Supply Chain Analytics最新文献

A game theoretic model for dual supply chains with green and non-green products and bi-directional free-riding and carbon policy 具有绿色和非绿色产品以及双向搭便车和碳政策的双供应链博弈论模型

Supply Chain Analytics

Pub Date : 2025-02-07 DOI: 10.1016/j.sca.2025.100103

Sanchari Ganguly , Pritha Das , Manoranjan Maiti

Cap-and-trade regulation is a strategy to reduce carbon emissions (CEs). During production, CEs are reduced by green technology. In a dual-channel supply chain (DCSC), customers try a product at an offline store but purchase it online (showrooming effect). Additionally, using internet information services, some customers purchase offline (ropo effect). Due to demand uncertainty, neutrosophic fuzzy sets are used to appropriately express a parameter’s impreciseness. We develop a game-theoretic model where a manufacturer produces non-green and green products using carbon reduction technology, sells the products through a traditional retailer (offline), and owns an online channel for imprecise market demands. Customers free-ride from both the channels. The CE from transportation and the non-green products are considered. For carbon costs, a cap and trade policy is adopted. The neutrosophic fuzzy variables indicate the impreciseness of the demand, bidirectional free-riding, and product greenness. These variables determine channel members’ truth, indeterminacy, and falsity degrees. Different models with some prices (inconsistent and consistent) and service efforts as decision variables are analyzed using the Stackelberg game approach. After the derivation of the corresponding equilibrium equations, numerical experiments are presented to verify the validity of our conclusions. The findings show that although free-riding is detrimental to the retailer, it becomes advantageous if its direction is altered. The profit of the retailer with consistent prices is higher than the inconsistent one. Opposite outcomes are observed for the manufacturer. The channel members’ profits are more under the neutrosophic fuzzy environment than deterministic/fuzzy. Some managerial insights and conclusions are presented.

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引用次数: 0

A robotic process automation model for order-handling optimization in supply chain management

Supply Chain Analytics

Pub Date : 2025-01-17 DOI: 10.1016/j.sca.2025.100102

Ahm Shamsuzzoha , Sini Pelkonen

This study proposes a robotic process automation (RPA) model to streamline and optimize order-handling procedures in supply chain management. The current manual approach to order handling poses challenges, including limited accessibility and significant cognitive demands on employees. An information systems design methodology is applied to analyze and improve the process, with data gathered through semi-structured interviews to address these issues. The findings highlight that reducing manual labor alleviates workload imbalances and saves time in supply chain automation. Moreover, automating repetitive tasks through well-designed software bots minimizes the risk of human error. While this research focuses on applying RPA in order handling, future studies should explore the potential of artificial intelligence-driven RPA to enhance process automation further.

引用次数: 0

An investigation of foreign affiliates and supply chain productivity in the European Union industrial sectors

Supply Chain Analytics

Pub Date : 2025-01-14 DOI: 10.1016/j.sca.2025.100101

Antonio Frenda , Abdoulaye Kané

This study examines the relationship between foreign affiliates and labour productivity in the supply chains of construction and manufacturing sectors. Labour productivity is calculated using EUKLEMS & INTANProd database of the Luiss Lab of European Economics, while foreign affiliates abroad data are taken from Eurostat. Based on 19 EU countries from 2010 to 2019, we demonstrate how turnover per employee in foreign subsidiaries controlled by the reporting country positively and significantly impacts labour productivity in the construction sector supply chains. Foreign direct investment from these European countries also positively and significantly impacts labour productivity in sectors’ supply chains. Public decision-makers can use this study to highlight elusive fiscal strategies and outline the actual share of domestic and foreign productivity for industrial economic sector supply chains by considering the impact of permanent establishments.

引用次数: 0

A Bayesian best-worst approach with blockchain integration for optimizing supply chain efficiency through supplier selection

Supply Chain Analytics

Pub Date : 2025-01-02 DOI: 10.1016/j.sca.2024.100100

Azam Modares , Vahideh Bafandegan Emroozi , Pardis Roozkhosh , Azade Modares

Supplier selection is a complex Multi-Criteria Decision-Making (MCDM) problem where decision-maker (DM) preferences heavily influence decision criteria and outcomes. Suitable suppliers capable of meeting performance criteria are central to successful Blockchain Technology (BT) implementation. Numerous qualitative factors influence blockchain adoption within organizations, particularly in the communication between retailers and suppliers via Blockchain, where qualitative uncertainties abound. This study aims to develop a robust system within a probabilistic and fuzzy framework to integrate DMs’ judgments amidst uncertainty effectively. Leveraging the Bayesian best-worst method (BWM), optimal weights for evaluating supplier criteria are determined. This method employs Markov-chain Monte Carlo (MCMC) to calculate the probability of preferring one criterion over another, facilitating confidence level elucidation between criterion pairs and enhancing criteria rankings. Supplier ranking is performed using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The efficacy of the proposed approach is demonstrated through a case study utilizing real data from the railway supply chain. Results indicate the model’s effectiveness in optimizing supplier selection and enhancing supply chain performance.

