. The application computes an integer solution to each of the 38 SCs; in the worst case, it takes 150 min to find the solution. Seventeen instances are solved optimally, and the worst gap between the actual and best solution is about 24% in three instances. The biggest instance is solved in less than 10 min, but other smaller ones are solved in a longer time because of the echelons’ structure (degree and depth). Thus, the time to compute a solution does not depend on the size of the SC but on its structure. The findings indicate that by using a state-of–the–art commercial solver (based on the B&C method) and running it with powerful computer capabilities, real–world SCs can be solved with techniques that return optimal solutions.

Artificial intelligence in supply chain management: enablers and constraints in pre-development, deployment, and post-development stages

This study presents a comprehensive investigation into the AI supply chain journey, combining a systematic literature review (SLR) and empirical interviews with supply chain experts. The objective is to identify and analyze key enablers and constraints influencing AI in the pre-development, deployment, and post-development stages. The research integrates empirical data with a Technology-Organization-Environment (TOE) framework, revealing the interactions between technological, organizational, and environmental factors. The thematic analysis uncovers six axial themes for the pre-development stage and one theme for the deployment and post-development stages respectively, providing valuable insights into factors influencing successful AI integration. Moreover, industry-specific insights are unveiled for the Airline, Agri-food, Retail, and Logistics sectors, emphasizing the importance of contextual factors and tailored AI strategies. The study contributes to the existing knowledge by offering practical implications for AI integration in supply chains, highlighting the significance of managing constraints and industry heterogeneity. By identifying and understanding the key constraints, this research provides a deeper understanding of the constraints faced during different stages of AI in supply chains. This study makes a substantial contribution to the current socio-technical discourse on the successful journey of AI in supply chains by deriving eight propositions that offer valuable insights. These propositions delve into the practical implications of addressing constraints and transforming them into enablers for achieving enhanced supply chain performance. The propositions offer guidance to both academic researchers and industry professionals, equipping them with actionable strategies to navigate the complexities and intricacies of integrating AI technologies into the supply chain. By embracing these propositions, stakeholders can effectively harness the power of AI to optimize various aspects of the supply chain, leading to improved efficiency, agility, and competitiveness. Ultimately, this research contributes to advancing the understanding of the AI journey in supply chains and offers practical solutions to drive the successful embracing of AI technologies in real-world supply chain environments.

Traceability Technology Adoption in Supply Chain Networks

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, and verifying sustainable supplier practices. Initiatives leading the implementation of traceability technologies must choose the least-costly set of firms—or seed set—to target for early adoption. Choosing this seed set is challenging because firms are part of supply chains interlinked in complex networks, yielding an inherent supply chain effect: benefits obtained from traceability are conditional on technology adoption by a subset of firms in a product’s supply chain. We prove that the problem of selecting the least-costly seed set in a supply chain network is hard to solve and even approximate within a polylogarithmic factor. Nevertheless, we provide a novel linear programming-based algorithm to identify the least-costly seed set. The algorithm is fixed-parameter tractable in the supply chain network’s treewidth, which we show to be low in real-world supply chain networks. The algorithm also enables us to derive easily computable bounds on the cost of selecting an optimal seed set. We leverage our toolbox to conduct large-scale numerical experiments that provide insights into how the supply chain network structure influences diffusion. These insights can help managers optimize their technology diffusion strategy. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01759 .

Predictive Analysis for Supply Chain Management Using Extreme Gradient Boost Classifier

A novel approach to predictive analysis and demand forecasting within supply chain management, employing the Extreme Gradient Boosting (XGBoost) classifier. In response to the escalating complexity and volatility of supply chains, accurate demand forecasting is imperative for optimizing inventory, production scheduling, and overall operational efficiency. Traditional forecasting methods often struggle to capture the nonlinear relationships and intricate patterns inherent in supply chain data. Conversely, XGBoost offers a potent machine learning technique adept at handling nonlinear relationships and delivering robust predictions. The proposed framework involves data preprocessing, feature engineering, model training, and validation stages. Through a case study employing real-world supply chain data, we demonstrate the superior performance of the XGBoost classifier over traditional methods in terms of accuracy, robustness, and scalability. This study underscores XGBoost's potential as a valuable tool for demand forecasting in supply chain management, facilitating informed decision-making, optimized inventory management, cost reduction, and enhanced customer satisfaction. Furthermore, the framework's adaptability and extendibility make it applicable to diverse industries and domains, contributing to the advancement of supply chain management through the integration of machine learning techniques for more precise and efficient demand forecasting

QAmplifyNet: pushing the boundaries of supply chain backorder prediction using interpretable hybrid quantum-classical neural network

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.

Hybrid Quantum-Enhanced Machine Learning for Supply Chain Optimization

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.

Techno-Economic and Life Cycle Assessment of Enhanced Rock Weathering: A Case Study from the Midwestern United States.

Enhanced rock weathering (ERW) is a carbon dioxide removal (CDR) strategy for combating climate change. The CDR potentials of ERW have been assessed at the process and national/global levels, but the environmental and economic implications of ERW have not been fully quantified for U.S. applications with real-world supply chain considerations. This study develops an optimization-based, integrated life cycle assessment and techno-economic analysis framework for ERW, which is demonstrated by a case study applying mining waste to croplands in the Midwestern U.S. The case study explores maximum transportation distances for intermodal transportation at varied mineral CDR yields and costs, informing supply chain design for economically viable ERW. ERW costs (US$45 to 472/tonne of net CO2e captured) and cradle-to-farm gate GHG emissions (41 to 359 kg CO2e/tonne of CO2e captured) are estimated based on a range of CDR yields and by transportation distances to and from two Midwest port destinations: Chicago and Duluth. Our sensitivity analysis identifies CDR yields, and transportation modes and distances as driving factors for result variations. Our study reveals the importance of ERW supply chain design and provides an example of U.S. CDR implementation. Our framework and findings can be applied to other regional ERW projects.