Supply Chain Analytics最新文献_第9页

A mathematical optimization model for cluster-based single-depot location-routing e-commerce logistics problems 基于集群的单仓库选址路径电子商务物流问题的数学优化模型

Supply Chain Analytics Pub Date : 2023-09-01 DOI: 10.1016/j.sca.2023.100019

Alireza Amini, Michael Haughton

引用次数: 1

An integrated multi-criteria decision-making and multivariate analysis towards sustainable procurement with application in automotive industry 面向可持续采购的综合多准则决策和多变量分析及其在汽车工业中的应用

Supply Chain Analytics Pub Date : 2023-09-01 DOI: 10.1016/j.sca.2023.100033

Sudipta Ghosh , Chiranjib Bhowmik , Sudipta Sinha , Rakesh D. Raut , Madhab Chandra Mandal , Amitava Ray

{"title":"An integrated multi-criteria decision-making and multivariate analysis towards sustainable procurement with application in automotive industry","authors":"Sudipta Ghosh , Chiranjib Bhowmik , Sudipta Sinha , Rakesh D. Raut , Madhab Chandra Mandal , Amitava Ray","doi":"10.1016/j.sca.2023.100033","DOIUrl":"https://doi.org/10.1016/j.sca.2023.100033","url":null,"abstract":"<div><p>Green Supply Chain Management (GSCM) has emerged as a paramount issue in modern business organizations striving to become environmentally sustainable. Suppliers are pivotal in building a green supply chain. Green supplier selection (GSS) is a complex task involving several steps, from evaluation to final selection. This research aims to select spare parts suppliers of an automotive company based on their GSCM practices. Fourteen critical criteria are extracted from extant literature and refined through a Delphi study. The data was collected through interviews with industry experts using structured questionnaires. This study proposes integrated multi-criteria decision-making (MCDM) and multivariate analysis method with internal consistency checks. The Principal Component Analysis (PCA) is used to calculate criteria weights. A Simple Additive Weighting (SAW) method ranks the suppliers based on weighted criteria. The result shows that “collaboration with suppliers for green purchasing” is the most influential parameter for GSS. The outcome of this research may aid managers in selecting the most suitable green suppliers in the automotive industry by attaining sustainability. The proposed framework can be replicated to select suppliers in other industries.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"3 ","pages":"Article 100033"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49751200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning 基于集成深度学习的时间序列需求预测的订货级库存优化模型

Supply Chain Analytics Pub Date : 2023-09-01 DOI: 10.1016/j.sca.2023.100024

Mahya Seyedan , Fereshteh Mafakheri , Chun Wang

{"title":"Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning","authors":"Mahya Seyedan , Fereshteh Mafakheri , Chun Wang","doi":"10.1016/j.sca.2023.100024","DOIUrl":"https://doi.org/10.1016/j.sca.2023.100024","url":null,"abstract":"<div><p>Inventory control aims to meet customer demands at a given service level while minimizing cost. As a result of market volatility, customer demand is generally changing, and ignoring this uncertainty could lead to under or over-estimation of inventories resulting in shortages or inefficiencies. Inventory managers need batch ordering such that the ordered items arrive before the depletion of stocks due to the lead time between the ordering point and delivery. Therefore, to meet demand while optimizing the cost of the inventory system, firms must forecast future demands to address ordering uncertainties. Traditionally, it was challenging to predict such uncertainties with high accuracy. The availability of high volumes of historical data and big data analytics have made it easier to overcome such a challenge. This study aims to predict future demand in the case of an online retail industry using ensemble deep learning-based forecasting methods with a comparison of their performance. Compared to single-model learning, ensemble learning could improve the accuracy of predictions by combining the best performance of each model. Also, the advantages of deep learning and ensemble learning are combined in ensemble deep learning models, allowing the final model to be more generalizable. Finally, safety stocks are estimated using the forecasted demand distribution, optimizing the inventory system under a cycle service level objective.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"3 ","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49751237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Supply chain risk management: A content analysis-based review of existing and emerging topics 供应链风险管理:对现有和新出现的主题进行基于内容分析的审查