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引用次数: 0

A data-driven machine learning model for forecasting delivery positions in logistics for workforce planning

Supply Chain Analytics

Pub Date : 2024-12-28 DOI: 10.1016/j.sca.2024.100099

Patrick Eichenseer , Lukas Hans , Herwig Winkler

Workforce planning in logistics is a major challenge due to increasing demands and a dynamic environment. The number of delivery positions is a key factor in determining staffing requirements. This is often predicted subjectively based on employee assessments. To improve decision making and increase both the efficiency of this important forecasting process and the use of resources in the production system, i.e. shopfloor logistics, a data-driven machine learning model with a forecasting horizon of 5 working days was developed and validated in a practical case study in a company. The results show that the novel and specifically developed model outperforms both the manual forecasting approach in practice and auto machine learning models in terms of accuracy. The outperformance is particularly strong in the short term. Based on the predicted delivery positions, an optimised workforce planning was subsequently carried out in the case study company. Limitations of the model include the fact that it was validated in only one company and that the number of picks may need to be derived for more accurate scheduling. These two aspects also represent potential for future research.

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引用次数: 0

A resilient poultry vaccine supply chain network design considering perishability and stress test

Supply Chain Analytics

Pub Date : 2024-12-27 DOI: 10.1016/j.sca.2024.100098

Mina Mehravaran, Arash Nemati

Vaccines and seeds are the most significant resources in the poultry industry, particularly for chicken as a global staple. However, designing a resilient network for the poultry vaccine supply chain to create a viable poultry industry has been neglected. Hence, this paper contributes to the poultry vaccine supply chain network design and planning problem by proposing a multi-period mixed-integer linear mathematical model. This model formulizes several resiliency strategies, including considering surplus vaccine production capacity, inventory holding in the customers and manufacturers, simultaneous utilization of both offshore suppliers and domestic manufacturers, and the necessity for fulfilling a proportion of periodic demand using warehoused vaccines. In addition, time-based prices and limited holding periods of poultry vaccines are considered in the model formulation to consider vaccine perishability. The newly developed model minimizes the poultry vaccine supply chain’s total costs, including vaccine purchasing, transportation, warehousing, manufacturer establishment, and opportunity cost, to make decisions on poultry vaccine manufacturer location-allocation, offshore supplier selection, customer order allocation, depot selection, and production capacity allocation. This model is solved in a case study of the chicken industry from Iran using CPLEX solver to design a new supply chain network for Newcastle, Gumboro, and Bronchitis vaccines from 2025 to 2030. Results showed that 6, 7, and 7 locations of 8 candidates have opted for Newcastle, Gumboro, and Bronchitis vaccine production lines establishment, respectively, and two offshore suppliers are selected between 4 potential ones. In addition, the results of stress tests verified the effectiveness of employed resiliency strategies, and sensitivity analysis showed the significant impact of demand variability on establishment and purchasing costs.

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引用次数: 0

An Integrated Multi-Product Biodiesel and Bioethanol Supply Chain Model with Torrefaction Under Uncertainty 不确定条件下多产品生物柴油和生物乙醇集成供应链模型

Supply Chain Analytics

Pub Date : 2024-11-28 DOI: 10.1016/j.sca.2024.100092

Farima Salamian , Masoud Rabbani , Amirmohammad Paksaz

This study presents an integrated supply chain network model for biodiesel and bioethanol production, incorporating torrefaction under uncertain conditions related to the establishment of new facilities. The proposed mixed-integer linear programming model aims to minimize the total cost of the supply chain while maximizing social objectives such as reducing unemployment. To solve the bi-objective model, a three-stage approach is employed: first, uncertain parameters are defuzzified; second, the augmented epsilon-constraint method is applied to generate a set of efficient Pareto-optimal solutions; and third, robust optimization is used to handle real-world uncertainties, such as disruptions caused by natural disasters and sanctions, ensuring feasibility under different scenarios. The study considers various stages of the supply chain, from feedstock cultivation to processing, transportation, and distribution. A real-life case study in Iran is used to evaluate the effectiveness of the proposed model, highlighting that biodiesel and bioethanol supply chains are interrelated, particularly at the cultivation stage, where each crop impacts the other. In this regard, Kermanshah, Isfahan, Chahar Mahal & Bakhtiari, Khorasan North, Kohgiluyeh & Boyer-Ahmad, and Lorestan are identified as the most suitable provinces for second-generation plant cultivation. Additionally, Azerbaijan East is identified as the best location for a bioethanol refinery, while Tehran and Markazi are the optimal choices for biodiesel refineries. This integrated approach offers a novel solution that prevents impractical overlaps in land use, providing a comprehensive, sustainable, and socially beneficial framework for bioenergy supply chain management.

