[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118953-en":3,"doc-seo-118953-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118953,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Supply Chain - Optimize the Production Cost Using Machine Learning","Study explores how machine learning algorithms can be applied across supply chain operations to produce at the cheapest site worldwide while accounting for multiple parameters. It emphasizes the iterative impact of digital transformation and highlights machine learning and dark data analytics as key drivers for lowering production costs and improving delivery performance. A case company with 33 production sites and operations across 100+ countries addresses cost inefficiencies caused by non-centralized production demands.","SUPPLY CHAIN: OPTIMIZE THE PRODUCTION COST USING MACHINE LEARNING  \nLoubna MoumeniA, Mohammed SaberB  \n\n| ARTICLE INFO\u003Cbr>Article history:\u003Cbr>Received 18 August 2023 Accepted 22 November 2023\u003Cbr>Keywords:\u003Cbr>Machine Learning; Dark Data; Supply Chain; Production Cost; Unstructured Data.\u003Cbr> | \u003Cbr>ABSTRACT\u003Cbr>Purpose: The objective of this study is to know how we use can machine learning by applying different algorithms on the supply chain to produce in the cheapest site in the world considering different parameters.\u003Cbr>Theoretical framework: the study has highlighted the iterative queries of digital revolution in the supply chain. The literary view in this article has illustrated the significant role of machine learning and dark data in reducing the production costs, enhancing delivery performance.\u003Cbr>Design/Methodology/Approach: The company concerned by the case study is a multinational company specializing in flooring and sports surfaces. It operates in 33 production sites, with 520 sites in more than 100 countries.\u003Cbr>One of the important factors underlying this complexity is the customer base that expects the product at the same cost all over the world, which forces the system that is currently not centralized to produce at a high cost in some countries.\u003Cbr>Findings: In this article, we use machine learning by applying different algorithms to unstructured data stored in company servers, where the feedback loop is implemented. The expected result is produced in the cheapest site in the world considering delivery costs.\u003Cbr>Research, Practical & Social implications: We suggest a future research to use all the remaining dark data saved during ordering on the supply chain and to reduce more the costs in the world .\u003Cbr>Originality/Value: This article provides insights into how dark data analytics can be used to reduce supply chain costs and offers recommendations for organizations looking to leverage dark data in their supply chain operations.\u003Cbr>\u003Cbr>Doi: [https://doi.org/10.26668/businessreview/2023.v8i11.3756](https://doi.org/10.26668/businessreview/2023.v8i11.3756) |\n| --- | --- |\n\nCADEIA DE FORNECIMENTO: OTIMIZE O CUSTO DE PRODUÇÃO USANDO APRENDIZAGEM  \nDE MÁQUINA  \nRESUMO  \nObjetivo: O objetivo deste estudo é saber como usamos o aprendizado de máquina, aplicando diferentes algoritmos na cadeia de suprimentos para produzir no local mais barato do mundo, considerando diferentes parâmetros.  \nEnquadramento teórico: o estudo destacou as questões iterativas da revolução digital na cadeia de abastecimento. A visão literária neste artigo ilustrou o papel significativo do aprendizado de máquina e dos dados obscuros naredução dos custos de produção, melhorando o desempenho da entrega.  \nDesign/Metodologia/Abordagem: A empresa objeto do estudo de caso é uma empresa multinacional especializada em pisos e superfícies esportivas. Opera em 33 locais de produção, com 520 locais em mais de 100 países. Um dos factores importantes subjacentes a esta complexidade é a base de clientes que espera o produto ao  \nA PhD in Computer Science at Mohammed First University, State computer engineer at SQLI Oujda, Morocco. E-mail: [l.moumeni@ump.ac.ma](l.moumeni@ump.ac.ma Orcid:)[ Orcid:](l.moumeni@ump.ac.ma Orcid:) [https://orcid.org/0000-0003-4520-718](https://orcid.org/0000-0003-4520-718X)[X](https://orcid.org/0000-0003-4520-718X)  \nBAssociate professor in the Department of Electronics, Computer Science and Telecommunications at National School of Applied Sciences at Mohammed First University, National School of Applied Sciences at Mohammed First University, Oujda, [Morocco E-mail: ](Morocco E-mail: m.saber@ump.ac.ma Orcid:)[m.saber@ump.ac.ma](Morocco E-mail: m.saber@ump.ac.ma Orcid:)[ Orcid:](Morocco E-mail: m.saber@ump.ac.ma Orcid:) [https://orcid.org/0000-0001-9384-5595](https://orcid.org/0000-0001-9384-5595)  \nmesmo custo em todo o mundo, o que obriga o sistema que actualmente não está centralizado a produzir a um custo elevado em alguns países.  \n","cbCaifUtp7icCiTL","https://ap.wps.com/l/cbCaifUtp7icCiTL","pdf",909440,1,15,"English","en",105,"# Abstract\n## Purpose\n## Theoretical framework\n## Design/Methodology/Approach\n## Findings\n## Research, Practical & Social implications\n## Originality/Value","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To apply machine learning with different algorithms to choose the production site that minimizes worldwide production cost under varying parameters.\"},{\"question\":\"How does the proposed approach reduce production costs?\",\"answer\":\"By using machine learning on unstructured data stored in company servers, with a feedback loop to improve decisions and target production at the lowest-cost site considering delivery costs.\"},{\"question\":\"What kind of company and operational scale is used in the case study?\",\"answer\":\"A multinational flooring and sports surface company operating 33 production sites and serving 520 sites across more than 100 countries.\"}]","Supply Chain - 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