Supply Chain Analytics最新文献_第2页

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

{"title":"A strategic and social analytics model for sustainable packaging in the cosmetic industry","authors":"Idiano D'Adamo , Massimo Gastaldi , Rossella Giacalone , Yigit Kazancoglu","doi":"10.1016/j.sca.2024.100090","DOIUrl":"10.1016/j.sca.2024.100090","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100090"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593027","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

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

{"title":"Amplifying swift-trust, collaboration, and teamwork in warehouse management through blockchain-enabled technology","authors":"Sadia Samar Ali , Syed Aqib Jalil , Murshid Kamal , Rudra Rameshwar","doi":"10.1016/j.sca.2024.100089","DOIUrl":"10.1016/j.sca.2024.100089","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100089"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571728","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 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

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

{"title":"Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques","authors":"Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher, Tim Weißer","doi":"10.1016/j.sca.2024.100087","DOIUrl":"10.1016/j.sca.2024.100087","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554016","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 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

{"title":"A bibliometric exploration of environmental sustainability in supply chain research","authors":"Brintha Rajendran , Manivannan Babu , Naliniprava Tripathy , Veeramani Anandhabalaji","doi":"10.1016/j.sca.2024.100086","DOIUrl":"10.1016/j.sca.2024.100086","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537375","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 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

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

{"title":"An integrated supply chain network design for advanced air mobility aircraft manufacturing using stochastic optimization","authors":"Esrat Farhana Dulia , Syed A.M. Shihab","doi":"10.1016/j.sca.2024.100083","DOIUrl":"10.1016/j.sca.2024.100083","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000268/pdfft?md5=b604a803c52861a058956e6fd8a64ecf&pid=1-s2.0-S2949863524000268-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230337","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 comparative assessment of holt winter exponential smoothing and autoregressive integrated moving average for inventory optimization in supply chains 霍尔特冬季指数平滑法与自回归综合移动平均法在供应链库存优化中的比较评估

Supply Chain Analytics Pub Date : 2024-09-05 DOI: 10.1016/j.sca.2024.100084

Lalji Kumar , Sudhakar Khedlekar , U.K. Khedlekar

{"title":"A comparative assessment of holt winter exponential smoothing and autoregressive integrated moving average for inventory optimization in supply chains","authors":"Lalji Kumar , Sudhakar Khedlekar , U.K. Khedlekar","doi":"10.1016/j.sca.2024.100084","DOIUrl":"10.1016/j.sca.2024.100084","url":null,"abstract":"<div><p>Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their performance in predicting demand amid unpredictable factors and develops robust forecasting algorithms using real-world data. It evaluates HWES and ARIMA in capturing demand fluctuations, considering seasonality, market trends, and cyclic patterns. A comprehensive comparative analysis is conducted under stable and unstable economic conditions. The study also focuses on a dynamic pricing model for limited sale seasons, examining lost sales patterns over time. In the context of supply chain and inventory management, efficient demand forecasting and dynamic pricing are essential for optimizing inventory levels and minimizing costs. Supply chains must adapt quickly to demand fluctuations to avoid overstocking or stockouts, which lead to revenue losses and inefficiencies. The findings reveal that ARIMA consistently outperforms HWES in minimizing lost sales, demonstrating its efficacy in demand forecasting, mitigating stockouts, and reducing revenue losses, particularly in varying economic conditions. This research significantly contributes to current knowledge by developing tailored forecasting algorithms and a dynamic pricing model, enhancing supply chain resilience and performance in uncertain business environments.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294986352400027X/pdfft?md5=98f10ccd1d31fdd03db055c77fb3faa2&pid=1-s2.0-S294986352400027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230338","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 explainable artificial intelligence model for predictive maintenance and spare parts optimization 用于预测性维护和备件优化的可解释人工智能模型

Supply Chain Analytics Pub Date : 2024-08-26 DOI: 10.1016/j.sca.2024.100078

Ufuk Dereci , Gülfem Tuzkaya

{"title":"An explainable artificial intelligence model for predictive maintenance and spare parts optimization","authors":"Ufuk Dereci , Gülfem Tuzkaya","doi":"10.1016/j.sca.2024.100078","DOIUrl":"10.1016/j.sca.2024.100078","url":null,"abstract":"<div><p>Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000219/pdfft?md5=de370f4dd5787db3d883f746b49da463&pid=1-s2.0-S2949863524000219-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130182","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 two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty 双重不确定性下向灾难幸存者发放救济的两阶段优化模型

Supply Chain Analytics Pub Date : 2024-08-23 DOI: 10.1016/j.sca.2024.100079

Palash Sahoo

{"title":"A two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty","authors":"Palash Sahoo","doi":"10.1016/j.sca.2024.100079","DOIUrl":"10.1016/j.sca.2024.100079","url":null,"abstract":"<div><p>Disasters are unforeseen occurrences requiring extensive transport deployment to support and relieve victims. Sometimes, this transportation is not feasible directly from some supply points to some destination points. Due to this tragedy, it is unclear precisely what is available at supply points, what is needed at destinations, how much transportation capacity there is, and what the routes are like. In this study, we investigate a two-stage multi-item fixed charge four-dimensional transportation problem using the concept of big data theory under the two-fold uncertainties. Here, the model’s parameters such as unit transportation costs, availabilities of items at the suppliers, fixed charges, capacities of conveyances, and demands of the items at the retailers are considered type-2 zigzag uncertain variables. Using big data theory and based on uncertain programming theory, two novel uncertain models are developed such as chance-constrained programming and expected value programming model. These two uncertain models transformed into the deterministic form via uncertainty inverse distribution theory. A critical value based reduction method with three categories (i.e., expected value, pessimistic value, and optimistic value) is applied to reduce the type-2 zigzag uncertain variable to the type-1 zigzag uncertain variable. The genetic algorithm and particle swarm optimization techniques have been proposed to find the optimal solution for the two deterministic models. The efficiency of our proposed approach is demonstrated with a real-life numerical example.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000220/pdfft?md5=1fce1edb90ad4c3e82f399b2092acdb8&pid=1-s2.0-S2949863524000220-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095580","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