本研究提出了生物柴油和生物乙醇生产的集成供应链网络模型，其中包括与建立新设施相关的不确定条件下的烘烤。提出的混合整数线性规划模型旨在使供应链的总成本最小化，同时使降低失业等社会目标最大化。为了求解双目标模型，采用了三步法：首先对不确定参数进行去模糊化；其次，应用增广的epsilon约束方法生成一组有效的pareto最优解；第三，鲁棒优化用于处理现实世界的不确定性，如自然灾害和制裁造成的中断，确保在不同场景下的可行性。该研究考虑了供应链的各个阶段，从原料种植到加工、运输和分销。在伊朗进行的一项现实案例研究被用来评估所提出模型的有效性，该研究强调了生物柴油和生物乙醇供应链是相互关联的，特别是在种植阶段，每种作物都会相互影响。在这方面，克尔曼沙阿、伊斯法罕、查哈尔玛哈和；巴赫蒂亚里，呼罗珊北部，科吉卢耶&；Boyer-Ahmad和Lorestan被确定为最适合种植第二代植物的省份。此外，阿塞拜疆东部被确定为生物乙醇精炼厂的最佳地点，而德黑兰和马卡齐是生物柴油精炼厂的最佳选择。这种综合方法提供了一种新颖的解决方案，可以防止土地使用中不切实际的重叠，为生物能源供应链管理提供一个全面、可持续和社会有益的框架。

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引用次数: 0

A novel sequential block path planning method for 3D unmanned aerial vehicle routing in sustainable supply chains

Supply Chain Analytics

Pub Date : 2024-11-28 DOI: 10.1016/j.sca.2024.100094

Muhammad Ikram , Robert Sroufe

Managing sustainable supply chain operations in dynamic three-dimensional (3D) environments is a significant challenge. Unmanned Aerial Vehicles (UAVs) offer transformative solutions to supply chains. This study aims to enhance sustainable supply chain management by considering new opportunities for optimizing UAV networks. The primary objective is to develop advanced path planning and routing algorithms that improve the quality of service in a supply chain. We present a novel Sequential Block Path Planning (SBPP) method, a modified version of the heuristic D* Lite algorithm, to achieve the shortest logistics path with reduced computation time. We utilize queuing theory for task scheduling and UAV assignments within a supply chain network while ensuring efficient and effective task distribution. The results demonstrate that the proposed combination of routing and path planning algorithms significantly improves performance in 3D environments, resulting in shorter logistics paths, enhanced quality of service, and reduced computation time. The outcomes of this study represent a substantial contribution to UAV network management, particularly in terms of efficiency and operational effectiveness. The novel approach utilized in this study contributes to the emerging UAV field in supply chains and enhances sustainability and operational efficiency in logistics networks.

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引用次数: 0

An agility and performance assessment framework for supply chains using confirmatory factor analysis and structural equation modelling 使用验证性因素分析和结构方程建模的供应链敏捷性和绩效评估框架

Supply Chain Analytics

Pub Date : 2024-11-23 DOI: 10.1016/j.sca.2024.100093

Akhil NSB , Rohit Raj , Vimal Kumar , Phanitha Kalyani Gangaraju , Tanmoy De

This study examines the impact of agile practices on supply chain performance measurements in manufacturing firms. Following COVID-19, there have been operational and logistics disruptions in manufacturing firms and supply chains worldwide. We study the link between supply chain performance and agile manufacturing practices by designing experimental research and collecting data from 340 responses from manufacturing firms. The experimental design proposed in this study uses a confirmatory factor and reliability analysis and smart-partial least square structural equation modeling. This research demonstrates the positive effect of agile supply chain strategies on manufacturing companies’ performance. The values obtained from the experiment support the dependability and effectiveness of the study. The research is supported by factors like customer involvement, facility management, supply chain responsiveness, strategic management, and supplier relationships but is undermined by technology utilization and supply chain contracts. The study will aid companies in combining agile with more conventional approaches to better adapt to market volatility and fierce global competition. Developing core competencies and acquiring a competitive advantage contribute to sustained advantage in the manufacturing industry. This study further outlines the need to understand how supply chains perform when agile practices are adopted.

