Supply Chain Analytics最新文献

An Integrated Multi-Product Biodiesel and Bioethanol Supply Chain Model with Torrefaction Under Uncertainty

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.

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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

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.

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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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引用次数: 0

A strategic and social analytics model for sustainable packaging in the cosmetic industry 化妆品行业可持续包装的战略和社会分析模型

Supply Chain Analytics

Pub Date : 2024-11-01 DOI: 10.1016/j.sca.2024.100090

Idiano D'Adamo , Massimo Gastaldi , Rossella Giacalone , Yigit Kazancoglu

Every day, we use cosmetic products that are not only focused on beauty but also with everything related to personal care and hygiene. The impact that these products have on sustainability cannot be overlooked. Many cosmetics contain unsustainable ingredients that can cause environmental damage and loss of biodiversity. In addition, fossil-based packaging contributes greatly to environmental pollution and increases waste in the absence of a circular supply chain. This work has a dual objective. The first is to provide a strategic analysis based on a multi-criteria approach to evaluate the most sustainable alternatives to traditional packaging that manufacturers could adopt based on the opinions of experts from different categories of stakeholders. In this study, the multi-criteria approach was employed, as it has been widely recognized in the literature for its effectiveness in evaluating and comparing alternatives across multiple, often conflicting criteria. The second is to provide a social analysis to assess consumers’ views, habits, preferences, and willingness to pay toward sustainable packaging. The results show divergence among experts who prefer refillable packaging while consumers prefer recyclable packaging. In contrast, a convergence in selling price and production costs is verified, highlighting the strategic importance of the economic dimension is for sustainable packaging, and the willingness to pay for sustainable packaging is about twice that of traditional packaging. The implications of this work suggest that circular supply chains covering the entire life cycle of products, based on a pragmatic approach, can drive the convergence of consumption and production patterns toward sustainable development.

我们每天使用的化妆品不仅注重美容，还涉及与个人护理和卫生有关的一切。这些产品对可持续发展的影响不容忽视。许多化妆品含有不可持续的成分，会造成环境破坏和生物多样性的丧失。此外，化石基包装也极大地加剧了环境污染，并在缺乏循环供应链的情况下增加了浪费。这项工作有双重目标。首先是提供基于多重标准方法的战略分析，根据不同类别利益相关者专家的意见，评估制造商可采用的最具可持续性的传统包装替代品。本研究采用了多重标准方法，因为该方法在评估和比较多种标准（通常是相互冲突的标准）的替代品方面的有效性已得到文献的广泛认可。其次是进行社会分析，评估消费者对可持续包装的看法、习惯、偏好和支付意愿。结果显示，专家倾向于可填充包装，而消费者则倾向于可回收包装。相比之下，销售价格和生产成本的趋同性得到了验证，凸显了经济维度对可持续包装的战略重要性，而且可持续包装的支付意愿约为传统包装的两倍。这项工作的影响表明，基于务实的方法，覆盖产品整个生命周期的循环供应链可以推动消费和生产模式向可持续发展靠拢。

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引用次数: 0

Amplifying swift-trust, collaboration, and teamwork in warehouse management through blockchain-enabled technology 通过区块链技术增强仓库管理中的快速信任、协作和团队精神

Supply Chain Analytics

Pub Date : 2024-10-28 DOI: 10.1016/j.sca.2024.100089

Sadia Samar Ali , Syed Aqib Jalil , Murshid Kamal , Rudra Rameshwar

This study explores the application of blockchain technology in optimizing warehouse management, focusing on improving transparency, security, and operational efficiency. By utilizing a Blockchain-based Warehouse Platform (BCWP), the study enhances inventory tracking and supply chain transparency. A fuzzy-Delphi method was employed to identify and evaluate critical blockchain practices, with their relative importance assessed using the Best-Worst Method (BWM). The Combined Compromise Solution (CoCoSo) technique further ranked key performance outcomes. The findings reveal that practices such as quality inspection and information sharing play a pivotal role in boosting warehouse performance. Additionally, the integration of blockchain technology led to significant improvements in transaction speed and operational efficiency. This research contributes to the existing literature by providing a structured decision-making framework for blockchain implementation, offering practical insights for supply chain managers aiming to streamline warehouse operations and enhance decision-making processes.

