[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117152-en":3,"doc-seo-117152-105":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},117152,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Supply Chain Management of Manufacturing Processes Using Machine Learning Technique","The expansion of the manufacturing processes network demands algorithms that improve planning and optimization. Recent advances in machine learning and artificial intelligence have accelerated Industry 4.0 adoption across manufacturing, supply chains, services, and products. Machine learning supports the creation of smart supply chains and intelligent manufacturing processes by addressing complex interactions. This research evaluates the suitability and efficiency of a machine learning approach for enhanced supply chains and shows improved manufacturing efficiency through clustering machine-tools and increasing component production at a single tool location.","SUPPLY CHAIN MANAGEMENT OF MANUFACTURING PROCESSES USING MACHINE LEARNING TECHNIQUE  \nMarcel ILIE 1, Augustin SEMENESCU2  \nDOI  10.56082/annalsarscieng.2024.1.38  \nRezumat. Extinderea rețelei de procese de producție necesită algoritmi care să permită o mai bună planificare și optimizare a proceselor de producție. Prin urmare, în ultimii ani, evoluțiiledin cadrul învățării automate (ML) și ale inteligenței artificiale (AI) au condus la o nouă terminologie, așa-numita Industrie 4.0. Cea mai rapidă creștere aIndustriei 4.0 afost întâlnită înproducție, lanț de aprovizionare, servicii și produse. Învățarea automată este predispusă să permită dezvoltarea lanțurilor de aprovizionare și a proceselor de producție inteligente. Prezentacercetare se referă la adecvarea și eficiența algoritmului de învățare automată pentru lanțul deaprovizionare îmbunătățit înprocesele de producție. Rezultatele arată că algoritmul de învățare automată permite și îmbunătățește eficiența proceselor de fabricație prin gruparea mașinilorunelte și creșterea numărului de componente fabricate în aceeași locație a sculei.  \nAbstract. The expansion of the manufacturing processes network requires algorithms that can enable better planning and optimization of the manufacturing processes. Therefore, in the recent years the developments within the machine-learning (ML) and artificial intelligence (AI) have led to a new terminology, the so-called Industry 4.0. The fastest growth of Industry 4.0 has been encountered in the manufacturing, supply chain, services and products. The machine learning is prone to enable the development of smart supply-chains and manufacturing processes. The present research concerns the suitability and efficiency of the machine learning algorithm for the enhanced supply chain in manufacturing processes. The results show that the machine learning algorithm enables and enhances the efficiency of the manufacturing processes by clustering the machine-tools and increasing the number of manufactured components at the same tool location.  \nKeywords: supply chain management, manufacturing process, machine learning, probability, neural networks, Bayesian statistics  \n1. Introduction  \nOver the past decades the optimization of the manufacturing systems have focused on the small-scale systems which consisted of simple structures such as serial manufacturing line, single machine, or parallel machine performing the same manufacturing operations. The expansion of the manufacturing processes  \n1PhD, Assoc. Professor: Dept. of Mechanical Engineering, Georgia Southern University, Statesboro, GA 30458, USA, [e-mail: ](e-mail: milie@georgiasouthern.edu)[milie@georgiasouthern.edu](e-mail: milie@georgiasouthern.edu)  \n2PhD, Professor, Faculty of Material Science & Engineering, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania, [augustin.semenescu@upb.ro](augustin.semenescu@upb.ro) ; Corresponding Member of Academy of Romanian Scientists, 3 Ilfov St., 050044 , Bucharest, Romania, [augustin.semenescu@upb.ro](augustin.semenescu@upb.ro)  \nnetwork requires algorithms that can enable better planning and optimization of the manufacturing processes. The large networks of manufacturing processes are usually prone to extensive and expensive preventive maintenance and quality control inspection times. These necessary activities may cause disruptions of the supply chain and thus, they can affect the overall manufacturing process. Generally, the specialized mass production can increase the efficiency of the manufacturing processes but unfortunately it increases the complexity of manufacturing network. Therefore, dynamic reliability and quality models are needed to mitigate the complex interaction within the network of the manufacturing processes. A flexible manufacturing process allows diverse flow routes of the manufacturing process which can result in a highly complex and challenging to manage manufacturing network. Therefore, one of the ","cbCain3jn25MhMTG","https://ap.wps.com/l/cbCain3jn25MhMTG","pdf",1368005,1,9,"English","en",105,"# Introduction\n## Problem background and network complexity\n## Reliability, quality, and failure modes\n## Optimum flow path and dynamic modeling","[{\"question\":\"What problem does the research focus on in manufacturing supply chains?\",\"answer\":\"It focuses on how to enable better planning and optimization of manufacturing process networks, which become complex as they expand.\"},{\"question\":\"How does machine learning contribute according to the paper’s results?\",\"answer\":\"The machine learning algorithm improves efficiency by clustering machine-tools and increasing the number of manufactured components at the same tool location.\"},{\"question\":\"Why are dynamic reliability and quality models needed?\",\"answer\":\"They help mitigate complex interactions in manufacturing process networks, especially when product quality and machine reliability influence downstream processes.\"}]","Supply Chain Management of Manufacturing Processes Using Machine Learning Technique | 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