本研究考察了敏捷实践对制造企业供应链绩效测量的影响。在2019冠状病毒病之后，全球制造企业和供应链出现了运营和物流中断。我们通过设计实验研究和收集340家制造企业的反馈数据来研究供应链绩效与敏捷制造实践之间的联系。本研究提出的实验设计采用验证性因子和可靠性分析以及智能偏最小二乘结构方程模型。本研究证明了敏捷供应链战略对制造企业绩效的积极影响。实验得到的数值支持了研究的可靠性和有效性。该研究得到了客户参与、设施管理、供应链响应、战略管理和供应商关系等因素的支持，但受到技术利用和供应链合同的破坏。这项研究将帮助企业将敏捷与更传统的方法结合起来，以更好地适应市场波动和激烈的全球竞争。发展核心竞争力和获得竞争优势有助于在制造业中获得持续的优势。本研究进一步概述了当采用敏捷实践时，了解供应链如何执行的必要性。

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引用次数: 0

A conceptual digital twin framework for supply chain recovery and resilience 供应链恢复和复原力数字孪生概念框架

Supply Chain Analytics

Pub Date : 2024-11-19 DOI: 10.1016/j.sca.2024.100091

Oluwagbenga Victor Ogunsoto , Jessica Olivares-Aguila , Waguih ElMaraghy

Amidst escalating global supply system risks and interruptions, the imperative for fortified supply networks is evident. Organizations striving for competitiveness and resilience must adeptly recognize, comprehend, and address disruptions. This study presents a three-phase digital supply chain twin framework, leveraging discrete event simulation and neural networks to anticipate floods—a typical natural catastrophe and disruptive event—and predict recovery indicators. This aids supply chain (SC) managers in making informed decisions. In the first phase, machine learning algorithms, including logistic regression and Long Short-Term Memory (LSTM), were trained on Kerala India's precipitation data to predict floods. LSTM outperforms logistic regression, achieving flood prediction with 73 % recall, 75 % accuracy, and 84 % Area Under Curve-Receiver Operating Characteristics score. In the second phase, simulations replicate value chain breakdowns. A process flow logic-driven discrete event simulation within a real-world SC network emulates operational disruptions. FlexSim is employed to model service-level failures, influencing SC model performance based on the distribution center service level. The third phase employs simulated case scenario data to train a multilayer neural perceptron network (MLPNN) for predicting production network recovery post-disruptions. The MLPNN monitors the mean squared error (MSE) and disruptive inputs throughout training and validation, revealing consistent MSE reduction over recovery periods. The number of epochs needed to achieve a minimum MSE is used as a recovery indicator to predict service restoration time. Consequently, this study introduces a conceptual digital twin framework for catastrophic operations chain breakdowns and recovery prediction. The framework's output assists SC planners in shaping robust strategies by foreseeing disruptions and facilitating recovery.

在全球供应系统风险和中断不断升级的情况下，强化供应网络的必要性显而易见。努力提高竞争力和复原力的组织必须善于识别、理解和应对中断。本研究提出了一个三阶段数字供应链孪生框架，利用离散事件模拟和神经网络来预测洪水--一种典型的自然灾害和破坏性事件--并预测恢复指标。这有助于供应链（SC）管理者做出明智决策。在第一阶段，包括逻辑回归和长短期记忆（LSTM）在内的机器学习算法在印度喀拉拉邦的降水数据上进行了训练，以预测洪水。LSTM 的表现优于逻辑回归，其洪水预测的召回率为 73%，准确率为 75%，曲线下面积-接收器工作特性得分率为 84%。在第二阶段，模拟复制了价值链中断。在现实世界的 SC 网络中进行流程逻辑驱动的离散事件仿真，模拟运营中断。FlexSim 用于模拟服务级故障，根据配送中心的服务水平影响 SC 模型的性能。第三阶段采用模拟案例情景数据来训练多层神经感知器网络（MLPNN），以预测中断后生产网络的恢复情况。在整个训练和验证过程中，MLPNN 监测均方误差（MSE）和中断输入，发现在恢复期间，MSE 持续降低。达到最小 MSE 所需的历元数被用作预测服务恢复时间的恢复指标。因此，本研究为灾难性运营链断裂和恢复预测引入了一个概念性数字孪生框架。该框架的输出可帮助 SC 规划人员通过预测中断和促进恢复来制定稳健的战略。

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