本研究探讨了区块链技术在优化仓库管理方面的应用，重点是提高透明度、安全性和运营效率。通过利用基于区块链的仓库平台（BCWP），本研究提高了库存跟踪和供应链透明度。研究采用模糊德尔菲法来识别和评估关键的区块链实践，并使用最佳-最差法（BWM）评估其相对重要性。综合折中方案（CoCoSo）技术进一步对关键性能结果进行了排序。研究结果表明，质量检验和信息共享等做法在提高仓库绩效方面发挥着关键作用。此外，区块链技术的整合还显著提高了交易速度和运营效率。本研究为区块链的实施提供了结构化决策框架，为旨在简化仓库运营和增强决策流程的供应链管理者提供了实用见解，从而为现有文献做出了贡献。

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引用次数: 0

A multi-step mixed integer programming heuristic for warehouse layout optimization 仓库布局优化的多步骤混合整数编程启发式

Supply Chain Analytics

Pub Date : 2024-10-28 DOI: 10.1016/j.sca.2024.100088

Sanjaya Mayadunne , Hari K. Rajagopalan , Elizabeth Sharer

Warehouse layout optimization is critical to inventory and logistics management in organizations. In many instances, limited warehouse space is a constraint and a barrier to expanding operations and increasing demand. We present a multi-step solution using mixed integer programming to improve space utilization and increase order retrieval and fulfillment efficiency. We present a real-world case study to demonstrate the applicability and efficiency of the proposed mixed integer programming heuristics at a large distribution center.

仓库布局优化对企业的库存和物流管理至关重要。在许多情况下，有限的仓库空间是扩大业务和增加需求的限制和障碍。我们提出了一种使用混合整数编程的多步骤解决方案，以改善空间利用率，提高订单检索和执行效率。我们介绍了一个实际案例研究，以证明所提出的混合整数编程启发式方法在一个大型配送中心的适用性和效率。

引用次数: 0

Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques 评估机器学习方法对工程变更时间表的早期预测性能--利用预测性流程监控技术的案例研究

Supply Chain Analytics

Pub Date : 2024-10-24 DOI: 10.1016/j.sca.2024.100087

Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher, Tim Weißer

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.

通过应用机器学习算法，预测性业务流程监控（PBPM）技术为抵消流程的不良结果提供了机会。工程变更 (EC) 流程是业务流程中一个特别复杂的变种。在这里，不遵守计划实施日期会对装配线造成严重影响，因此尽早识别潜在的负面情况至关重要。然而，当前的 PBPM 研究很少研究机器学习方法的预测性能及其在早期流程步骤中的适用性，更不用说在 EC 流程中了。在我们的研究中，我们发现，如果有足够的特征编码，浅层学习器可以在流程初始化后准确预测计划的执行情况。基于一家汽车制造商的 EC 数据，我们提供了算法-编码组合的案例敏感性能概览。为此，我们将三种算法（XGBoost、随机森林、LSTM）与四种编码技术相结合。使用的编码技术包括两种常见的基于聚合的编码和基于索引的最后状态编码，以及这两种编码的两种新组合，我们称之为基于高级聚合的编码和基于复杂聚合的编码。研究表明，XGBoost-索引编码方法在平均预测性能方面更胜一筹，而随机森林-聚合编码方法由于减少了动态特征的影响，在时间稳定性方面表现更好。我们的研究提供了一种基于案例的推理方法，用于决定采用哪种算法-编码组合和评价指标。这样，我们就为欧盟委员会流程和其他类似复杂流程中的预警和监控方法提供了一个蓝图。

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引用次数: 0

A bibliometric exploration of environmental sustainability in supply chain research 供应链研究中的环境可持续性文献计量学探索

Supply Chain Analytics

Pub Date : 2024-10-22 DOI: 10.1016/j.sca.2024.100086

Brintha Rajendran , Manivannan Babu , Naliniprava Tripathy , Veeramani Anandhabalaji

This study undertakes a bibliometric examination of the literature on supply chain sustainability (SCS). The analysis includes exploring co-authorship, examining keyword co-occurrences, conducting citation analysis, bibliographic coupling, co-citation analysis for performance evaluation, and employing science mapping techniques. The paper thus explores the significant facets of the literature on SCS. We studied the literature on SCS management from 1996 to 2024 and extracted 6898 articles retrieved from the Scopus database. In the preliminary phase, the investigation employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow methodology alongside a designated search strategy. Secondly, it employs Biblioshiny, an RStudio package, and VOSviewer. The study finds that the SCS field has evolved with a major focus on collaboration, innovation, and sustainability. Furthermore, the findings indicate that China, the USA, the UK, and India lead in research contributions, emphasizing the importance of international collaboration. Additionally, the findings signpost that technology such as blockchain enhances sustainability efforts. Social sustainability also gains recognition alongside environmental concerns. These findings can inform researchers, highlighting the need for international cooperation, technology integration, and emphasis on social sustainability in advancing the management of supply chains. This study makes novel contributions by providing global coverage of publications, adopting an inclusive approach encompassing case studies and empirical research articles, addressing social desirability bias by reporting positive as well as negative aspects of sustainability in supply chain practices, and identifying alternative areas for future research within the discipline.