Supply Chain Analytics Pub Date : 2023-09-01 DOI: 10.1016/j.sca.2023.100031

Ali Emrouznejad , Sina Abbasi , Çiğdem Sıcakyüz

{"title":"Supply chain risk management: A content analysis-based review of existing and emerging topics","authors":"Ali Emrouznejad , Sina Abbasi , Çiğdem Sıcakyüz","doi":"10.1016/j.sca.2023.100031","DOIUrl":"https://doi.org/10.1016/j.sca.2023.100031","url":null,"abstract":"<div><p>This paper presents a systematic review of the literature on Supply Chain Risk (SCR) research, focusing on content-based analysis. The study comprehensively examines the general factors associated with key themes and trends in supply chain risk management, encompassing the identification and assessment of risks, risk mitigation strategies, and the influence of emerging technologies on Supply Chain Risk Management (SCRM). The review provides an overview of current and emerging topics in SCRM, while also introducing categorization frameworks to address research gaps and provide a roadmap for future studies, thereby generating valuable insights in this field. The review highlights the significance of effective SCRM in ensuring business continuity and resilience, emphasizing the need for organizations to adopt a proactive approach to risk management. The paper concludes by identifying areas for future research, including the development of novel risk management frameworks and the integration of emerging technologies into supply chain risk management practices. Additionally, a comprehensive evaluation of each classification is presented, highlighting overlooked aspects and unexplored domains, and offering recommendations for potential next steps in SCRM research.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"3 ","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49767355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A comprehensive systematic review of the literature on the impact of the COVID-19 pandemic on supply chains 对COVID-19大流行对供应链影响的文献进行了全面系统的综述

Supply Chain Analytics Pub Date : 2023-09-01 DOI: 10.1016/j.sca.2023.100025

Tariq Aljuneidi , Shahid Ahmad Bhat , Youssef Boulaksil

引用次数: 3

An optimal replenishment cycle and order quantity inventory model for deteriorating items with fluctuating demand 需求波动的变质物品最优补货周期和订货数量库存模型

Supply Chain Analytics Pub Date : 2023-09-01 DOI: 10.1016/j.sca.2023.100021

Hui-Ling Yang

{"title":"An optimal replenishment cycle and order quantity inventory model for deteriorating items with fluctuating demand","authors":"Hui-Ling Yang","doi":"10.1016/j.sca.2023.100021","DOIUrl":"https://doi.org/10.1016/j.sca.2023.100021","url":null,"abstract":"<div><p>Suppliers often prefer to offer their retailers a delay period in payment to attract more sales and promote revenue in a supply chain. The retailers usually ask their customers to pay a portion of purchasing cost when receiving the product (<em>i</em>.<em>e</em>., a downstream partial trade credit) to reduce the default risk. On the other hand, the suppliers provide discounts for bulk purchases, and the retailer has enough capital to purchase more goods than can be stored in its warehouse. The retailer must store the excess quantities in a rented warehouse if the storage capacity is limited. A two-warehouse inventory system is needed to model this problem. In reality, the demand rate fluctuates with time, and the relevant cost is usually affected by the present value of time. This study focuses on the limited storage capacity inventory model for deteriorating items with fluctuating demand, downstream partial trade credit transactions, and discounted cash-flow considerations. The aim is to find the optimal replenishment cycle and order quantity and keep the present value of the total relevant cost per unit of time as low as possible. We further present numerical examples to demonstrate the applicability and develop managerial insights.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"3 ","pages":"Article 100021"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49758861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains 供应链电动传动系统状态监测的统计机器学习方法比较研究

Supply Chain Analytics Pub Date : 2023-06-01 DOI: 10.1016/j.sca.2023.100011

Salim Lahmiri

{"title":"A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains","authors":"Salim Lahmiri","doi":"10.1016/j.sca.2023.100011","DOIUrl":"https://doi.org/10.1016/j.sca.2023.100011","url":null,"abstract":"<div><p>Fault detection and identification are critical for the accurate maintenance and management of industrial machinery. In this regard, data-driven condition monitoring models play an important role in machinery fault diagnosis and management. This study investigates the applicability of various statistical machine learning systems in modeling large data in the condition monitoring of electric drive trains in supply chains. Large data is used to train linear discriminant analysis, K-nearest neighbor algorithm, naïve Bayes, kernel naïve Bayes, decision trees, and support vector machine to distinguish between eleven fault states. The experimental results from the testing data set show that the decision trees achieved 93.8% accuracy, followed by kernel naïve Bayes (91.9%), radial basis function (Gaussian) support vector machine (89.3%), linear discriminant analysis (84.5%), k-NN algorithm (80.5%), and Gaussian naïve Bayes (71.3%). Accordingly, the choice of statistical machine learning algorithm influences classification accuracy related to electric drive fault diagnosis. In addition, decision trees take only few seconds to learn and classify new instances from big data. This makes the selection of decision trees trivial for condition monitoring and management of electric drive trains.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"2 ","pages":"Article 100011"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49748240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel grey multi-objective binary linear programming model for risk assessment in supply chain management 一种新的供应链管理风险评估灰色多目标二元线性规划模型

