[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122669-en":3,"doc-seo-122669-105":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},122669,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Big Data - Supply Chain Management Framework for Forecasting - Data Preprocessing and Machine Learning Techniques","A circular big-data and supply-chain (SC) framework is proposed to improve forecasting performance through a full pipeline: data preprocessing for machine-learning (ML) models, model training and optimization, and post-process evaluation using SC KPIs. The framework links KPIs to error-measurement systems, guiding hyperparameter tuning and performance parameters while clarifying how phantom inventory can distort forecasting. The study also outlines how data collection strategies, different forecasting periods/objectives, and operational planning metrics support transparent, efficient control of inventory, workforce, production, and capacity decisions.","Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000 .  \nDigital Object Identifier 10.1109/ACCESS.2017.DOI  \nBig Data – Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques  \n24 Jul 2023  \nMD ABRAR JAHIN1 , MD SAKIB HOSSAIN SHOVON2 , JUNGPIL SHIN3 ,(Senior Member, IEEE), ISTIYAQUE AHMED RIDOY4 , YOICHI TOMIOKA3 ,(Member, IEEE), AND M. F. MRIDHA2 ,(Senior Member, IEEE)  \n1Department of Industrial Engineering and Management, Khulna University of Engineering and Technology (KUET), Khulna 9203, Bangladesh  \n2Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh 3Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan 4Institute of Business Administration, University of Dhaka, Dhaka, Bangladesh  \nCorresponding author: Jungpil Shin (e-mail: [jpshin@u-aizu.ac.jp](jpshin@u-aizu.ac.jp)).  \narXiv :2307 . 12971v1  \ndata analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning) . The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research.  \n INDEX TERMS Data analysis; Decision making; Demand forecasting; Hyperparameter tuning; Literature review; Supply chain performance  \nI. INTRODUCTION  \nTHE supply chain (SC) has evolved sufficiently over the  \npast years to discover new methods and techniques for solving SCM problems. The SC can develop its configuration based on its control, coordination, and management [16] . The advent of Big Data (BD) brings one such change. Like other fields, BD can be utilized to improve decision-making reprocesses and alter business models through multiple resources, tools, and applications [83] . Therefore, SC and BD usages are connected to help one another. Although the concepts for SCM are already well-developed, it is possible to improve  \nfurther; recent researches on enhancing efficiency through collaboration [78], usage of RFID and intelligent goods [89] are two examples of such innovations that improved SCM processes. Newer technologies are further enabling the discovery of innovative strategies for solving SC problems. Big Data Analytics (BDA) is one such disruptive innovation. Although BD has been present for a long time, the approaches to making sense of BD are comparatively new, and such systems have not been wholly integrated into other branches of knowledge [143] . We identified the absence of data usage  \nand relevant processes in SC directly as a major problem that needs to be addressed.  \nBD has similarly grown popular over the years. After academic and technical publications first mentioned such technological developments, it has drawn the attention of various people, including literary scholars, corporate leaders, and government officials [17] . The most recognizable feature of BD is probably its size or the amount of data stored. Beyo","cbCaipY6ydY0gmcT","https://ap.wps.com/l/cbCaipY6ydY0gmcT","pdf",2813032,1,26,"English","en",105,"# Introduction\n## Research gaps\n## Research objectives","[{\"question\":\"What problem does the research identify in supply chain forecasting with big data?\",\"answer\":\"The study identifies the absence of data usage and relevant processes in supply chains as a major issue that needs to be addressed using big data analytics.\"},{\"question\":\"How does the proposed framework connect preprocessing, forecasting, and evaluation?\",\"answer\":\"It introduces a cyclic connection where preprocessing optimization is driven by post-process KPIs, linking model evaluation to upstream control and operational planning decisions.\"},{\"question\":\"Which factors are used to optimize the forecasting model in the supply chain setting?\",\"answer\":\"Supply chain KPIs and error-measurement systems guide model performance parameters, while hyperparameter tuning and ML algorithms optimization are used to improve forecasting outcomes.\"}]","Big Data - Supply Chain Management Framework for Forecasting - Data Preprocessing and Machine Learning Techniques | PDF",1785812080,66,{"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},"big-data-supply-chain-management-framework-for-forecasting-data-preprocessing-and-machine-learning-techniques","",{"@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/big-data-supply-chain-management-framework-for-forecasting-data-preprocessing-and-machine-learning-techniques/122669/",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 problem does the research identify in supply chain forecasting with big data?","Question",{"text":75,"@type":76},"The study identifies the absence of data usage and relevant processes in supply chains as a major issue that needs to be addressed using big data analytics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework connect preprocessing, forecasting, and evaluation?",{"text":80,"@type":76},"It introduces a cyclic connection where preprocessing optimization is driven by post-process KPIs, linking model evaluation to upstream control and operational planning decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors are used to optimize the forecasting model in the supply chain setting?",{"text":84,"@type":76},"Supply chain KPIs and error-measurement systems guide model performance parameters, while hyperparameter tuning and ML algorithms optimization are used to improve forecasting outcomes.","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"]