本研究对有关供应链可持续性（SCS）的文献进行了文献计量学研究。分析包括探讨共同作者、研究关键词的共同出现、进行引文分析、书目耦合、用于绩效评估的共同引文分析，以及采用科学图谱技术。因此，本文探讨了 SCS 文献的重要方面。我们研究了 1996 年至 2024 年有关 SCS 管理的文献，并从 Scopus 数据库中提取了 6898 篇文章。在初步阶段，调查采用了 PRISMA（系统综述和元分析的首选报告项目）流程方法和指定的检索策略。其次，它采用了 RStudio 软件包 Biblioshiny 和 VOSviewer。研究发现，SCS 领域的发展主要侧重于合作、创新和可持续性。此外，研究结果表明，中国、美国、英国和印度在研究贡献方面处于领先地位，强调了国际合作的重要性。此外，研究结果还表明，区块链等技术可以加强可持续发展工作。在关注环境问题的同时，社会可持续性也得到了认可。这些发现可以为研究人员提供参考，突出了国际合作、技术整合的必要性，以及在推进供应链管理时对社会可持续性的重视。本研究通过以下方式做出了新的贡献：提供全球范围内的出版物；采用包含案例研究和实证研究文章的包容性方法；通过报告供应链实践中可持续性的积极和消极方面来解决社会可取性偏差问题；以及确定本学科内未来研究的替代领域。

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引用次数: 0

A systematic review of supply chain analytics for targeted ads in E-commerce 电子商务定向广告供应链分析系统综述

Supply Chain Analytics

Pub Date : 2024-09-28 DOI: 10.1016/j.sca.2024.100085

Shrestha Pundir, Hardik Garg, Devnaad Singh, Prashant Singh Rana

Supply Chain Analytics (SCA) has emerged as a critical factor in determining the success of electronic commerce (E-commerce) companies. This review investigates the significant impact that SCA has had on the advertising landscape in the e-commerce industry. This article examines the complex correlation between electronic vendor (E-vendor) targeted advertising strategies and SCA by extensively reviewing critical scholarly works. By harnessing sophisticated analytics methodologies, organisations can acquire intricate understandings of consumer behaviour, cultivating heightened customer engagement and loyalty levels. Furthermore, the review highlights the significance of anticipating and resolving potential roadblocks that may arise during the deployment of SCA, such as financial consequences and external disruptions. Ultimately, the broad application of SCA facilitates customised advertisements.

供应链分析（SCA）已成为决定电子商务（E-commerce）公司成败的关键因素。本综述探讨了 SCA 对电子商务行业广告业的重大影响。本文通过广泛查阅重要学术著作，研究了电子供应商（E-vendor）定向广告策略与 SCA 之间的复杂关联。通过利用先进的分析方法，企业可以深入了解消费者行为，提高客户参与度和忠诚度。此外，评论还强调了预测和解决在部署 SCA 过程中可能出现的潜在障碍（如财务后果和外部干扰）的重要性。最终，《爱生雅》的广泛应用将促进定制广告的发展。

引用次数: 0

An integrated supply chain network design for advanced air mobility aircraft manufacturing using stochastic optimization 利用随机优化为先进的空中机动飞机制造设计综合供应链网络

Supply Chain Analytics

Pub Date : 2024-09-12 DOI: 10.1016/j.sca.2024.100083

Esrat Farhana Dulia , Syed A.M. Shihab

Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence mass eVTOL production with our model, enabling them to make optimal decisions and profits even under potential disruptions.

电动垂直起降（eVTOL）飞机制造商正等待着大量的 eVTOL 预购订单，并预计对这种先进的空中机动（AAM）飞机的需求将很快急剧上升。然而，由于缺乏 eVTOL 生产的供应链规划，eVTOL 制造商（EM）无法开始大规模生产商用 eVTOL。由于严格的质量标准和有限的 eVTOL 零部件供应商、熟练劳动力和机械设备短缺以及与主要航空供应商的合同重新谈判，eVTOL 供应链与传统供应链不同。新兴的 AAM 飞机市场带来了供应商定价和能力、eVTOL 制造成本和 eVTOL 需求方面的不确定性，进一步加剧了新兴市场在供应链规划方面的挑战。尽管存在这一迫切需求，但目前还没有针对新兴市场开发综合供应链规划模型的研究。针对这一研究空白，我们提出了一种随机优化模型，用于在上述不确定性条件下，在最大限度地提高新兴市场运营利润的同时，对新兴市场的综合供应链进行规划。我们通过各种数值案例分析了以下因素对新兴市场供应链规划的影响：1）受 eVTOL 质量影响的内生 eVTOL 需求；2）地缘政治冲突和资源稀缺导致的供应链中断；3）类似于汽车制造商所经历的大批量 eVTOL 需求。结果表明，我们提出的模型在所有情况下都具有适应性，并且优于已有的基准随机模型。研究结果表明，EM 可以利用我们的模型开始大规模 eVTOL 生产，即使在潜在的中断情况下也能做出最佳决策并获得最佳利润。

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引用次数: 0