Supply Chain Analytics Pub Date : 2023-06-01 DOI: 10.1016/j.sca.2023.100012

Amin Vafadarnikjoo , Md. Abdul Moktadir , Sanjoy Kumar Paul , Syed Mithun Ali

{"title":"A novel grey multi-objective binary linear programming model for risk assessment in supply chain management","authors":"Amin Vafadarnikjoo , Md. Abdul Moktadir , Sanjoy Kumar Paul , Syed Mithun Ali","doi":"10.1016/j.sca.2023.100012","DOIUrl":"https://doi.org/10.1016/j.sca.2023.100012","url":null,"abstract":"<div><p>Robust and resilient agri-food supply chain management (AFSCM) is paramount to agribusinesses, given the many challenges and risks that this increased demand will bring in the coming decades. Interruptions caused by various risks to this crucial supply chain network, particularly in emerging economies, can put the lives of millions in danger, not to mention creating devastating impacts on the economy and the environment. Even so, there are only a limited number of quantitative risk management studies in the AFSCM literature. In this study, an integrated modified risk mitigation matrix (M-RMM) is developed to analyze the mitigation strategies for dealing with various risks in the context of the agri-food supply chain. The M-RMM is integrated with the grey multi-objective binary linear programming (GMOBLP) model to obtain the optimal risk mitigation strategies related to the three objective functions of risk, cost, and time minimization. The proposed model is a useful tool for formulating sustainable business policies and reducing food waste, and acquiring a context-specific (i.e., a developing economy), sector-specific (i.e., the agri-food processing sector), and multi-product (i.e., fresh and non-perishable) approach. The findings reveal that continuous training and development and vulnerability analysis of IT systems are the most effective risk mitigation strategies to lessen the impacts of lack of skilled personnel, sub-standard leadership, failure in IT systems, insufficient capacity to produce quality products, and poor customer relationships. The findings assist practitioners in managing risks in supply chains.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"2 ","pages":"Article 100012"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49748241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An analytical model for analyzing the value of information flow in the production chain model using regression algorithms and neural networks 利用回归算法和神经网络对生产链中信息流的价值进行分析

Supply Chain Analytics Pub Date : 2023-06-01 DOI: 10.1016/j.sca.2023.100013

Florent Biyeme , André Marie Mbakop , Anne Marie Chana , Joseph Voufo , Jean Raymond Lucien Meva'a

引用次数: 2

Information quality and supply chain performance: The mediating role of information sharing 信息质量与供应链绩效：信息共享的中介作用

Supply Chain Analytics Pub Date : 2023-06-01 DOI: 10.1016/j.sca.2023.100005

George Kankam , Evans Kyeremeh , Gladys Narki Kumi Som , Isaac Tetteh Charnor

{"title":"Information quality and supply chain performance: The mediating role of information sharing","authors":"George Kankam , Evans Kyeremeh , Gladys Narki Kumi Som , Isaac Tetteh Charnor","doi":"10.1016/j.sca.2023.100005","DOIUrl":"https://doi.org/10.1016/j.sca.2023.100005","url":null,"abstract":"<div><p>This paper brings to light the powerful connection between buyer and supplier relationships in terms of information sharing, information quality, and supply chain performance. We show supply chain partners coordinate their activities by offering high-quality information to enable interactions between buyers and providers. We show information sharing acts as a mediator between information quality and supply chain performance. A survey is distributed to suppliers of key industrial businesses active in the manufacturing sector to collect empirical data. Confirmatory factor analysis and structural equation modeling (CB-SEM) are used to analyze the data. The results show twenty manufacturing firms recognized the information-sharing function of mediation. We demonstrate that there is a partial mediating effect between information quality and supply chain performance satisfaction through information sharing. Accordingly, this study focuses on information sharing and information quality regarding supply chain performance. The main goal is to ensure that supply chain organizations communicate reliable information, which would boost overall performance due to imposing supply chain management principles that would enhance information quality and dependability.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"2 ","pages":"Article 100005"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49748282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3