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

{"title":"A game theoretic model for dual supply chains with green and non-green products and bi-directional free-riding and carbon policy","authors":"Sanchari Ganguly , Pritha Das , Manoranjan Maiti","doi":"10.1016/j.sca.2025.100103","DOIUrl":"10.1016/j.sca.2025.100103","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

{"title":"A Bayesian best-worst approach with blockchain integration for optimizing supply chain efficiency through supplier selection","authors":"Azam Modares , Vahideh Bafandegan Emroozi , Pardis Roozkhosh , Azade Modares","doi":"10.1016/j.sca.2024.100100","DOIUrl":"10.1016/j.sca.2024.100100","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A data-driven machine learning model for forecasting delivery positions in logistics for workforce planning","authors":"Patrick Eichenseer , Lukas Hans , Herwig Winkler","doi":"10.1016/j.sca.2024.100099","DOIUrl":"10.1016/j.sca.2024.100099","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100099"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A resilient poultry vaccine supply chain network design considering perishability and stress test","authors":"Mina Mehravaran, Arash Nemati","doi":"10.1016/j.sca.2024.100098","DOIUrl":"10.1016/j.sca.2024.100098","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An Integrated Multi-Product Biodiesel and Bioethanol Supply Chain Model with Torrefaction Under Uncertainty","authors":"Farima Salamian , Masoud Rabbani , Amirmohammad Paksaz","doi":"10.1016/j.sca.2024.100092","DOIUrl":"10.1016/j.sca.2024.100092","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100092"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A novel sequential block path planning method for 3D unmanned aerial vehicle routing in sustainable supply chains","authors":"Muhammad Ikram , Robert Sroufe","doi":"10.1016/j.sca.2024.100094","DOIUrl":"10.1016/j.sca.2024.100094","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"An agility and performance assessment framework for supply chains using confirmatory factor analysis and structural equation modelling","authors":"Akhil NSB , Rohit Raj , Vimal Kumar , Phanitha Kalyani Gangaraju , Tanmoy De","doi":"10.1016/j.sca.2024.100093","DOIUrl":"10.1016/j.sca.2024.100093","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"A conceptual digital twin framework for supply chain recovery and resilience","authors":"Oluwagbenga Victor Ogunsoto , Jessica Olivares-Aguila , Waguih ElMaraghy","doi":"10.1016/j.sca.2024.100091","DOIUrl":"10.1016/j.sca.2024.100091","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"9 ","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0