[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123954-en":3,"doc-seo-123954-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123954,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","An Application of Machine Learning to Logistics Performance Prediction - Economics Attribute-Based Collective Instance","A machine learning framework predicts the logistics performance index using economic attributes, combining linear and non-linear algorithms. A macroeconomic panel dataset is integrated with a microeconomic panel dataset derived via data envelopment analysis for financial efficiency evaluation. Experiments cover six ASEAN member countries, where an artificial neural network best captures collective patterns, followed by ridge regression. Due to limited training data, ANN applies to Singapore, Malaysia, and the Philippines, while ridge regression supports Indonesia, Thailand, and Vietnam. Results enable precise short-term trend forecasting, highlight macro drivers in Vietnam, and inform policy reforms to strengthen regional logistics and supply chain capabilities.","An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute‑Based of Collective Instance  \nSuriyan Jomthanachai1 · Wai Peng Wong2 · KhaiWah Khaw3  \nAccepted: 15 January 2023 / Published online: 1 February 2023  \n© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023  \nAbstract  \nIn this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business’s financial efficiency. The results at the country level can be used to track, improve, and reform the country’s short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.  \nKeywords Machine learning · Artificial neural network · Linear regression · Prediction · Data envelopment analysis · Logistics performance index  \n* Wai Peng Wong [waipeng.wong@monash.edu](waipeng.wong@monash.edu)  \n1 Faculty of Management Sciences, Prince of Songkla University (PSU), Songkhla 90112, Thailand  \n2 School of Information Technology, Monash University, Malaysia Campus, Selangor, Malaysia  \n3 School of Management, Universiti Sains Malaysia, 11800 Penang, Malaysia  \nAbbreviations  \nAI  \nANNASEAN BBC CCRCOVID-19 CRS DEADDM DMUs FSP  \nGDP IDNIoT LASSO  \nLEARNGDM LP  \nLPILSP MAEMFGML MYS NSE OLSPHLPred. RMSESDGs SFASGP TANSIG THA  \nTRAINLMUN  \nVNM VRS  \nArtificial intelligence  \nArtificial neural network  \nThe Association of Southeast Asian Nations Banker, Charnes and Cooper  \nCharnes, Cooper and Rhodes The coronavirus disease 2019  \nConstant returns to scale Data envelopment analysis Data-driven modelling Decision-making units Functional service provider Gross domestic product Indonesia  \nInternet of things  \nLeast absolute shrinkage and selection operator  \nGradient descent with momentum weight and bias learning Linear programming  \nLogistics performance index  \nLogistics service provider Mean absolute error Manufacturing  \nMachine learning Malaysia  \nNash − Sutcliffe efficiency coefficient Ordinary least squares The Philippines  \nPrediction  \nRoot mean square error Sustainable development goals Stochastic frontier analysis Singapore  \nTangent sigmoid Thailand  \nLevenberg-Marquardt optimization training The United Nations  \nVietnam  \nVariable returns to scale  \n1 Introduction  \nThe Logistics Performance Index (LPI) of the World Bank is a well-known practical instrument for measuring a country’s logistics performance that is available to policymakers (World Bank, 2018) . The LPI, which measures national logistics performance on a biannual basis since 2007, is arguably the most important instrument  \nto emerge from the trade facilitation domain. It primarily focuses on (international) trade logistics and assesses national logistical connection across six p","cbCaiseQugezdDVY","https://ap.wps.com/l/cbCaiseQugezdDVY","pdf",1603462,1,52,"English","en",105,"# Abstract\n# Introduction\n## Motivation and significance of LPI\n## Mapping LPI into inputs and outcomes\n## Need for prediction using macro- and microeconomics","[{\"question\":\"What does the proposed machine learning framework predict?\",\"answer\":\"It predicts the logistics performance index based on economic attributes, using both macroeconomic and microeconomic information.\"},{\"question\":\"Which datasets are used in the study?\",\"answer\":\"The study uses a macroeconomic panel dataset and a microeconomic panel dataset obtained through data envelopment analysis for evaluating financial efficiency.\"},{\"question\":\"How do the results differ between artificial neural networks and ridge regression?\",\"answer\":\"The artificial neural network performs best overall for complex collective patterns, while ridge regression fits the datasets for Indonesia, Thailand, and Vietnam.\"}]","An Application of Machine Learning to Logistics Performance Prediction - Economics Attribute-Based Collective Instance | PDF",1785819419,131,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-application-of-machine-learning-to-logistics-performance-prediction-economics-attribute-based-collective-instance","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-application-of-machine-learning-to-logistics-performance-prediction-economics-attribute-based-collective-instance/123954/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the proposed machine learning framework predict?","Question",{"text":75,"@type":76},"It predicts the logistics performance index based on economic attributes, using both macroeconomic and microeconomic information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets are used in the study?",{"text":80,"@type":76},"The study uses a macroeconomic panel dataset and a microeconomic panel dataset obtained through data envelopment analysis for evaluating financial efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results differ between artificial neural networks and ridge regression?",{"text":84,"@type":76},"The artificial neural network performs best overall for complex collective patterns, while ridge regression fits the datasets for Indonesia, Thailand, and Vietnam.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]